Indian Journal of Dermatology
  Publication of IADVL, WB
  Official organ of AADV
Indexed with Science Citation Index (E) , Web of Science and PubMed
Users online: 864  
Home About  Editorial Board  Current Issue Archives Online Early Coming Soon Guidelines Subscriptions  e-Alerts    Login  
    Small font sizeDefault font sizeIncrease font size Print this page Email this page

Table of Contents 
Year : 2016  |  Volume : 61  |  Issue : 3  |  Page : 251-260
Biostatistics series module 3: Comparing groups: Numerical variables

1 Department of Pharmacology, Institute of Postgraduate Medical Education and Research, Kolkata, West Bengal, India
2 Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Parel, Mumbai, Maharashtra, India

Date of Web Publication13-May-2016

Correspondence Address:
Dr. Avijit Hazra
Department of Pharmacology, Institute of Postgraduate Medical Education and Research, 244B Acharya J. C. Bose Road, Kolkata - 700 020, West Bengal
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0019-5154.182416

Rights and Permissions


Numerical data that are normally distributed can be analyzed with parametric tests, that is, tests which are based on the parameters that define a normal distribution curve. If the distribution is uncertain, the data can be plotted as a normal probability plot and visually inspected, or tested for normality using one of a number of goodness of fit tests, such as the Kolmogorov–Smirnov test. The widely used Student's t-test has three variants. The one-sample t-test is used to assess if a sample mean (as an estimate of the population mean) differs significantly from a given population mean. The means of two independent samples may be compared for a statistically significant difference by the unpaired or independent samples t-test. If the data sets are related in some way, their means may be compared by the paired or dependent samples t-test. The t-test should not be used to compare the means of more than two groups. Although it is possible to compare groups in pairs, when there are more than two groups, this will increase the probability of a Type I error. The one-way analysis of variance (ANOVA) is employed to compare the means of three or more independent data sets that are normally distributed. Multiple measurements from the same set of subjects cannot be treated as separate, unrelated data sets. Comparison of means in such a situation requires repeated measures ANOVA. It is to be noted that while a multiple group comparison test such as ANOVA can point to a significant difference, it does not identify exactly between which two groups the difference lies. To do this, multiple group comparison needs to be followed up by an appropriate post hoc test. An example is the Tukey's honestly significant difference test following ANOVA. If the assumptions for parametric tests are not met, there are nonparametric alternatives for comparing data sets. These include Mann–Whitney U-test as the nonparametric counterpart of the unpaired Student's t-test, Wilcoxon signed-rank test as the counterpart of the paired Student's t-test, Kruskal–Wallis test as the nonparametric equivalent of ANOVA and the Friedman's test as the counterpart of repeated measures ANOVA.

Keywords: Analysis of variance, Friedman's test, Kolmogorov–Smirnov test, Kruskal–Wallis test, Mann–Whitney U-test, normal probability plot, t-test, Tukey's test, Wilcoxon's test

How to cite this article:
Hazra A, Gogtay N. Biostatistics series module 3: Comparing groups: Numerical variables. Indian J Dermatol 2016;61:251-60

How to cite this URL:
Hazra A, Gogtay N. Biostatistics series module 3: Comparing groups: Numerical variables. Indian J Dermatol [serial online] 2016 [cited 2022 Nov 27];61:251-60. Available from:

   Introduction Top

We have discussed earlier that numerical data can be recorded on an interval scale or a ratio scale – the latter scale is distinguished by a true zero and enables differences to be judged in the form of ratios. This distinction, however, usually does not influence the choice of statistical test for comparing such data. However, the distribution of the data does influence this choice.

Numerical data that are normally distributed can be analyzed with parametric tests, that is tests which are based on the parameters that define a normal distribution curve. Parametric tests assume that:

  • Data are numerical
  • The distribution in the underlying population is normal
  • Observations within a group are independent of one another
  • The samples have been drawn randomly from the population
  • The samples have the same variance (“homogeneity of variances”).

If it is uncertain whether the data are normally distributed, they can be plotted as a normal probability plot and visually inspected. In making such a plot, the data are first sorted and the sorted data are plotted along one axis against theoretical values plotted along the other axis. These latter values are selected to make the resulting plot appear like straight line if the data are approximately normally distributed. Deviations from a straight line suggest departures from normality. The normal probability plot is a special case of the quantile-quantile (Q–Q) probability plot used to test for a normal distribution.

[Figure 1] depicts two instances of the normal probability plot. The dots are closely approximating the straight line in the left panel suggesting that the data are approximately normally distributed. The dots are sagging below the expected straight line in the right panel. This suggests that the data are positively skewed. An S-shaped pattern about the straight line would suggest multimodal data.
Figure 1: Normal probability plots for normally distributed (left panel) and skewed (right panel) data

Click here to view

If we do not wish to assess normality by eyeballing, we can opt for one of the “goodness of fit” tests that test the goodness of the fit of the sample distribution to an expected normal distribution. The Kolmogorov–Smirnov test (after Andrei Nikolaevich Kolmogorov, 1933 and Nikolai Vasilevich Smirnov, 1939) for normality is frequently used. This compares the sample data with a standard normal distribution with the same mean and variance and derives a P value; if P > 0.05 then the null hypothesis cannot be rejected (i.e., the sample data are not different from the normal distribution) and the data are considered to be normally distributed. Another widely used test to determine normality based on the null hypothesis principle is the Shapiro–Wilk test (after Samuel S. Shapiro and Martin Bradbury Wilk, 1965). The Lilliefors test (after Hubert Lilliefors, 1967) is another normality test derived from the Kolmogorov–Smirnov test. There are still other tests to determine whether the sample has been derived from a normally distributed population, but there is no satisfactory answer to the question which is the best test in a given situation. In general, the Kolmogorov–Smirnov test is the oldest of this family of tests, is widely used and tolerates more deviation from strict normality.

Nonnormal, or skewed, data can be transformed so that they approximate a normal distribution. The commonest method is a log transformation, whereby the natural logarithms of the raw data are analyzed. If the transformed data are shown to approximate a normal distribution, they can then be analyzed with parametric tests. Large samples (say n > 100) approximate a normal distribution and can nearly always be analyzed with parametric tests. This assumption often holds even when the sample is not so large but say is over 30. However, with the increasing implementation of nonparametric tests in statistical software, the need for normality assumptions and data transformations seldom arise now-a-days.

The requirement for observations within a group to be independent means that multiple measurements from the same set of subjects cannot be treated as separate unrelated sets of observations. Such a situation requires specific repeated measures analyses. The requirement for samples to be drawn randomly from a population is not always met, but the results of hypothesis tests have proved to be reliable even if this assumption is not fully met.

Before we take up individual tests, let us recapitulate through [Figure 2], the tests that are available to compare groups or sets of numerical data for significant difference.
Figure 2: Statistical tests to compare numerical data for difference

Click here to view

   Student's T -Test Top

The Student's t -test is used to test the null hypothesis that there is no difference between two means. There are three variants:

  • One-sample t -test: To test if a sample mean (as an estimate of a population mean) differs significantly from the population mean. In other words, it is used to determine whether a sample comes from a population with a specific mean. The population mean is not always known, but may be hypothesized
  • Unpaired or independent samples t -test: To test if the means estimated for two independent samples differ significantly
  • Paired or related samples t -test: To test if the means of two dependent samples, or in other words two related data sets, differ significantly.

The test is named after the pseudonym Student of William Sealy Gossett who published his work in 1908 while he was an employee of the Guinness Breweries company in Dublin and company policy prevented him from using his real name. His t -test is used when the underlying assumptions of parametric tests are satisfied. However, it is robust enough to tolerate some deviation from these assumptions which can occur when small samples (say n <30) are being studied. Theoretically, the t -test can be used even when the samples are very small (n <10), so long as the variables are normally distributed within each group and the variances in the two groups are not too different.

The t -test is based on the t distribution calculated by Student. A sample from a population with a normal distribution is also normally distributed if the sample size is large. With smaller sample sizes, the likelihood of extreme values is greater, so the distribution “curve” is flatter and broader [Figure 3]. The t distribution, like the normal distribution, is also bell shaped, but has wider dispersion-this accommodates for the unreliability of the sample standard deviation as an estimate of the population standard deviation. There is a t distribution curve for any particular sample size and this is identified by denoting the t distribution at a given degree of freedom. Degree of freedom is equal to one less than the sample size and denotes the number of independent observations available. As the degree of freedom increases, the t distribution approaches the normal distribution. The table for the t distribution would show that as the degree of freedom increases, the value of t approaches 1.96 at a P value of 0.05. This is analogous to a normal distribution where 5% of values lie outside 1.96 standard deviations from the mean.
Figure 3: A t distribution (for n = 10) compared with a normal distribution. A t distribution is broader and flatter, such that 95% of observations lie within the range mean ± t × standard deviation (t = 2.23 for n = 10) compared with mean ± 1.96 standard deviation for the normal distribution

Click here to view

The formulae for the t -test (which are relatively simple) give a value of t . This is then referred to a t distribution table to obtain a P value. However, statistical software will directly return a P value from the calculated t value. To recapitulate, the P value quantifies the probability of obtaining a difference or change similar to the one observed, or one even more extreme, assuming the null hypothesis to be true. The null hypothesis of no difference can be rejected if the P value is less than the chosen value of the probability of Type I error (α), and it can be concluded that the means of the data sets are significantly different.

The unpaired t -test is used when two data sets that are “independent” of one another (that is values in one set are unlikely to be influenced by those in the other) are compared. However, if data sets are deliberately matched or paired in some way, then the paired t -test should be used. A common scenario that yields such paired data sets is when measurements are made on the same subjects before and after a treatment. Another is when subjects are matched in one or more characteristics during allocation to two different groups. A crossover study design also yields paired data sets for comparison. In the analysis of paired data, instead of considering the separate means of the two groups, the t -test looks at the differences between the two sets of measurements in each subject or each pair of subjects. By analyzing only the differences, the effect of variations that result from unequal baseline levels in individual subjects is reduced. Thus, a smaller sample size can be used in a paired design to achieve the same power as in an unpaired design.

If there is definite reason to look for a difference between means in only one direction (i.e., larger or smaller), then a one-tailed t -test can be used, rather than the more commonly used two-tailed t -test. This essentially doubles the chance of finding a significant difference. However, it is unfair to use a one-tailed t -test just because a two-tailed test failed to show P < 0.05. A one-tailed t -test should only be used if there is a valid reason for investigating a difference in only one direction. Ideally, this should be based on known effects of the treatment and be specified a priori in the study protocol.

Before we move on to comparison of more than two means, it is useful to take another view of the t -test procedure. Comparison of means of two samples essentially means comparing their central locations. When comparing central location between samples, we actually compare the difference (or variability) between samples with the variability within samples. Evidently, if the variability between sample means is very large and the variability within a sample is very low, a difference between the means is readily detected. Conversely if the difference between means is small but the variability within the samples is large, it will become more difficult to detect a difference. In the formula for the t -test, the difference between means is in the numerator. If this is small relative to the variance within the samples, which comes in the denominator, the resultant t value will be small and we are less likely to reject the null hypothesis. This effect is demonstrated in [Figure 4].
Figure 4: When comparing two groups, the ability to detect a difference between group means is affected by not only the absolute difference but also the group variance (a) two sampling distributions with no overlap and easily detected difference; (b) means now closer together causing overlap of curves and possibility of not detecting a difference; (c) means separated by same distance as in second case but the smaller variance means that there is no overlap, and the difference is easier to detect

Click here to view

[Box 1] provides some examples of t -test applications from published literature.

   Comparing Means of More Than Two Groups Top

The t -test should not be used to compare the means of three or more groups. Although it is possible to compare groups in pairs, when there are more than two groups, this will increase the probability of making a Type I error. For instance in an eight group study, there would be 28 possible pairs and with α of 0.05, or 1/20, there is every possibility that the observed difference in one of the pairs would occur by chance.

To circumvent the problem of inflated Type I error risk during multiple pair-wise testing, it has been proposed to divide the critical P value for each test by the total number of pair-wise comparisons, so that overall, the Type I error is limited to the original α. For example, if there are three t -tests to be done, then the critical value of 0.05 would be reduced to 0.0167 for each test and only if the P value is less than this adjusted α, would we reject the null hypothesis. This maintains a probability of 0.05 of making a Type I error overall. This principle is known as the Bonferroni correction (after Carlo Emilio Bonferroni). However, it is apparent that as the number of comparisons increases, the adjusted critical value of P becomes increasingly smaller, so that the chance of finding a significant difference becomes miniscule. Therefore, the Bonferroni correction is not universally accepted, and in any case, is considered to be untenable when more than 5 comparisons could be made, as it renders the critical value ofP < 0.01.

The comparison of means of multiple groups is best carried out using a family of techniques broadly known as one-way analysis of variance (ANOVA). When conducting multiple comparisons, ANOVA is generally also more powerful, that is more efficient at detecting a true difference.

In biomedical literature, we sometimes come across a Z -test applied to situations where the t -test could have been used. The term actually refers to any statistical test for which the distribution of the test statistic under the null hypothesis can be approximated by a normal distribution. The test involves in the calculation of Z -score. For each significance level, the Z -test has a single critical value (e.g., 1.96 for two-tailed 5%) unlike the Student's t -test which has separate critical values for each sample size. Many statistical tests can be conveniently performed as approximate Z -tests if the sample size is large and the population variance is known. If the population variance is unknown (and therefore has to be estimated from the sample itself) and the sample size is not large (n < 60; 30), the Student's t -test may be more appropriate.

A one-sample location test, two-sample location test, and paired difference test are examples of tests that can be conducted as Z -tests. The simplest Z -test is the one-sample location test that compares the mean of a set of measurements to a given population mean when the data are expected to have a common and known variance. The Z -test for single proportion is used to test a hypothesis on a specific value of the population proportion. The Z -test for difference of proportions is used to test the hypothesis that two populations have the same proportion. There are other applications such as determining whether predictor variables in logistic regression and probit analysis have a significant effect on the response and undertaking normal approximation for tests of poisson rates. These normal approximations are valid when the sample sizes and the number of events are large. A useful set of Z -test calculators can be found online at <>

   Analysis of Variance Top

ANOVA is employed to test for significant differences between the means of more than two groups. Thus, it may be regarded as a multiple group extension of the t -test. If applied to two groups, ANOVA will return a result similar to the t -test. ANOVA uses the same assumptions that apply to parametric tests in general.

Although it may seem strange that a test that compares means is called ANOVA, in ANOVA, we are actually comparing the ratio of two variances. We have already considered, in relation to the t -test, that a significant result is more likely when the difference between means is greater than the variance within the samples. With ANOVA, we also compare the difference between the means (using variance as our measure of dispersion) with the variance within the samples that results from random variation between the subjects within each group. The test asks if the difference between groups (between-group variability) can be explained by the degree of spread within a group (within-group variability). To answer this, it divides up the total variability into between group variance and within group variance and takes a ratio of the two. If the null hypothesis was true, the two variances will be similar, but if the observed variance between groups is greater than that within groups, then a significant difference of means is likely. The within-group variability is also known as the error variance or residual variance because it is variation we cannot readily account for in the study design since it stems from random differences in our samples. However, in a clinical trial situation, we hope that the between-group, or effect variance, is the result of our treatment.

From these two estimates of variance we compute the F statistic that underlies ANOVA. In a manner analogous to the t -test, the F statistic calculated from the samples is compared with known values of the F distribution, to obtain a P value. If this value is less than the preselected critical value of the probability of Type I error (α), then the null hypothesis of no difference between means can be rejected. However, it is important to note that if k represents the number of groups and n the total number of results for all groups, the variation between groups has degrees of freedom k − 1, and the variation within groups has degrees of freedom nk . When looking at the F distribution table, the two degrees of freedom must be used to locate the correct entry. These complex manipulations are programmed into statistical software that provides ANOVA routines, and a P value is automatically returned.

If ANOVA returns P < 0.05, this only tells us that there is a significant difference but not exactly where (i.e., between which two groups) the difference lies. Thus, if we are comparing more than two samples, a significant result will not identify which sample mean is different from any other. If we are interested in knowing this, we must make use of further tests to identify where the differences lie. Such tests that follow a multiple group comparison test are called post hoc tests.

One confusing aspect of ANOVA is that there are many post hoc tests without consensus among statisticians as to which test should be used most commonly. The tests differ in the amount and kind of adjustment for alpha error provided. Statistical software packages offer choice of multiple post hoc tests. Tukey's (honestly significant difference) test (after John Wilder Tukey) is frequently used. Dunnett's test (after Charles William Dunnett) is used specifically when one wishes to compare all other means to the mean of one data set (the “control” group). [Table 1] provides a summary of the post hoc tests used following ANOVA.
Table 1: Post hoc tests that may be applied following analysis of variance

Click here to view

The advancement in statistical software permits use of ANOVA beyond its traditional role of comparing means of multiple groups. If we simply compare the means of three or more groups, the ANOVA is often referred to as a one-way ANOVA or one-factor ANOVA. There is also two-way ANOVA when two grouping factors are analyzed and multivariate ANOVA when multiple dependent variables are analyzed. An example of one-way ANOVA would be to compare the changes in blood pressure between groups after the administration of three different drugs. If we wish to look at one additional factor that influences the result, we can perform the two-way ANOVA. Thus, if we are interested in gender-specific effect on blood pressure, we can opt for a two-factor (drug treatment and gender) ANOVA. In such a case the ANOVA will return a P value for the difference based on drug treatment and another P value for the difference based on gender. There will also be P value for the interaction of drug treatment and gender, indicating perhaps that one drug treatment may be more likely to be effective in patients of a certain gender. As an extended statistical technique, ANOVA can test each factor while controlling for all other factors and also enable us to detect interaction effects between factors. Thus, more complex hypotheses can be tested, and this is another reason why ANOVA is considered more desirable than applying multiple t -tests.

Repeated measures ANOVA can be regarded as an extension of the paired t -test, used in situ ations where repeated measurements of the same variable are taken at different points in time (a time series) or under different conditions. Such situations are common in drug trials, but their analysis has certain complexities. Parametric tests based on the normal distribution assume that data sets being compared are independent. This is not the case in a repeated measures study design because the data from different time points or under different conditions come from the same subjects. This means the data sets are related, to account for which an additional assumption has to be introduced into the analysis. This is the assumption of sphericity which implies that the data should not only have the same variance at each time but also that the correlations between all pairs of repeated measurements should be equal. This is often not the case – in the typical repeated measures situation, values at adjacent time points are likely to be closer to one another than those further apart. The effect of violation of the sphericity assumption is loss of power, that is, the probability of Type II error is increased. Software may offer the Mauchly's test of sphericity which tests the hypothesis that the variances of the different conditions are equal. If Mauchly's test returns a significant P value we cannot rely on the F -statistics produced in the conventional manner. The Greenhouse-Geisser or Hunyh-Feldt correction factors are applied in this situation.

ANOVA is a powerful statistical technique and many complex analyses are possible. However, there are also many pitfalls, especially with repeated measures ANOVA. Assistance from an experienced statistician is highly desirable before making elaborate ANOVA analyses the basis for important clinical decisions. [Box 2] provides two examples of ANOVA use from literature. The second example is more complicated, and readers can download and study the full paper for understanding it better.

   Nonparametric Tests Top

If the assumptions for the parametric tests are not met, there are many nonparametric alternatives for comparing data sets. [Table 2] provides a summary.
Table 2: Nonparametric tests commonly applied for assessing difference between numerical data sets

Click here to view

Nonparametric tests do not assume normality or some other underlying distribution and hence may be referred to as distribution-free tests. In addition to testing data that are known not to be normally distributed, they should be used for small samples where it is unlikely that the data can be demonstrated to be normally distributed. They do, however, have some underlying assumptions:

  • Data are at least on an ordinal scale
  • Observations within a group are independent of one another
  • The samples have been drawn randomly from the population.

Nonparametric tests convert the raw values into ranks and then perform calculations on these ranks to obtain a test statistic. The test statistic is then compared with known values for the sampling distribution of that statistic, and the null hypothesis is accepted or rejected. With these tests, the null hypothesis is that the samples come from populations with the same median. However, it is to be noted that, if applied to parametric data, nonparametric tests may fail to detect a significant difference where a parametric test may. In other words, they have less power to detect a statistically significant difference. Therefore, it is worthwhile to test for normality before finally selecting the test of significance to be applied.

   Mann–whitney U-Test and Wilcoxon Rank Sum Test Top

The Mann–Whitney U-test is a distribution-free test used to determine whether two independent groups have been drawn from the same population. The test statistic U is calculated from comparing each pair of values, one from each group, scoring these pairs 1 or 0 depending on whether the first group observation is higher or lower than that from the second group and summing the resulting scores over all pairs. The calculated test statistic is then referred to the appropriate table of critical values to decide whether the null hypothesis of no difference in the location of the two data sets can be rejected. This test has fewer assumptions and can be more powerful than the t -test when conditions for the latter are not satisfied.

In the Wilcoxon rank sum test, data from both the groups are combined and treated as one large group. Then the data are ordered and given ranks, separated back into their original groups, and the ranks in each group are then added to give the test statistic for each group. Tied data are given the same rank, calculated as the mean rank of the tied observations. The test then determines whether or not the sum of ranks in one group is different from that in the other. This test essentially gives results identical to the more frequently used Mann–Whitney U-test.

   Wilcoxon Signed-Rank Test Top

This test is considered as the nonparametric counterpart of the Student's paired t -test. It is named after Frank Wilcoxon, who, in a single paper in 1845, proposed both this test and the rank-sum test for two independent samples.

The test assumes data are paired and come from the same population. As with the paired t -test, the differences between pairs are calculated but then the absolute differences are ranked (without regard to whether they are positive or negative). The positive or negative signs of the original differences are preserved and assigned back to the corresponding ranks when calculating the test statistic. The sum of the positive ranks is compared with the sum of the negative ranks. The sums are expected to be equal if there is no difference between groups. The test statistic is designated as W .

Before the development of Wilcoxon matched pairs signed-rank test, the sign test was the statistical method used to check for consistent differences between pairs of observations. For comparisons of paired observations (designated x , y ) the sign test is most useful if comparisons are only expressed as x > y , x = y , or x < 60; y . If, instead, the observations can be expressed as numeric quantities (e.g., x = 7, y = 8), or as ranks (e.g., rank of x = 2nd, rank of y = 8th), then the Wilcoxon signed-rank test will usually have greater power than the sign test to detect consistent differences. If x and y are quantitative variables in the situation when we can draw paired samples, the sign test can be used to test the hypothesis that the difference between the median of x and the median of y is zero, assuming continuous distributions of the two variables x and y . The sign test can also assess if the median of a collection of numbers is significantly greater or lesser than a specified value.

   Kruskal–wallis Test Top

The Kruskal–Wallis test checks the null hypothesis that k independent groups come from populations with the same median. It is thus a multiple group extension of the two sample Mann–Whitney U-test.

Calculation of the test statistic H entails rank ordering of the data like other distribution-free tests. It is also referred to as the Kruskal–Wallis ANOVA because the calculation is analogous to the ANOVA of a one-way design.

If a significant difference is found between groups, post hoc comparisons need to be performed to determine where the difference lies. The Mann–Whitney U -test with a Bonferroni correction may be applied for this. Alternatively, the Dunn's test (named after Olive Dunn, 1964), which is also based on the Bonferroni correction principle, may be used.

   Friedman's Test Top

Named after the Nobel Prize winning economist Milton Friedman, who introduced it in 1937, this tests the null hypothesis that k repeated measures or matched groups come from populations with the same median. It represents the distribution free counterpart of repeated measures ANOVA.

The test may be regarded as a nonparametric ANOVA for a two-way crossed design, in which the observations are replaced by their ranks. As such, it is also referred to as Friedman's two-way ANOVA or simply Friedman's ANOVA.

Post hoc tests need to be performed if a significant difference is found. The Dunn's test may be used for this purpose.

[Box 3] provides some examples from literature of use of nonparametric tests.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

   Further Reading Top

  1. Pereira-Maxwell F. A-Z of Medical Statistics: A Companion for Critical Appraisal. London: Arnold (Hodder Headline Group); 1998.
  2. Kirkwood BR, Sterne JA. Essential Medical Statistics. 2 nd ed. Oxford: Blackwell Science; 2003.
  3. Everitt BS. Medical Statistics from A to Z: A Guide for Clinicians and Medical Students. Cambridge: Cambridge University Press; 2006.
  4. Miles PS. Statistical Methods for Anaesthesia and Intensive Care. Oxford: Butterworth-Heinemann; 2000.
  5. Field A. Discovering Statistics Using SPSS. 3 rd ed. London: SAGE Publications Ltd.; 2009.


  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1], [Table 2]

This article has been cited by
1 Characterization of m6A regulator-mediated methylation modification patterns and tumor microenvironment infiltration in acute myeloid leukemia
Shiyu Han, Jiaqian Qi, Kun Fang, Hong Wang, Yaqiong Tang, Depei Wu, Yue Han
Cancer Medicine. 2022;
[Pubmed] | [DOI]
2 Investigation on the regulatory T cells signature and relevant Foxp3/ STAT3 axis in esophageal cancer
Lin Yang, Qijie Zhao, Xing Wang, Chalermchai Pilapong, Yi Li, Jun Zou, Jing Jin, Jinfeng Rong
Cancer Medicine. 2022;
[Pubmed] | [DOI]
3 The hub ten gene-based risk score system using RNA m6A methylation regulator features and tumor immune microenvironment in breast cancer
Baowen Yuan, Wei Liu, Miaomiao Huo, Jingyao Zhang, Yunkai Yang, Tianyang Gao, Xin Yin, Tianshu Yang, Xu Teng, Wei Huang, Hefen Yu
Breast Cancer. 2022;
[Pubmed] | [DOI]
4 Environmental pressure on estuary sediment texture and grain size in India versus other locations (China, South Korea, Uruguay, and Argentina)
Remy Rumuri, Thirunavukkarasu Ramkumar, Sivaprakasam Vasudevan, Gopalakrishnan Gnanachandrasamy
Arabian Journal of Geosciences. 2022; 15(8)
[Pubmed] | [DOI]
5 Comprehensive analysis of pyroptosis regulation patterns and their influence on tumor immune microenvironment and patient prognosis in glioma
Tianyu Fan, Yi Wan, Delei Niu, Bin Wang, Bei Zhang, Zugui Zhang, Yue Zhang, Zheng Gong, Li Zhang
Discover Oncology. 2022; 13(1)
[Pubmed] | [DOI]
6 TMEM92 acts as an immune-resistance and prognostic marker in pancreatic cancer from the perspective of predictive, preventive, and personalized medicine
Simeng Zhang, Xing Wan, Mengzhu Lv, Ce Li, Qiaoyun Chu, Guan Wang
EPMA Journal. 2022;
[Pubmed] | [DOI]
7 Albumin is an independent predictor of up to 9-year mortality for intra-capsular femoral neck fractures aiding in decision-making for total hip arthroplasty or hemiarthroplasty.
Bijai Kurian Thomas, Stefan Bajada, Rhodri Williams
The Journal of Arthroplasty. 2022;
[Pubmed] | [DOI]
8 Dynamics of changes in the breed composition of pastoral and agro-pastoral cattle herds in Benin: implications for the sustainable use of indigenous breeds
S.O. Houessou, S.F.U. Vanvanhossou, R.V.C. Diogo, L.H. Dossa
Heliyon. 2022; : e09229
[Pubmed] | [DOI]
9 Deletions of the cystathionine-ß-synthase (CBS) and cystathionine-?-lyase (CSE) genes, involved in the control of hydrogen sulfide biosynthesis, significantly affect lifespan and fitness components of Drosophila melanogaster
Mikhail V. Shaposhnikov, Alexey S. Zakluta, Nadezhda V. Zemskaya, Zulfiya G. Guvatova, Victoria Y. Shilova, Daria V. Yakovleva, Anastasia A. Gorbunova, Liubov A. Koval, Natalia S. Ulyasheva, Mikhail B. Evgen’ev, Olga G. Zatsepina, Alexey A. Moskalev
Mechanisms of Ageing and Development. 2022; 203: 111656
[Pubmed] | [DOI]
10 Physician Electronic Health Record Usage as Affected by the COVID-19 Pandemic
Elise Ruan, Moshe Beiser, Vivian Lu, Soaptarshi Paul, Jason Ni, Nijas Nazar, Jianyou Liu, Mimi Kim, Eric Epstein, Marla Keller, Elizabeth Kitsis, Yaron Tomer, Sunit P. Jariwala
Applied Clinical Informatics. 2022; 13(04): 785
[Pubmed] | [DOI]
11 Two Pyroptosis-Related Subtypes with Distinct Immune Microenvironment Characteristics and a Novel Signature for Predicting Immunotherapy Response and Prognosis in Uveal Melanoma
Shizhen Lei, Haihui Li
Current Eye Research. 2022; : 1
[Pubmed] | [DOI]
12 Validated LC/MS/MS Method for the Determination of Rivastigmine in Human Plasma: Application to a Pharmacokinetic Study in Egyptian Volunteers to Determine the Effect of Gender and Body Mass Index
Ehab F ElKady, Eman A Mostafa
Journal of Chromatographic Science. 2022;
[Pubmed] | [DOI]
13 Methylation Pattern Mediated by m6A Regulator and Tumor Microenvironment Invasion in Lung Adenocarcinoma
Feng Jiang, Yifang Hu, Xiaoqin Liu, Ming Wang, Chuyan Wu, Alessandro Poggi
Oxidative Medicine and Cellular Longevity. 2022; 2022: 1
[Pubmed] | [DOI]
14 A New Inflammation-Related Risk Model for Predicting Hepatocellular Carcinoma Prognosis
Mindan Xing, Jia Li, Paolo Magistri
BioMed Research International. 2022; 2022: 1
[Pubmed] | [DOI]
15 Prognosis of Tumor Microenvironment in Luminal B-Type Breast Cancer
Ji Lv, Jia Ren, Jie Zheng, Fuliang Zhang, Meng Han, Yuanwei Zhang
Disease Markers. 2022; 2022: 1
[Pubmed] | [DOI]
16 C1q/tumor necrosis factor related protein 6 (CTRP6) regulates the phenotypes of high glucose-induced gestational trophoblast cells via peroxisome proliferator-activated receptor gamma (PPAR?) signaling
Jin Zhang, Wen-Pei Bai
Bioengineered. 2022; 13(1): 206
[Pubmed] | [DOI]
17 Stearoyl-CoA desaturase 1 regulates malignant progression of cervical cancer cells
Lingling Wang, Guoliu Ye, Yan Wang, Caizhi Wang
Bioengineered. 2022; 13(5): 12941
[Pubmed] | [DOI]
18 Diagnostic and prognostic values of pyroptosis-related genes for the hepatocellular carcinoma
Mindan Xing, Jia Li
BMC Bioinformatics. 2022; 23(1)
[Pubmed] | [DOI]
19 The Effect of Public Rental Housing on Birth Interval of Newlyweds and Policy Implications
Hoon Lee, Jinuk Sung
Journal of Korea Planning Association. 2022; 57(5): 136
[Pubmed] | [DOI]
20 Molecular subtypes, prognostic and immunotherapeutic relevant gene signatures mediated by DNA methylation regulators in hepatocellular carcinoma
Rongfeng Shi, Hui Zhao, Suming Zhao, Hongxin Yuan
Aging. 2022;
[Pubmed] | [DOI]
21 DNA methylation regulator-mediated modification patterns and tumor microenvironment characterization in glioma
Haitao Luo, Minhua Ye, Yan Hu, Miaojing Wu, Mengqi Cheng, Xingen Zhu, Kai Huang
Aging. 2022;
[Pubmed] | [DOI]
22 Molecular subtypes identified by pyroptosis-related genes are associated with tumor microenvironment cell infiltration in colon cancer
Yiting Ling, Yinda Wang, Chenxi Cao, Lianzhong Feng, Binzhong Zhang, Senjuan Li
Aging. 2022;
[Pubmed] | [DOI]
23 SPOP promotes cervical cancer progression by inducing the movement of PD-1 away from PD-L1 in spatial localization
Jiangchun Wu, Yong Wu, Qinhao Guo, Siyu chen, Simin Wang, Xiaohua Wu, Jun Zhu, Xingzhu Ju
Journal of Translational Medicine. 2022; 20(1)
[Pubmed] | [DOI]
24 Identification of mRNA vaccines and conserved ferroptosis related immune landscape for individual precision treatment in bladder cancer
Cheng-Peng Gui, Jia-Ying Li, Liang-Min Fu, Cheng-Gong Luo, Chi Zhang, Yi-Ming Tang, Li-zhen Zhang, Guan-nan Shu, Rong-Pei Wu, Jun-Hang Luo
Journal of Big Data. 2022; 9(1)
[Pubmed] | [DOI]
25 Molecular Characterization of m6A Modifications in Non-Clear Cell Renal Cell Carcinoma and Potential Relationship with Pathological Types
Xuebao Xiang, Yi Guo, Zhongyuan Chen, Zengnan Mo
International Journal of General Medicine. 2022; Volume 15: 1595
[Pubmed] | [DOI]
26 A signature based on chromatin regulation and tumor microenvironment infiltration in clear cell renal cell carcinoma
Chen Yang, Tian Yu, Qin Lin
Epigenomics. 2022;
[Pubmed] | [DOI]
27 Marginal Microleakage of Glass Ionomer-Based Restorations After Conventional Cavity Preparation and Er: YAG Laser Irradiation
Zeynep Buket KAYNAR, Nazmiye DÖNMEZ, Seyda HERGUNER-SISO
Journal of Basic and Clinical Health Sciences. 2022;
[Pubmed] | [DOI]
28 Analysis of N6-Methyladenosine Modification Patterns and Tumor Immune Microenvironment in Pancreatic Adenocarcinoma
Yong Liu, Guangbing Li, Yang Yang, Ziwen Lu, Tao Wang, Xiaoyu Wang, Jun Liu
Frontiers in Genetics. 2022; 12
[Pubmed] | [DOI]
29 Identification of genes modified by N6-methyladenosine in patients with colorectal cancer recurrence
Qianru Zhu, Xingxing Huang, Shuxian Yu, Lan Shou, Ruonan Zhang, Han Xie, Zimao Liang, Xueni Sun, Jiao Feng, Ting Duan, Mingming Zhang, Yu Xiang, Xinbing Sui, Weiwei Jin, Lili Yu, Qibiao Wu
Frontiers in Genetics. 2022; 13
[Pubmed] | [DOI]
30 A Novel Ras--Related Signature Improves Prognostic Capacity in Oesophageal Squamous Cell Carcinoma
Hao-Shuai Yang, Wei Liu, Shao-Yi Zheng, He-Yuan Cai, Hong-He Luo, Yan-Fen Feng, Yi-Yan Lei
Frontiers in Genetics. 2022; 13
[Pubmed] | [DOI]
31 The Expression Pattern of Ferroptosis-Related Genes in Colon Adenocarcinoma: Highly Correlated to Tumor Microenvironment Characteristics
Jie Liu, Hui Li, Shen Zhao, Rongbo Lin, Jiaqing Yu, Nanfeng Fan
Frontiers in Genetics. 2022; 13
[Pubmed] | [DOI]
32 Identification of Crucial Gene Modules Related to the Efficiency of Anti-PD-1/PD-L1 Therapy and Comprehensive Analyses of a Novel Signature Based on These Modules
Wei Wang, Dong Dong, Liang Chen, Heng Wang, Bo Bi, Tianyi Liu
Frontiers in Genetics. 2022; 13
[Pubmed] | [DOI]
33 Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer
Mengjia Huang, Yuqing Ye, Yi Chen, Junkai Zhu, Li Xu, Wenxuan Cheng, Xiaofan Lu, Fangrong Yan
Frontiers in Genetics. 2022; 13
[Pubmed] | [DOI]
34 Follicular Helper T-Cell-Based Classification of Endometrial Cancer Promotes Precise Checkpoint Immunotherapy and Provides Prognostic Stratification
Yi Chen, Shuwen You, Jie Li, Yifan Zhang, Georgia Kokaraki, Elisabeth Epstein, Joseph Carlson, Wen-Kuan Huang, Felix Haglund
Frontiers in Immunology. 2022; 12
[Pubmed] | [DOI]
35 m7G regulator-mediated methylation modification patterns define immune cell infiltration and patient survival
Lu Wang, Xing Hu, Xiaoni Liu, Yingmei Feng, Yuan Zhang, Jing Han, Xuqing Liu, Fankun Meng
Frontiers in Immunology. 2022; 13
[Pubmed] | [DOI]
36 The Hypoxic Landscape Stratifies Gastric Cancer Into 3 Subtypes With Distinct M6a Methylation and Tumor Microenvironment Infiltration Characteristics
Zhi-kun Ning, Ce-gui Hu, Jiang Liu, Hua-kai Tian, Zhong-lin Yu, Hao-nan Zhou, Hui Li, Zhen Zong
Frontiers in Immunology. 2022; 13
[Pubmed] | [DOI]
37 Combining Immune-Related Genes For Delineating the Extracellular Matrix and Predicting Hormone Therapy and Neoadjuvant Chemotherapy Benefits In Breast Cancer
Jianyu Liu, Bo Lei, Xin Yu, Yingpu Li, Yuhan Deng, Guang Yang, Zhigao Li, Tong Liu, Leiguang Ye
Frontiers in Immunology. 2022; 13
[Pubmed] | [DOI]
38 Molecular subtypes of osteosarcoma classified by cancer stem cell related genes define immunological cell infiltration and patient survival
Lei Guo, Taiqiang Yan, Wei Guo, Jianfang Niu, Wei Wang, Tingting Ren, Yi Huang, Jiuhui Xu, Boyang Wang
Frontiers in Immunology. 2022; 13
[Pubmed] | [DOI]
39 A novel cuproptosis-related prognostic signature and potential value in HCC immunotherapy
Xiang-Xu Wang, Li-Hong Wu, Hongchen Ji, Qing-Qing Liu, Shi-Zhou Deng, Qiong-Yi Dou, Liping Ai, Wei Pan, Hong-Mei Zhang
Frontiers in Molecular Biosciences. 2022; 9
[Pubmed] | [DOI]
40 Construction of m6A-based prognosis signature and prediction for immune and anti-angiogenic response
Xiang-Xu Wang, Li-Hong Wu, Qiong-Yi Dou, Liping Ai, Yajie Lu, Shi-Zhou Deng, Qing-Qing Liu, Hongchen Ji, Hong-Mei Zhang
Frontiers in Molecular Biosciences. 2022; 9
[Pubmed] | [DOI]
41 Ferroptosis Regulator Modification Patterns and Tumor Microenvironment Immune Infiltration Characterization in Hepatocellular Carcinoma
Dong-Li Liu, Ming-Yao Wu, Tie-Ning Zhang, Chun-Gang Wang
Frontiers in Molecular Biosciences. 2022; 9
[Pubmed] | [DOI]
42 Immune Cell Infiltration and Relevant Gene Signatures in the Tumor Microenvironment that Significantly Associates With the Prognosis of Patients With Breast Cancer
Qiang Xu, Xinghe Yan, Zhezhu Han, Xiuying Jin, Yongmin Jin, Honghua Sun, Junhua Liang, Songnan Zhang
Frontiers in Molecular Biosciences. 2022; 9
[Pubmed] | [DOI]
43 Analysis of m6A Methylation Modification Patterns and Tumor Immune Microenvironment in Breast Cancer
Menglu Dong, Wenzhuang Shen, Guang Yang, Zhifang Yang, Xingrui Li
Frontiers in Cell and Developmental Biology. 2022; 10
[Pubmed] | [DOI]
44 Identification and Validation of N6-Methyladenosine-Related Biomarkers for Bladder Cancer: Implications for Immunotherapy
Hongyu Deng, Faqing Tang, Ming Zhou, Dongyong Shan, Xingyu Chen, Ke Cao
Frontiers in Oncology. 2022; 12
[Pubmed] | [DOI]
45 A trained communication partner’s use of responsive strategies in aided communication with three adults with Rett syndrome: A case report
Helena Wandin, Per Lindberg, Karin Sonnander
Frontiers in Psychology. 2022; 13
[Pubmed] | [DOI]
46 Novel Risk Classification Based on Pyroptosis-Related Genes Defines Immune Microenvironment and Pharmaceutical Landscape for Hepatocellular Carcinoma
Jianye Wang, Ying Wang, Marcella Steffani, Christian Stöß, Donna Ankerst, Helmut Friess, Norbert Hüser, Daniel Hartmann
Cancers. 2022; 14(2): 447
[Pubmed] | [DOI]
47 APOBEC Alteration Contributes to Tumor Growth and Immune Escape in Pan-Cancer
Honghong Guo, Ling Zhu, Lu Huang, Zhen Sun, Hui Zhang, Baoting Nong, Yuanyan Xiong
Cancers. 2022; 14(12): 2827
[Pubmed] | [DOI]
48 Investigation of Gut Bacterial Communities of Asian Citrus Psyllid (Diaphorina citri) Reared on Different Host Plants
Lixue Meng, Changxiu Xia, Zhixiong Jin, Hongyu Zhang
Insects. 2022; 13(8): 694
[Pubmed] | [DOI]
49 Effect of Malignancy on Semen Parameters
Guy Shrem, Liat Azani, Ido Feferkorn, Tamar Listovsky, Sofia Hussaini, Benjamin Farber, Michael H. Dahan, Mali Salmon-Divon
Life. 2022; 12(6): 922
[Pubmed] | [DOI]
50 Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research
Katherine Huang, Natalie Thomas, Paul R. Gooley, Christopher W. Armstrong
Metabolites. 2022; 12(10): 963
[Pubmed] | [DOI]
51 How to conduct inferential statistics online: A brief hands-on guide for biomedical researchers
Shaikat Mondal, Swarup Saha, Himel Mondal, Rajesh De, Rabindranath Majumder, Koushik Saha
Indian Journal of Vascular and Endovascular Surgery. 2022; 9(1): 54
[Pubmed] | [DOI]
52 Vergelijking van de prehospitaal afwijkende respiratoire parameters in de eerste twee Covid-19-perioden in Brussel: een retrospectieve cohortstudie
I. Piepers, E. Verhoeven, I. Hubloue
Tijdschrift voor Geneeskunde. 2022;
[Pubmed] | [DOI]
53 Single-cell analysis of a tumor-derived exosome signature correlates with prognosis and immunotherapy response
Jiani Wu, Dongqiang Zeng, Shimeng Zhi, Zilan Ye, Wenjun Qiu, Na Huang, Li Sun, Chunlin Wang, Zhenzhen Wu, Jianping Bin, Yulin Liao, Min Shi, Wangjun Liao
Journal of Translational Medicine. 2021; 19(1)
[Pubmed] | [DOI]
54 Matrix Effects of the Hydroethanolic Extract of Calyces of Physalis peruviana L. on Rutin Pharmacokinetics in Wistar Rats Using Population Modeling
Gina Paola Domínguez Moré, María Isabel Cardona, Paula Michelle Sepúlveda, Sandra Milena Echeverry, Cláudia Maria Oliveira Simões, Diana Marcela Aragón
Pharmaceutics. 2021; 13(4): 535
[Pubmed] | [DOI]
55 Soils Carbon Stocks and Litterfall Fluxes from the Bornean Tropical Montane Forests, Sabah, Malaysia
Nurul Syakilah Suhaili, Syahrir Mhd Hatta, Daniel James, Affendy Hassan, Mohamadu Boyie Jalloh, Mui-How Phua, Normah Awang Besar
Forests. 2021; 12(12): 1621
[Pubmed] | [DOI]
56 Pyroptosis Patterns Characterized by Distinct Tumor Microenvironment Infiltration Landscapes in Gastric Cancer
Renshen Xiang, Yuhang Ge, Wei Song, Jun Ren, Can Kong, Tao Fu
Genes. 2021; 12(10): 1535
[Pubmed] | [DOI]
57 Joint Modeling of Singleton Preterm Birth and Perinatal Death Using Birth Registry Cohort Data in Northern Tanzania
Innocent B. Mboya, Michael J. Mahande, Joseph Obure, Henry G. Mwambi
Frontiers in Pediatrics. 2021; 9
[Pubmed] | [DOI]
58 Significance of N6-Methyladenosine RNA Methylation Regulators in Immune Infiltrates of Ovarian Cancer
Jing Gu, Fangfang Bi
Frontiers in Genetics. 2021; 12
[Pubmed] | [DOI]
59 Immunosuppressive Microenvironment Revealed by Immune Cell Landscape in Pre-metastatic Liver of Colorectal Cancer
Dongqiang Zeng, Miaohong Wang, Jiani Wu, Siheng Lin, Zilan Ye, Rui Zhou, Gaofeng Wang, Jianhua Wu, Huiying Sun, Jianping Bin, Yulin Liao, Nailin Li, Min Shi, Wangjun Liao
Frontiers in Oncology. 2021; 11
[Pubmed] | [DOI]
60 Identification of Gene-Set Signature in Early-Stage Hepatocellular Carcinoma and Relevant Immune Characteristics
Qijie Zhao, Rawiwan Wongpoomchai, Arpamas Chariyakornkul, Zhangang Xiao, Chalermchai Pilapong
Frontiers in Oncology. 2021; 11
[Pubmed] | [DOI]
61 Extracellular Matrix Characterization in Gastric Cancer Helps to Predict Prognosis and Chemotherapy Response
Zhi Yang, Feifei Xue, Minhuan Li, Xingya Zhu, Xiaofeng Lu, Chao Wang, En Xu, Xingzhou Wang, Liang Zhang, Heng Yu, Chuanfu Ren, Hao Wang, Yizhou Wang, Jie Chen, Wenxian Guan, Xuefeng Xia
Frontiers in Oncology. 2021; 11
[Pubmed] | [DOI]
62 Comprehensive Analysis of m6A Regulators Characterized by the Immune Cell Infiltration in Head and Neck Squamous Cell Carcinoma to Aid Immunotherapy and Chemotherapy
Zhiqiang Yang, Xiaoping Ming, Shuo Huang, Minlan Yang, Xuhong Zhou, Jiayu Fang
Frontiers in Oncology. 2021; 11
[Pubmed] | [DOI]
63 Metabolism-Relevant Molecular Classification Identifies Tumor Immune Microenvironment Characterization and Immunotherapeutic Effect in Cervical Cancer
Luyi Li, Hui Gao, Danhan Wang, Hao Jiang, Hongzhu Wang, Jiajian Yu, Xin Jiang, Changjiang Huang
Frontiers in Molecular Biosciences. 2021; 8
[Pubmed] | [DOI]
64 The N6-Methyladenosine-Modified Pseudogene HSPA7 Correlates With the Tumor Microenvironment and Predicts the Response to Immune Checkpoint Therapy in Glioblastoma
Rongrong Zhao, Boyan Li, Shouji Zhang, Zheng He, Ziwen Pan, Qindong Guo, Wei Qiu, Yanhua Qi, Shulin Zhao, Shaobo Wang, Zihang Chen, Ping Zhang, Xing Guo, Hao Xue, Gang Li
Frontiers in Immunology. 2021; 12
[Pubmed] | [DOI]
65 Comprehensive of N1-Methyladenosine Modifications Patterns and Immunological Characteristics in Ovarian Cancer
Jinhui Liu, Can Chen, Yichun Wang, Cheng Qian, Junting Wei, Yan Xing, Jianling Bai
Frontiers in Immunology. 2021; 12
[Pubmed] | [DOI]
66 Identification of Immune-Related Subtypes and Characterization of Tumor Microenvironment Infiltration in Bladder Cancer
Mengjia Huang, Lin Liu, Junkai Zhu, Tong Jin, Yi Chen, Li Xu, Wenxuan Cheng, Xinjia Ruan, Liwen Su, Jialin Meng, Xiaofan Lu, Fangrong Yan
Frontiers in Cell and Developmental Biology. 2021; 9
[Pubmed] | [DOI]
67 Uncovering the Association Between m5C Regulator-Mediated Methylation Modification Patterns and Tumour Microenvironment Infiltration Characteristics in Hepatocellular Carcinoma
Xinyu Gu, Haibo Zhou, Qingfei Chu, Qiuxian Zheng, Jing Wang, Haihong Zhu
Frontiers in Cell and Developmental Biology. 2021; 9
[Pubmed] | [DOI]
68 Identification of m6A Regulator-Associated Methylation Modification Clusters and Immune Profiles in Melanoma
Fengying Du, Han Li, Yan Li, Yang Liu, Xinyu Li, Ningning Dang, Qingqing Chu, Jianjun Yan, Zhen Fang, Hao Wu, Zihao Zhang, Xingyu Zhu, Xiaokang Li
Frontiers in Cell and Developmental Biology. 2021; 9
[Pubmed] | [DOI]
69 Tumor microenvironment characterization in cervical cancer identifies prognostic relevant gene signatures
Linyu Peng, Gati Hayatullah, Haiyan Zhou, Shuzhen Chang, Liya Liu, Haifeng Qiu, Xiaoran Duan, Liping Han, Edwin Wang
PLOS ONE. 2021; 16(4): e0249374
[Pubmed] | [DOI]
70 Identification of an Immune Gene Signature Based on Tumor Microenvironment Characteristics in Colon Adenocarcinoma
Ying Chen, Jia Zhao
Cell Transplantation. 2021; 30: 0963689721
[Pubmed] | [DOI]
71 Classification and clinical value of three immune subtypes of ovarian cancer based on transcriptome data
Li Yuan, Qiang An, Ting Liu, Jukun Song
All Life. 2021; 14(1): 963
[Pubmed] | [DOI]
72 Identification of immune-based prostate cancer subtypes using mRNA expression
Jukun Song, Wei Wang, Yiwen Yuan, Yong Ban, Jiaming Su, Dongbo Yuan, Weihong Chen, Jianguo Zhu
Bioscience Reports. 2021; 41(1)
[Pubmed] | [DOI]
73 MAFLD better predicts the progression of atherosclerotic cardiovascular risk than NAFLD: Generalized estimating equation approach
Tsubasa Tsutsumi, Mohammed Eslam, Takumi Kawaguchi, Sakura Yamamura, Atsushi Kawaguchi, Dan Nakano, Masahiro Koseki, Shinobu Yoshinaga, Hirokazu Takahashi, Keizo Anzai, Jacob George, Takuji Torimura
Hepatology Research. 2021; 51(11): 1115
[Pubmed] | [DOI]
74 NOD2 deficiency confers a pro-tumorigenic macrophage phenotype to promote lung adenocarcinoma progression
Yibei Wang, Ziwei Miao, Xiaoxue Qin, Bo Li, Yun Han
Journal of Cellular and Molecular Medicine. 2021; 25(15): 7545
[Pubmed] | [DOI]
75 Mycobactericidal Effects of Different Regimens Measured by Molecular Bacterial Load Assay among People Treated for Multidrug-Resistant Tuberculosis in Tanzania
Peter M. Mbelele, Emmanuel A. Mpolya, Elingarami Sauli, Bariki Mtafya, Nyanda E. Ntinginya, Kennedy K. Addo, Katharina Kreppel, Sayoki Mfinanga, Patrick P. J. Phillips, Stephen H. Gillespie, Scott K. Heysell, Wilber Sabiiti, Stellah G. Mpagama, Christine Y. Turenne
Journal of Clinical Microbiology. 2021; 59(4)
[Pubmed] | [DOI]
76 m6A-Mediated Tumor Invasion and Methylation Modification in Breast Cancer Microenvironment
Fei Liu, Xiaopeng Yu, Guijin He, Jimei Wang
Journal of Oncology. 2021; 2021: 1
[Pubmed] | [DOI]
77 Molecular subtypes based on ferroptosis-related genes and tumor microenvironment infiltration characterization in lung adenocarcinoma
Weiju Zhang, Sumei Yao, Hua Huang, Hao Zhou, Haomiao Zhou, Qishuang Wei, Tingting Bian, Hui Sun, Xiaoli Li, Jianguo Zhang, Yifei Liu
OncoImmunology. 2021; 10(1)
[Pubmed] | [DOI]
78 Effect of Radiofrequency Waves of Mobile Phones on Distortion Product Otoacoustic Emissions
Swetapadma Nayak, Rajeshwary Aroor, Usha Shastri, M. K. Goutham, Devika Sinha
Journal of Health and Allied Sciences NU. 2021;
[Pubmed] | [DOI]
79 Volatile allosteric antagonists of mosquito odorant receptors inhibit human-host attraction
Georgia Kythreoti, Nadia Sdralia, Panagiota Tsitoura, Dimitrios P. Papachristos, Antonios Michaelakis, Vasileios Karras, David M. Ruel, Esther Yakir, Jonathan D. Bohbot, Stefan Schulz, Kostas Iatrou
Journal of Biological Chemistry. 2021; 296: 100172
[Pubmed] | [DOI]
80 Well-to-well correlation and identifying lithological boundaries by principal component analysis of well-logs
Amir Mohammad Karimi, Saeid Sadeghnejad, Mansoor Rezghi
Computers & Geosciences. 2021; 157: 104942
[Pubmed] | [DOI]
81 Identification of copy number variation-driven molecular subtypes informative for prognosis and treatment in pancreatic adenocarcinoma of a Chinese cohort
Qian Zhan, Chenlei Wen, Yi Zhao, Lu Fang, Yangbing Jin, Zehui Zhang, Siyi Zou, Fanlu Li, Ying Yang, Lijia Wu, Jiabin Jin, Xiongxiong Lu, Junjie Xie, Dongfeng Cheng, Zhiwei Xu, Jun Zhang, Jiancheng Wang, XiaXing Deng, Hao Chen, Chenghong Peng, Hongwei Li, Henghui Zhang, Hai Fang, Chaofu Wang, Baiyong Shen
EBioMedicine. 2021; 74: 103716
[Pubmed] | [DOI]
Yahya A. Mohzari, Renad Alshuraim, Syed Mohammed Basheeruddin Asdaq, Fahad Aljobair, Ahmed Alrashed, Yazed Saleh Alsowaida, Amnah Alamer, Manea Fares Al Munjem, Mohammed I. Al Musawa, Muhannad Hatata, Meshal A. Alzaaqi, Aljawharah Binrokan, Saleh Ahmad Alajlan, Ivo Abraham, Ahmad Alamer
Journal of Infection and Public Health. 2021;
[Pubmed] | [DOI]
83 Race, Ethnicity, and Other Sociodemographic Characteristics of Patients with Hospital Admission for Migraine in the United States
Francesco Amico, Sait Ashina, Eliot Parascandolo, Roni Sharon
Journal of the National Medical Association. 2021;
[Pubmed] | [DOI]
84 Comment on “Evaluating the efficiency of infrared breast thermography for early breast cancer risk prediction in asymptomatic population”
Edgar Guevara, Francisco Javier González
Infrared Physics & Technology. 2021; 113: 103615
[Pubmed] | [DOI]
85 Testing pigeon control efficiency by different methods in urban industrial areas, Hungary
Thabang Rainett Teffo, Gergo Fuszonecker, Krisztián Katona
Biologia Futura. 2021;
[Pubmed] | [DOI]
86 S53P4 bioactive glass scaffolds induce BMP expression and integrative bone formation in a critical-sized diaphysis defect treated with a single-staged induced membrane technique
E. Eriksson, R. Björkenheim, G. Strömberg, M. Ainola, P. Uppstu, L. Aalto-Setälä, V-M. Leino, L. Hupa, J. Pajarinen, N.C. Lindfors
Acta Biomaterialia. 2021; 126: 463
[Pubmed] | [DOI]
87 Exploring urban tree diversity and carbon stocks in Zaria Metropolis, North Western Nigeria
Murtala Dangulla, Latifah Abd Manaf, Mohammad Firuz Ramli, Mohd Rusli Yacob, Sanusi Namadi
Applied Geography. 2021; 127: 102385
[Pubmed] | [DOI]
88 Immunogenomic characterization in gastric cancer identifies microenvironmental and immunotherapeutically relevant gene signatures
Xiao Han, Heyue Lu, Xiaojun Tang, Yao Zhao, Hongxue Liu
Immunity, Inflammation and Disease. 2021;
[Pubmed] | [DOI]
89 Integrated Assessment of Cd-contaminated Paddy Soil with Application of Combined Ameliorants: A Three-Year Field Study
Guobing Wang, Wenchao Du, Meiling Xu, Fuxun Ai, Ying Yin, Hongyan Guo
Bulletin of Environmental Contamination and Toxicology. 2021; 107(6): 1236
[Pubmed] | [DOI]
90 Application of M5 model tree optimized with Excel Solver Platform for water quality parameter estimation
Maryam Bayatvarkeshi, Monzur Alam Imteaz, Ozgur Kisi, Mahtab Zarei, Zaher Mundher Yaseen
Environmental Science and Pollution Research. 2021; 28(6): 7347
[Pubmed] | [DOI]
91 Tumor Microenvironment Characterization in Glioblastoma Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures
Jinsen Zhang, Xing Xiao, Xin Zhang, Wei Hua
Journal of Molecular Neuroscience. 2020; 70(5): 738
[Pubmed] | [DOI]
92 Integration of multiple key molecules in lung adenocarcinoma identifies prognostic and immunotherapeutic relevant gene signatures
Qiong Wu, Lei Wang, Huagen Wei, Ben Li, Jiaming Yang, Zilin Wang, Jianfeng Xu, You Lang Zhou, Bo Zhang
International Immunopharmacology. 2020; 83: 106477
[Pubmed] | [DOI]
93 Autophagy-related gene expression classification defines three molecular subtypes with distinct clinical and microenvironment cell infiltration characteristics in colon cancer
Shajun Zhu, Qiong Wu, Bo Zhang, Huagen Wei, Ben Li, Wei Shi, Miao Fang, Shengze Zhu, Lei Wang, You Lang Zhou, Yulin Dong
International Immunopharmacology. 2020; 87: 106757
[Pubmed] | [DOI]
94 Identification of prognostic and immune-related gene signatures in the tumor microenvironment of endometrial cancer
Guangwei Wang, Dandan Wang, Meige Sun, Xiaofei Liu, Qing Yang
International Immunopharmacology. 2020; 88: 106931
[Pubmed] | [DOI]
95 Anxiolytic like effect of L-Carnitine in mice: Evidences for the involvement of NO-sGC-cGMP signaling pathway
Poonam Singh, Vaibhav Walia
Behavioural Brain Research. 2020; 391: 112689
[Pubmed] | [DOI]
96 Association of the Psoriatic Microenvironment With Treatment Response
Gaofeng Wang, Yong Miao, Noori Kim, Evan Sweren, Sewon Kang, Zhiqi Hu, Luis A. Garza
JAMA Dermatology. 2020; 156(10): 1057
[Pubmed] | [DOI]
97 Bio-analytical methods for investigating the effect of age, body mass index and gender on the PK/PD ratio of antibiotics
Ehab F. ElKady, Ahmed A. Abo-Elwafa, Faten Farouk
Biomedical Chromatography. 2020; 34(2)
[Pubmed] | [DOI]
98 Tumor microenvironment characterization in head and neck cancer identifies prognostic and immunotherapeutically relevant gene signatures
Mengqi Huo, Ying Zhang, Zhong Chen, Suxin Zhang, Yang Bao, Tianke Li
Scientific Reports. 2020; 10(1)
[Pubmed] | [DOI]
99 Phytoestrogen genistein hinders ovarian oxidative damage and apoptotic cell death-induced by ionizing radiation: co-operative role of ER-ß, TGF-ß, and FOXL-2
Yasmin Hamdy Haddad, Riham S. Said, Rehab Kamel, Engy M. El Morsy, Ebtehal El-Demerdash
Scientific Reports. 2020; 10(1)
[Pubmed] | [DOI]
100 Formalin-fixed paraffin-embedded renal biopsy tissues: an underexploited biospecimen resource for gene expression profiling in IgA nephropathy
Sharon Natasha Cox, Samantha Chiurlia, Chiara Divella, Michele Rossini, Grazia Serino, Mario Bonomini, Vittorio Sirolli, Francesca B. Aiello, Gianluigi Zaza, Isabella Squarzoni, Concetta Gangemi, Maria Stangou, Aikaterini Papagianni, Mark Haas, Francesco Paolo Schena
Scientific Reports. 2020; 10(1)
[Pubmed] | [DOI]
101 Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma
Ming Wu, Yu Xia, Yadong Wang, Fei Fan, Xian Li, Jukun Song, Jie Ding
Bioscience Reports. 2020; 40(10)
[Pubmed] | [DOI]
102 Identification of three molecular subtypes based on immune infiltration in ovarian cancer and its prognostic value
Juan Liu, Zongjian Tan, Jun He, Tingting Jin, Yuanyuan Han, Li Hu, Jukun Song, Shengwen Huang
Bioscience Reports. 2020; 40(10)
[Pubmed] | [DOI]
103 Alteration in Lysophospholipids and Converting Enzymes in Glaucomatous Optic Nerves
Sasha M. Milbeck, Sanjoy K. Bhattacharya
Investigative Opthalmology & Visual Science. 2020; 61(6): 60
[Pubmed] | [DOI]
104 m6A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer
Bo Zhang, Qiong Wu, Ben Li, Defeng Wang, Lei Wang, You Lang Zhou
Molecular Cancer. 2020; 19(1)
[Pubmed] | [DOI]
105 Comprehensive Bioinformatics Analysis Identifies Tumor Microenvironment and Immune-related Genes in Small Cell Lung Cancer
Yongchun Song, Yanqin Sun, Tuanhe Sun, Ruixiang Tang
Combinatorial Chemistry & High Throughput Screening. 2020; 23(5): 381
[Pubmed] | [DOI]
106 Immunogenomic Profiling and Classification of Prostate Cancer Based on HIF-1 Signaling Pathway
Jukun Song, Weiming Chen, Guohua Zhu, Wei Wang, Fa Sun, Jianguo Zhu
Frontiers in Oncology. 2020; 10
[Pubmed] | [DOI]
107 Microenvironment Analysis of Prognosis and Molecular Signature of Immune-Related Genes in Lung Adenocarcinoma
Bo Ling, Zuliang Huang, Suoyi Huang, Li Qian, Genliang Li, Qianli Tang
Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics. 2020; 28(6): 561
[Pubmed] | [DOI]
108 A novel assessing system for predicting the prognosis of gastric cancer
Siheng Lin, Rui Zhou, Dongqiang Zeng, Jiani Wu, Jianhua Wu, Jingwen Zhang, Huiying Sun, Shaowei Zhu, Min Shi, Jianping Bin, Yulin Liao, Wangjun Liao
Epigenomics. 2019; 11(11): 1251
[Pubmed] | [DOI]
109 Discovery of lipid biomarkers correlated with disease progression in clear cell renal cell carcinoma using desorption electrospray ionization imaging mass spectrometry
Keita Tamura, Makoto Horikawa, Shumpei Sato, Hideaki Miyake, Mitsutoshi Setou
Oncotarget. 2019; 10(18): 1688
[Pubmed] | [DOI]
110 Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures
Dongqiang Zeng, Meiyi Li, Rui Zhou, Jingwen Zhang, Huiying Sun, Min Shi, Jianping Bin, Yulin Liao, Jinjun Rao, Wangjun Liao
Cancer Immunology Research. 2019; 7(5): 737
[Pubmed] | [DOI]
111 Whole Exome and Transcriptome Analyses Integrated with Microenvironmental Immune Signatures of Lung Squamous Cell Carcinoma
Jeong-Sun Seo, Ji Won Lee, Ahreum Kim, Jong-Yeon Shin, Yoo Jin Jung, Sae Bom Lee, Yoon Ho Kim, Samina Park, Hyun Joo Lee, In-Kyu Park, Chang-Hyun Kang, Ji-Young Yun, Jihye Kim, Young Tae Kim
Cancer Immunology Research. 2018; 6(7): 848
[Pubmed] | [DOI]


Print this article  Email this article
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Article in PDF (652 KB)
    Citation Manager
    Access Statistics
    Reader Comments
    Email Alert *
    Add to My List *
* Registration required (free)  

    Comparing Means ...
   Analysis of Variance
   Nonparametric Tests
    Wilcoxon Signed-...
   Friedman's Test
   Further Reading
    Article Figures
    Article Tables

 Article Access Statistics
    PDF Downloaded425    
    Comments [Add]    
    Cited by others 111    

Recommend this journal