Systematic pan-cancer landscape identifies CARM1 as a potential prognostic and immunological biomarker

Background Belonging to the protein arginine methyltransferase (PRMT) family, the enzyme encoded by coactivator associated arginine methyltransferase 1 (CARM1) catalyzes the methylation of protein arginine residues, especially acts on histones and other chromatin related proteins, which is essential in regulating gene expression. Beyond its well-established involvement in the regulation of transcription, recent studies have revealed a novel role of CARM1 in tumorigenesis and development, but there is still a lack of systematic understanding of CARM1 in human cancers. An integrated analysis of CARM1 in pan-cancer may contribute to further explore its prognostic value and potential immunological function in tumor therapy. Results Based on systematic analysis of data in multiple databases, we firstly verified that CARM1 is highly expressed in most tumors compared with corresponding normal tissues, and is bound up with poor prognosis in some tumors. Subsequently, relevance between CARM1 expression level and tumor immune microenvironment is analyzed from the perspectives of tumor mutation burden, microsatellite instability, mismatch repair genes, methyltransferases genes, immune checkpoint genes and immune cells infiltration, indicating a potential relationship between CARM1 expression and tumor microenvironment. A gene enrichment analysis followed shortly, which implied that the role of CARM1 in tumor pathogenesis may be related to transcriptional imbalance and viral carcinogenesis. Conclusions Our first comprehensive bioinformatics analysis provides a broad molecular perspective on the role of CARM1 in various tumors, highlights its value in clinical prognosis and potential association with tumor immune microenvironment, which may furnish an immune based antitumor strategy to provide a reference for more accurate and personalized immunotherapy in the future.

Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.

Transcription Elongation Factor A (SII) -Like (TCEAL) Gene Family Member-TCEAL2: A Novel Prognostic Marker in Pan-Cancer

Background: Cancer is the leading cause of death in the world. The mechanism is not fully elucidated and the therapeutic effect is also unsatisfactory. In our study, we aim to find new target gene in pan-cancer.Methods: Differentially expressed genes (DEGs) was screened out in various types of cancers from GEO database. The expression of DEG (TCEAL2) in tumor cell lines, normal tissues and tumor tissues was calculated. Then the clinical characteristics, DNA methylation, tumor infiltration and gene enrichment of TCEAL2 was studied. Results: TCEAL2 expressions were down-regulated in most cancers. Its expression and methylation were positively or negatively associated with prognosis in different cancers. The tumor infiltration results revealed that TCEAL2 was significantly related with many immune cells especially NK cells and immune-related genes in majority cancers. Furthermore, tau protein and tubulin binding were involved in the molecular function mechanisms of TCEAL2. Conclusion: TCEAL2 may be a novel prognostic marker in different cancers and may affect tumor through immune infiltration.

Decreased Expression of ACADSB Predicts Poor Prognosis in Clear Cell Renal Cell Carcinoma

BackgroundPrevious reports have shown that short/branched chain acyl-CoA dehydrogenase (ACADSB) plays an important role in glioma, but its role in clear cell renal carcinoma (ccRCC) has not been reported.MethodsThe TIMER and UALCAN databases were used for pan-cancer analysis. RNA sequencing and microarray data of patients with ccRCC were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus database. The differential expression of ACADSB in ccRCC and normal kidney tissues was tested. Correlations between ACADSB expression and clinicopathological parameters were assessed using the Wilcoxon test. The influences of ACADSB expression and clinicopathological parameters on overall survival were assessed using Cox proportional hazards models. Gene set enrichment analysis (GSEA) was performed to explore the associated gene sets enriched in different ACADSB expression phenotypes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on genes with similar expression patterns to ACADSB. Correlations between ACADSB and ferroptosis-related genes were assessed using Spearman’s correlation analysis.ResultsPan-cancer analysis revealed that ACADSB is down-regulated in multiple cancers, and decreased expression of ACADSB correlates with poor prognosis in certain types of cancer. Differential expression analyses revealed that ACADSB was down-regulated in ccRCC, indicating that ACADSB expression could be a single significant parameter to discriminate between normal and tumor tissues. Clinical association analysis indicated that decreased ACADSB expression was associated with high tumor stage and grade. The Cox regression model indicated that low ACADSB expression was an independent risk factor for the overall survival of patients with ccRCC. GSEA showed that 10 gene sets, including fatty acid (FA) metabolism, were differentially enriched in the ACADSB high expression phenotype. GO and KEGG pathway enrichment analysis revealed that ACADSB-related genes were significantly enriched in categories related to FA metabolism, branched-chain amino acid (BCAA) metabolism, and iron regulation. Spearman’s correlation analysis suggested that the expression of ACADSB was positively correlated with the expression of ferroptosis driver genes.ConclusionsACADSB showed good diagnostic and prognostic abilities for ccRCC. The downregulation of ACADSB might promote tumorigenesis and tumor progression by inhibiting FA catabolism, BCAA catabolism, and ferroptosis in ccRCC.

Pan-Cancer Analysis Shows Enrichment of Macrophages, Overexpression of Checkpoint Molecules, Inhibitory Cytokines, and Immune Exhaustion Signatures in EMT-High Tumors

Epithelial–mesenchymal transition (EMT) is a highly dynamic process that occurs under normal circumstances; however, EMT is also known to play a central role in tumor progression and metastasis. Furthermore, role of tumor immune microenvironment (TIME) in shaping anticancer immunity and inducing the EMT is also well recognized. Understanding the key features of EMT is critical for the development of effective therapeutic interventions. Given the central role of EMT in immune escape and cancer progression and treatment, we have carried out a pan-cancer TIME analysis of The Cancer Genome Atlas (TCGA) dataset in context to EMT. We have analyzed infiltration of various immune cells, expression of multiple checkpoint molecules and cytokines, and inflammatory and immune exhaustion gene signatures in 22 cancer types from TCGA dataset. A total of 16 cancer types showed a significantly increased (p < 0.001) infiltration of macrophages in EMT-high tumors (mesenchymal samples). Furthermore, out of the 17 checkpoint molecules we analyzed, 11 showed a significant overexpression (p < 0.001) in EMT-high samples of at least 10 cancer types. Analysis of cytokines showed significant enrichment of immunosuppressive cytokines—TGFB1 and IL10—in the EMT-high group of almost all cancer types. Analysis of various gene signatures showed enrichment of inflammation, exhausted CD8+ T cells, and activated stroma signatures in EMT-high tumors. In summary, our pan-cancer EMT analysis of TCGA dataset shows that the TIME of EMT-high tumors is highly immunosuppressive compared to the EMT-low (epithelial) tumors. The distinctive features of EMT-high tumors are as follows: (i) the enrichment of tumor-associated macrophages, (ii) overexpression of immune checkpoint molecules, (iii) upregulation of immune inhibitory cytokines TGFB1 and IL10, and (iv) enrichment of inflammatory and exhausted CD8+ T-cell signatures. Our study shows that TIMEs of different EMT groups differ significantly, and this would pave the way for future studies analyzing and targeting the TIME regulators for anticancer immunotherapy.

Pan-Cancer Analysis Revealed SRSF9 as a New Biomarker for Prognosis and Immunotherapy

Background. Serine/arginine-rich splicing factor 9 (SRSF9) is one of the members of SRSF gene family and related to the tumorigenesis and the progression of tumor. However, whether SRSF9 has a crucial role across pan-cancer is still unknown. Methods. In this study, we used public databases, such as The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx), to analyze SRSF9 expression level among tumor and normal cells. Survival analysis, K-M plotter, and PrognoScan were used to analyze the prognosis value of SRSF9, regarding to overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Moreover, we performed the correlation between SRSF9 and clinical characteristics (including the outcome of prognosis), as well as molecular events of tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint gene, tumor microenvironment (TME), immune infiltrating cells, mismatch repair (MMR) genes, m6A genes, DNA methyltransferases, and neoantigen with bioinformatics methods and TISIDB, TIMER, and Sangerbox websites. Results. In general, SRSF9 expression was upregulated in most cancers, such as BLCA, CHOL, and UCEC, which SRSF9 was associated with short survival and severe progression. In COAD, STAD, and UCEC, SRSF9 expression was positively related to both TMB and MSI. In BRCA, BLCA, ESCA, GBM, HNSC, LUSC, LUAD, OV, PRAD, TGCT, THCA, and UCEC, both immune score and stomal score showed a negative relationship with SRSF9 expression. Immune score showed a positive relationship with SRSF9 expression in LGG. SRSF9 expression had a significant and positive correlation with six types of immune infiltration cells in LGG, KIRC, LIHC, PCPG, PRAD, SKCM, THCA, and THYM, except in LUSC. In LIHC, SRSF9 was highly significant correlated with most immune checkpoint genes. For neoantigens, correlation between SRSF9 and the quantity of neoantigens was significantly positive in some cancer types. SRSF9 was also correlated with MMR genes, m6A genes, and DNA methyltransferases. In the 33 cancer types, gene set enrichment analysis (GSEA) demonstrated that SRSF9 was correlated with multiple functions and signaling pathways. Conclusion. These findings demonstrated that SRSF9 may be a new biomarker for the prognosis and immunotherapy in various cancers. As a result, it will be beneficial to provide new therapies for cancer patients, thereby improving the treatment and prognosis of cancer patients.

Multi-cancer classification; an analysis of neural network complexity

Background: Cancer identification is generally framed as binary classification, normally discrimination of a control group from a single cancer group. However, such models lack any cancer-specific information, as they are only trained on one cancer type. The models fail to account for competing cancer risks. For example, an ostensibly healthy individual may have any number of different cancer types, and a tumor may originate from one of several primary sites. Pan-cancer evaluation requires a model trained on multiple cancer types, and controls, simultaneously, so that a physician can be directed to the correct area of the body for further testing. Methods: We introduce novel neural network models to address multi-cancer classification problems across several data types commonly applied in cancer prediction, including circulating miRNA expression, protein, and mRNA. In particular, we present an analysis of neural network depth and complexity, and investigate how this relates to classification performance. Comparisons of our models with state-of-the-art neural networks from the literature are also presented. Results: Our analysis evidences that shallow, feed-forward neural net architectures offer greater performance when compared to more complex deep feed-forward, Convolutional Neural Network (CNN), and Graph CNN (GCNN) architectures considered in the literature. Conclusion: The results show that multiple cancers and controls can be classified accurately using the proposed models, across a range of expression technologies in cancer prediction. Impact: This study addresses the important problem of pan-cancer classification, which is often overlooked in the literature. The promising results highlight the urgency for further research.

A Pan-cancer Analysis of Thioredoxin-interacting Protein as an Immunological and Prognostic Biomarker

BackgroundThe critical role of thioredoxin-interacting protein (TXNIP) in cellular sulfhydryl redox homeostasis and inflammasome activation is already widely known, however, no pan-cancer analysis is currently available. MethodsWe thus first explored the potential roles of TXNIP across thirty-three tumors mainly based on The Cancer Genome Atlas and Gene Expression Omnibus datasets. ResultsTXNIP is lowly expressed in most cancers, and distinct associations exist between TXNIP expression and the prognosis of tumor patients. TXNIP expression was associated with tumor mutational burden, microsatellite instability, mismatch repair genes, tumor infiltrating immune cell abundance as well as cancer-associated fibroblasts. Moreover, ubiquitin mediated proteolysis, protein post-translational modification and other related pathways were involved in the functional mechanisms of TXNIP. ConclusionsOur first pan-cancer study offers a relatively comprehensive understanding of the carcinostatic roles of TXNIP across different tumors. And this molecule may be considered as a potential immunological and prognostic biomarker.