Characterization of the Survival Influential Genes in Carcinogenesis

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.

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Pan-Cancer Molecular Patterns and Biological Implications Associated with a Tumor-Specific Molecular Signature

Cells ◽

10.3390/cells10010045 ◽

2020 ◽

Vol 10 (1) ◽

pp. 45

Author(s):

Darío Rocha ◽

Iris A. García ◽

Aldana González Montoro ◽

Andrea Llera ◽

Laura Prato ◽

...

Keyword(s):

Cell Cycle ◽

Cell Cycle Progression ◽

Uncertainty Assessment ◽

Cell Cycle Checkpoints ◽

Molecular Signature ◽

Cancer Subtypes ◽

United States Food ◽

Cancer Types ◽

Molecular Patterns ◽

Pan Cancer

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications.

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The Pan-Cancer Landscape of Prognostic Germline Variants in 10,582 Patients

10.1101/19010264 ◽

2019 ◽

Author(s):

Ajay Chatrath ◽

Roza Przanowska ◽

Shashi Kiran ◽

Zhangli Su ◽

Shekhar Saha ◽

...

Keyword(s):

Patient Outcome ◽

Cox Regression ◽

Clinical Information ◽

Tumor Aggressiveness ◽

Sequencing Data ◽

Germline Variants ◽

Outcome Information ◽

Germline Variation ◽

Patient Prognosis ◽

Pan Cancer

AbstractWhile clinical data provides physicians with information about patient prognosis, genomic data can further improve these predictions. We analyzed sequencing data from over 10,000 cancer patients and identified hundreds of prognostic germline variants using multivariate Cox regression models. These variants provide information about patient outcomes beyond clinical information currently in use and may augment clinical decisions based on expected tumor aggressiveness. Molecularly, at least twelve of the germline variants are likely associated with patient outcome through perturbation of protein structure and at least five through association with gene expression differences. About half of these germline variants are in previously reported tumor suppressors or oncogenes, with the other half pointing to loci of previously unstudied genes in the literature that should be further investigated for roles in cancers. Our results suggest that germline variation contributes to tumor progression across most cancers and contains patient outcome information not captured by clinical factors.

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A pan-cancer analysis of prognostic genes

PeerJ ◽

10.7717/peerj.1499 ◽

2016 ◽

Vol 3 ◽

pp. e1499 ◽

Cited By ~ 13

Author(s):

Jordan Anaya ◽

Brian Reon ◽

Wei-Min Chen ◽

Stefan Bekiranov ◽

Anindya Dutta

Keyword(s):

Microarray Data ◽

The Cancer Genome Atlas ◽

Rna Seq ◽

Cancer Pathogenesis ◽

Gene Sets ◽

Protective Genes ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Measure Of Association ◽

Pan Cancer

Numerous studies have identified prognostic genes in individual cancers, but a thorough pan-cancer analysis has not been performed. In addition, previous studies have mostly used microarray data instead of RNA-SEQ, and have not published comprehensive lists of associations with survival. Using recently available RNA-SEQ and clinical data from The Cancer Genome Atlas for 6,495 patients, we have investigated every annotated and expressed gene’s association with survival across 16 cancer types. The most statistically significant harmful and protective genes were not shared across cancers, but were enriched in distinct gene sets which were shared across certain groups of cancers. These groups of cancers were independently recapitulated by both unsupervised clustering of Cox coefficients (a measure of association with survival) for individual genes, and for gene programs. This analysis has revealed unappreciated commonalities among cancers which may provide insights into cancer pathogenesis and rationales for co-opting treatments between cancers.

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CD96 Correlates With Immune Infiltration and Impacts Patient Prognosis: A Pan-Cancer Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.634617 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wenrui Ye ◽

Cong Luo ◽

Fangkun Liu ◽

Zhixiong Liu ◽

Fenghua Chen

Keyword(s):

T Cells ◽

Nk Cells ◽

Protective Factor ◽

Immune Checkpoints ◽

Lower Grade ◽

Immune Infiltration ◽

Potential Biomarker ◽

Cancer Types ◽

Patient Prognosis ◽

Pan Cancer

BackgroundImmunotherapy has significantly improved patient outcomes, but encountered obstacles recently. CD96, a novel immune checkpoint expressed on T cells and natural killer (NK) cells, is essential for regulating immune functions. However, how CD96 correlating with immune infiltration and patient prognosis in pan-cancer remains unclear.MethodsHPA, TCGA, GEO, GTEx, Oncomine, TIMER2.0, PrognoScan, Linkedomics, Metascape, and GEPIA2 databases were used to analyze CD96 in cancers. Visualization of data was mostly achieved by R language, version 4.0.2.ResultsIn general, CD96 was differentially expressed between most cancer and adjacent normal tissues. CD96 significantly impacted the prognosis of diverse cancers. Especially, high CD96 expression was associated with poorer overall survival (OS) and disease-specific survival (DSS) in the TCGA lower grade glioma (LGG) cohort (OS, HR = 2.18, 95% CI = 1.79–2.66, P < 0.001). The opposite association was significantly observed in skin cutaneous melanoma (SKCM) cohort (OS, HR = 0.96, 95% CI = 0.94–0.98, P < 0.001). Notably, SKCM samples demonstrated the highest CD96 mutation frequency among all cancer types. Furthermore, in most cancers, CD96 expression level was significantly correlated with expression levels of recognized immune checkpoints and abundance of multiple immune infiltrates including CD8+ T cells, dendric cells (DCs), macrophages, monocytes, NK cells, neutrophils, regulatory T cells (Tregs), and follicular helper T cells (Tfh). CD96 was identified as a risk factor, protective factor, and irrelevant variable in LGG, SKCM and adrenocortical carcinoma (ACC), respectively. CD96 related genes were involved in negative regulation of leukocyte in LGG, however, involved in multiple positive immune processes in SKCM. Furthermore, CD96 was significantly associated with particular immune marker subsets. Importantly, it strongly correlated with markers of type 1 helper T cell (Th1) in SKCM, but not in LGG or ACC either.ConclusionsCD96 participates in diverse immune responses, governs immune cell infiltration, and impacts malignant properties of various cancer types, thus standing as a potential biomarker for determining patient prognosis and immune infiltration in multiple cancers, especially in glioma and melanoma.

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Integrative Analysis the characterization of peroxiredoxins in pan-cancer

10.1101/2020.10.09.334086 ◽

2020 ◽

Author(s):

Lei Gao ◽

Jialin Meng ◽

Chuang Yue ◽

Xingyu Wu ◽

Quanxin Su ◽

...

Keyword(s):

Expression Profiles ◽

Genetic Alterations ◽

Comprehensive Analysis ◽

Biological Pathways ◽

Cancer Cell Proliferation ◽

Biological Functions ◽

Systematic Analysis ◽

Cancer Types ◽

Pan Cancer

AbstractPeroxiredoxins (PRDXs) are antioxidant enzymes protein family members that involves the process of several biological functions, such as differentiation, cell growth. Considerable evidence demonstrates that PRDXs play critical roles in the occurrence and development of carcinomas. However, a systematic analysis of PRDXs in cancers is deficiency. Therefore, we perform a comprehensive analysis of PRDXs in 33 cancer types including mRNA expression profiles, genetic alterations, methylation, prognostic values, potential biological pathways and target drugs. Moreover, we validated that PRDX6 could regulate cancer cell proliferation via JAK2-STAT3 pathway and involve into the process of cell cycle in bladder cancer.

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Persistent cell proliferation signals correlates with increased glycolysis in tumor hypoxia microenvironment across cancer types

10.1101/2020.03.16.993311 ◽

2020 ◽

Author(s):

Jinfen Wei ◽

Kaitang Huang ◽

Meiling Hu ◽

Zixi Chen ◽

Yunmeng Bai ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Cell Proliferation ◽

Tumor Cell ◽

Gene Expression Signature ◽

Gene Set ◽

Expression Signature ◽

Molecular Features ◽

Cancer Types ◽

Pan Cancer

AbstractBackgroundAltered metabolism is a hallmark of cancer and glycolysis is one of the important factors promoting tumor development. Given that the absence of multi-sample big data research about glycolysis, the molecular mechanisms involved in glycolysis or the relationships between glycolysis and tumor microenvironment are not fully studied. Thus, a more comprehensive approach in a pan-cancer landscape may be needed.MethodsHere, we develop a computational pipeline to study multi-omics molecular features defining glycolysis activity and identify molecular alterations that correlate with glycolysis. We apply a 22-gene expression signature to define the glycolysis activity landscape and verify the robustness using clinically defined glycolysis samples from several previous studies. Based on gene expression signature, we classify about 5552 of 9229 tumor samples into glycolysis score-high and score-low groups across 25 cancer types from The Cancer Genome Atlas (TCGA) and demonstrate their prognostic associations. Moreover, using genomes and transcriptome data, we characterize the association of copy-number aberrations (CNAs), somatic single-nucleotide variants (SNVs) and hypoxia signature with glycolysis activity.FindingsGene set variation analysis (GSVA) score by gene set expression was verified robustly to represent glycolytic activity and highly glycolytic tumors presented a poor overall survival in some cancer types. Then, we identified various types of molecular features promoting tumor cell proliferation were associated with glycolysis activity. Our study showed that TCA cycle and respiration electron transport were active in glycolysis-high tumors, indicating glycolysis was not a symptom of impaired oxidative metabolism. The glycolytic score significantly correlated with hypoxia score across all cancer types. Glycolysis score was also associated with elevated genomic instability. In all tumor types, high glycolysis tumors exhibited characteristic driver genes altered by CNAs identified multiple oncogenes and tumor suppressors. We observed widespread glycolysis-associated dysregulation of mRNA across cancers and screened out HSPA8 and P4HA1 as the potential modulating factor to glycolysis. Besides, the expression of genes encoding glycolytic enzymes positively correlated with genes in cell cycle.InterpretationThis is the first study to identify gene expression signatures that reflect glycolysis activity, which can be easily applied to large numbers of patient samples. Our analysis establishes a computational framework for characterizing glycolysis activity using gene expression data and defines correlation of glycolysis with the hypoxia microenvironment, tumor cell cycle and proliferation at a pan-cancer landscape. The findings suggest that the mechanisms whereby hypoxia influence glycolysis are likely multifactorial. Our finding is significant not just in demonstrating definition value for glycolysis but also in providing a comprehensive molecular-level understanding of glycolysis and suggesting a framework to guide combination therapy that may block the glycolysis pathway to control tumor growth in hypoxia microenvironment.

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Hiding in the dark: pan-cancer characterization of expression and clinical relevance of CD40 to immune checkpoint blockade therapy

Molecular Cancer ◽

10.1186/s12943-021-01442-3 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Chi Yan ◽

Ann Richmond

Keyword(s):

Immune Checkpoint ◽

Tumor Response ◽

Immune Checkpoint Blockade ◽

Checkpoint Blockade ◽

Type I ◽

List Type ◽

Cancer Types ◽

Pan Cancer ◽

Cancer Bioinformatics

Highlights CD40 expression correlates with the type I anti-tumor response and better survival. Pan-cancer bioinformatics characterization reveals reduced CD40 expression in 11 cancer types, including RASmut melanoma compared to nevi. RAS mutation correlates with reduced CD40 expression in malignant melanoma. CD40 expression is associated with better response to immune checkpoint blockade therapy in melanoma.

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Pan-Cancer Integrated Analysis of HSF2 Expression, Prognostic Value and Potential Implications for Cancer Immunity

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.789703 ◽

2022 ◽

Vol 8 ◽

Author(s):

Fei Chen ◽

Yumei Fan ◽

Xiaopeng Liu ◽

Jianhua Zhang ◽

Yanan Shang ◽

...

Keyword(s):

Dna Methylation ◽

Signaling Pathways ◽

Immune Cells ◽

Cox Regression ◽

Prognostic Significance ◽

Clinicopathological Parameters ◽

Immune Infiltration ◽

Cancer Types ◽

The Relationship ◽

Pan Cancer

Heat shock factor 2 (HSF2), a transcription factor, plays significant roles in corticogenesis and spermatogenesis by regulating various target genes and signaling pathways. However, its expression, clinical significance and correlation with tumor-infiltrating immune cells across cancers have rarely been explored. In the present study, we comprehensively investigated the expression dysregulation and prognostic significance of HSF2, and the relationship with clinicopathological parameters and immune infiltration across cancers. The mRNA expression status of HSF2 was analyzed by TCGA, GTEx, and CCLE. Kaplan-Meier analysis and Cox regression were applied to explore the prognostic significance of HSF2 in different cancers. The relationship between HSF2 expression and DNA methylation, immune infiltration of different immune cells, immune checkpoints, tumor mutation burden (TMB), and microsatellite instability (MSI) were analyzed using data directly from the TCGA database. HSF2 expression was dysregulated in the human pan-cancer dataset. High expression of HSF2 was associated with poor overall survival (OS) in BRCA, KIRP, LIHC, and MESO but correlated with favorable OS in LAML, KIRC, and PAAD. The results of Cox regression and nomogram analyses revealed that HSF2 was an independent factor for KIRP, ACC, and LIHC prognosis. GO, KEGG, and GSEA results indicated that HSF2 was involved in various oncogenesis- and immunity-related signaling pathways. HSF2 expression was associated with TMB in 9 cancer types and associated with MSI in 5 cancer types, while there was a correlation between HSF2 expression and DNA methylation in 27 types of cancer. Additionally, HSF2 expression was correlated with immune cell infiltration, immune checkpoint genes, and the tumor immune microenvironment in various cancers, indicating that HSF2 could be a potential therapeutic target for immunotherapy. Our findings revealed the important roles of HSF2 across different cancer types.

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Aneuploidy increases resistance to chemotherapeutics by antagonizing cell division

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2009506117 ◽

2020 ◽

Vol 117 (48) ◽

pp. 30566-30576 ◽

Cited By ~ 1

Author(s):

John Michael Replogle ◽

Wen Zhou ◽

Adrianna E. Amaro ◽

James M. McFarland ◽

Mariana Villalobos-Ortiz ◽

...

Keyword(s):

Cell Cycle ◽

Drug Resistance ◽

Detrimental Effect ◽

Cancer Cell Line ◽

Chemotherapy Treatment ◽

Single Chromosome ◽

Cancer Types ◽

Patient Prognosis ◽

Gains And Losses ◽

Increase Drug Resistance

Aneuploidy, defined as whole chromosome gains and losses, is associated with poor patient prognosis in many cancer types. However, the condition causes cellular stress and cell cycle delays, foremost in G1 and S phase. Here, we investigate how aneuploidy causes both slow proliferation and poor disease outcome. We test the hypothesis that aneuploidy brings about resistance to chemotherapies because of a general feature of the aneuploid condition—G1 delays. We show that single chromosome gains lead to increased resistance to the frontline chemotherapeutics cisplatin and paclitaxel. Furthermore, G1 cell cycle delays are sufficient to increase chemotherapeutic resistance in euploid cells. Mechanistically, G1 delays increase drug resistance to cisplatin and paclitaxel by reducing their ability to damage DNA and microtubules, respectively. Finally, we show that our findings are clinically relevant. Aneuploidy correlates with slowed proliferation and drug resistance in the Cancer Cell Line Encyclopedia (CCLE) dataset. We conclude that a general and seemingly detrimental effect of aneuploidy, slowed proliferation, provides a selective benefit to cancer cells during chemotherapy treatment.

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A pan-cancer analysis of prognostic genes

10.1101/030924 ◽

2015 ◽

Author(s):

Jordan Anaya ◽

Brian J. Reon ◽

Wei-Min Chen ◽

Stefan Bekiranov ◽

Anindya Dutta

Keyword(s):

Microarray Data ◽

The Cancer Genome Atlas ◽

Rna Seq ◽

Cancer Pathogenesis ◽

Gene Sets ◽

Protective Genes ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Measure Of Association ◽

Pan Cancer

AbstractNumerous studies have identified prognostic genes in individual cancers, but a thorough pan-cancer analysis has not been performed. In addition, previous studies have mostly used microarray data instead of RNA-SEQ, and have not published comprehensive lists of associations with survival. Using recently available RNA-SEQ and clinical data from the The Cancer Genome Atlas for 6,495 patients, we have investigated every annotated and expressed gene’s association with survival across 16 cancer types. The most statistically significant harmful and protective genes were not shared across cancers, but were enriched in distinct gene sets which were shared across certain groups of cancers. These groups of cancers were independently reconstructed by unsupervised clustering of Cox coefficients (a measure of association with survival) for individual genes or for gene programs. This analysis has revealed unappreciated commonalities among cancers which may provide insights into cancer pathogenesis and rationales for co-opting treatments between cancers.

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