Chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing family (CMTM) is a gene family involved in multiple malignancies. CMTM4 is a member of this family and is located at chromosome 16q22.1, a locus that harbours a number of tumour suppressor genes. It has been defined as a regulator of cell cycle and division in HeLa cells; however, its roles in tumourigenesis remain poorly studied. An integrated bioinformatics analysis based on the array data from the GEO database was conducted to view the differential expression of CMTM4 across multiple cancers and their corresponding control tissues. Primary clear cell renal cell carcinoma (ccRCC) and the paired adjacent non-tumour tissues were then collected to examine the expression of CMTM4 by western blotting, immunohistochemistry, and quantitative RT-PCR. The ccRCC cell lines A498 and 786-O and the normal renal tubular epithelial cell line HK-2 were also tested for CMTM4 expression by western blotting. Cell Counting Kit-8 (CCK-8) and viable cell counting assays were used to delineate the growth curves of 786-O cells after CMTM4 overexpression or knockdown. Wound healing and transwell assays were performed to assess the cells’ ability to migrate. The effects of CMTM4 on cellular apoptosis and cell cycle progression were analysed by flow cytometry, and cell cycle hallmarks were detected by western blotting and RT-PCR. The xenograft model in nude mice was used to elucidate the function of CMTM4 in tumourigenesis ex vivo. By omic data analysis, we found a substantial downregulation of CMTM4 in ccRCC. Western blotting then confirmed that CMTM4 was dramatically reduced in 86.9 % (53/61) of ccRCC tissues compared with the paired adjacent non-tumour tissues, as well as in the 786-O and A498 ccRCC cell lines. Restoration of CMTM4 significantly suppressed 786-O cell growth by inducing G2/M cell cycle arrest and p21 upregulation, and cell migration was also inhibited. However, knockdown of CMTM4 led to a completely opposite effect on these cell behaviours. Overexpression of CMTM4 also markedly inhibited the tumour xenograft growth in nude mice. CMTM4 is downregulated and exhibits tumour-suppressor activities in ccRCC, and could be exploited as a target for ccRCC treatment.

To evade immune surveillance, tumors develop a hostile microenvironment that inhibits anti-tumor immunity. Recent immunotherapy breakthroughs that target the reinvigoration of tumor-infiltrating T lymphocytes (TIL) have led to unprecedented success in treating some cancers that are resistant to conventional therapy, suggesting that T cells play a pivotal role in anti-tumor immunity. In the hostile tumor microenvironment (TME), activated T cells are known to mainly rely on aerobic glycolysis to facilitate their proliferation and anti-tumor function. However, TILs usually exhibit an exhausted phenotype and impaired anti-tumor activity due to the limited availability of key nutrients (e.g., glucose) in the TME. Given that different T cell subsets have unique metabolic pathways which determine their effector function, this review introduces our current understanding of T cell development, activation signals and metabolic pathways. Moreover, emerging evidence suggests that fatty acid binding protein 5 (FABP5) expression in T cells regulates T cell lipid metabolism and function. We highlight how FABP5 regulates fatty acid uptake and oxidation, thus shaping the survival and function of different T cell subsets in the TME.

Gene expression is highly dynamic and plastic. We present a new immunological database, ImmuSort. Unlike other gene expression databases, ImmuSort provides a convenient way to view global differential gene expression data across thousands of experimental conditions in immune cells. It enables electronic sorting, which is a bioinformatics process to retrieve cell states associated with specific experimental conditions that are mainly based on gene expression intensity. A comparison of gene expression profiles reveals other applications, such as the evaluation of immune cell biomarkers and cell subsets, identification of cell specific and/or disease-associated genes or transcripts, comparison of gene expression in different transcript variants and probe set quality evaluation. A plasticity score is introduced to measure gene plasticity. Average rank and marker evaluation scores are used to evaluate biomarkers. The current version includes 31 human and 17 mouse immune cell groups, comprising 10,422 and 3,929 microarrays derived from public databases, respectively. A total of 20,283 human and 20,963 mouse genes are available to query in the database. Examples show the distinct advantages of the database. The database URL is http://immusort.bjmu.edu.cn/.

Background: Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms. Results: In this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain. Conclusions: In summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis.

Current gene co-expression databases and correlation networks do not support cell-specific analysis. Gene co-expression and expression correlation are subtly different phenomena, although both are likely to be functionally significant. Here, we report a new database, ImmuCo (http://immuco.bjmu.edu.cn), which is a cell-specific database that contains information about gene co-expression in immune cells, identifying co-expression and correlation between any two genes. The strength of co-expression of queried genes is indicated by signal values and detection calls, whereas expression correlation and strength are reflected by Pearson correlation coefficients. A scatter plot of the signal values is provided to directly illustrate the extent of co-expression and correlation. In addition, the database allows the analysis of cell-specific gene expression profile across multiple experimental conditions and can generate a list of genes that are highly correlated with the queried genes. Currently, the database covers 18 human cell groups and 10 mouse cell groups, including 20 283 human genes and 20 963 mouse genes. More than 8.6 × 10⁸ and 7.4 × 10⁸ probe set combinations are provided for querying each human and mouse cell group, respectively. Sample applications support the distinctive advantages of the database.