The spliceosome pathway activity correlates with reduced anti-tumor immunity and immunotherapy response, and unfavorable clinical outcomes in pan-cancer

Abstract Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 13 million people and has caused more than 570,000 deaths worldwide as of July 13, 2020. The SARS-CoV-2 human cell receptor ACE2 has recently received extensive attention for its role in SARS-CoV-2 infection. Many studies have also explored the association between ACE2 and cancer. However, a systemic investigation into associations between ACE2 and oncogenic pathways, tumor progression, and clinical outcomes in pan-cancer remains lacking. Methods: Using cancer genomics datasets from the Cancer Genome Atlas (TCGA) program, we performed computational analyses of associations between ACE2 expression and antitumor immunity, immunotherapy response, oncogenic pathways, tumor progression phenotypes, and clinical outcomes in 12 cancer cohorts. We also identified co-expression networks of ACE2 in cancer.Results: ACE2 upregulation was associated with increased antitumor immune signatures and PD-L1 expression, and favorable anti-PD-1/PD-L1/CTLA-4 immunotherapy response. ACE2 expression levels inversely correlated with the activity of cell cycle, mismatch repair, TGF-β, Wnt, VEGF, and Notch signaling pathways. Moreover, ACE2 expression levels had significant inverse correlations with tumor proliferation, stemness, and epithelial-mesenchymal transition (EMT). ACE2 upregulation was associated with favorable survival in pan-cancer and in multiple individual cancer types. Conclusions: ACE2 upregulation was associated with increased antitumor immunity and immunotherapy response, reduced tumor malignancy, and favorable survival in cancer, suggesting that ACE2 is a protective factor for cancer progression. Our data may provide potential clinical implications for treating cancer patients infected with SARS-CoV-2.

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Identification of pan-cancer Ras pathway activation with deep learning

Briefings in Bioinformatics ◽

10.1093/bib/bbaa258 ◽

2020 ◽

Author(s):

Xiangtao Li ◽

Shaochuan Li ◽

Yunhe Wang ◽

Shixiong Zhang ◽

Ka-Chun Wong

Keyword(s):

Deep Learning ◽

Superior Performance ◽

Recent Attempt ◽

Precision Oncology ◽

Pathway Activity ◽

Ras Pathway ◽

Cancer Data ◽

Pathway Activation ◽

Cancer Types ◽

Pan Cancer

Abstract The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several critical limitations there, such as high-dimensionality, data sparsity and model performance. Given the central importance and broad impact of precision oncology, we propose nature-inspired deep Ras activation pan-cancer (NatDRAP), a deep neural network (DNN) model, to address those restrictions for the identification of hidden responders. In this study, we develop the nature-inspired deep learning model that integrates bulk RNA sequencing, copy number and mutation data from PanCanAltas to detect pan-cancer Ras pathway activation. In NatDRAP, we propose to synergize the nature-inspired artificial bee colony algorithm with different gradient-based optimizers in one framework for optimizing DNNs in a collaborative manner. Multiple experiments were conducted on 33 different cancer types across PanCanAtlas. The experimental results demonstrate that the proposed NatDRAP can provide superior performance over other benchmark methods with strong robustness towards diagnosing RAS aberrant pathway activity across different cancer types. In addition, gene ontology enrichment and pathological analysis are conducted to reveal novel insights into the RAS aberrant pathway activity identification and characterization. NatDRAP is written in Python and available at https://github.com/lixt314/NatDRAP1.

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Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data

Briefings in Bioinformatics ◽

10.1093/bib/bby097 ◽

2018 ◽

Cited By ~ 7

Author(s):

Sangsoo Lim ◽

Sangseon Lee ◽

Inuk Jung ◽

Sungmin Rhee ◽

Sun Kim

Keyword(s):

Critical Evaluation ◽

Activity Measurement ◽

Pathway Activity ◽

Cancer Data ◽

Measurement Tools ◽

Pan Cancer

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Upregulation of ARNTL2 Predicts Poor Survival for Clear Cell Renal Cell Carcinoma and Explores its Associations with PD-L1 and Immune Infiltration

10.21203/rs.3.rs-351674/v1 ◽

2021 ◽

Author(s):

Song Wang ◽

Xueyou Ma ◽

Yufan Ying ◽

Jiazhu Sun ◽

Zitong Yang ◽

...

Keyword(s):

Overall Survival ◽

T Cell ◽

Tumor Immunity ◽

Cox Regression ◽

Immune Escape ◽

Survival Curve ◽

Regression Analyses ◽

Immune Infiltration ◽

Ccrcc Patient ◽

Pan Cancer

Abstract Background: Aryl hydrocarbon receptor nuclear translocator like 2 (ARNTL2) pertain to the PAS superfamily. Emerging evidences have demonstrated the carcinogenic roles of transcription factor ARNTL2 in human malignancies, while its roles in ccRCC remain elusive. We sought to explore the comprehensive roles of ARNTL2 in ccRCC and place major emphasis on its correlations with tumor immunity.Methods: The available data from GEO, TCGA and GTEx database were combined with our ccRCC patient tissues to verify the upregulation of ARNTL2, Kaplan–Meier survival curve analysis, Cox regression analyses (including univariate and multivariate) were utilized to evaluate the prognostic values of ARNTL2, the potential biological mechanisms of ARNTL2 were analyzed by using GSEA method. The ssGSEA and xCell algorithm were employed to assess the correlations of ARNTL2 expression with tumor immune microenvironment (TIME), The spearman analyses were applied to investigate the relationships between ARNTL2 expression and the tumor mutational burden (TMB), PD-L1 expression and microsatellite instability (MSI) in pan-cancer.Results: ARNTL2 was overexpressed in ccRCC and increased ARNTL2 expression strongly linked to advanced clinical stage and unfavorable overall survival. ARNTL2 was recognized as an independent prognostic marker through cox regression analyses. A prognostic nomogram was subsequently constructed to predict 1-, 3- and 5-year overall survival via integrating ARNTL2 expression with other clinicopathologic variables. GSEA analysis revealed that the focal adhesion, T cell receptor, cell cycle and JAK-STAT signaling pathway were remarkably enriched in high ARNTL2 samples. xCell analysis suggested that high expression of ARNTL2 exhibited an immune infiltration status similar to CD8+ inflamed ccRCC subtype, which characterized by a high infiltration level of CD8+ T cell and elevated expression level of the immune escape biomarkers such as PD-L1, PD-L2, PD1 and CTLA4. Further pan-cancer analysis indicated that ARNTL2 was tightly linked to TMB, MSI, PD-L1 expression, tumor immunity and poor OS in diverse cancer types.Conclusions: ARNTL2 is an independent adverse predictor of ccRCC patient survival and tightly linked to TMB, MSI, PD-L1 expression and immunity.

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The pan‐cancer mutational landscape of the PPAR pathway reveals universal patterns of dysregulated metabolism and interactions with tumor immunity and hypoxia

Annals of the New York Academy of Sciences ◽

10.1111/nyas.14170 ◽

2019 ◽

Vol 1448 (1) ◽

pp. 65-82 ◽

Cited By ~ 5

Author(s):

Wai Hoong Chang ◽

Alvina G. Lai

Keyword(s):

Tumor Immunity ◽

Mutational Landscape ◽

Pan Cancer ◽

Ppar Pathway

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Optimized detection of somatic allelic imbalances specific for homologous recombination deficiency improves the prediction of clinical outcomes in ovarian cancer

10.1101/2021.08.19.456809 ◽

2021 ◽

Author(s):

Fernando Ramon Perez-Villatoro ◽

Julia Casado ◽

Anniina Farkkila

Keyword(s):

Ovarian Cancer ◽

Homologous Recombination ◽

Clinical Outcomes ◽

Repair Deficiency ◽

Improve Patient Selection ◽

Dna Repair Deficiency ◽

Tumor Types ◽

Improve Patient ◽

Pan Cancer

Specific patterns of genomic allelic imbalances (AIs) have been associated with Homologous recombination DNA-repair deficiency (HRD). We performed a systematic pan-cancer characterization of AIs across tumor types, revealing unique patterns in ovarian cancer. Using machine learning on a multi-omics dataset, we generated an optimized algorithm to detect HRD in ovarian cancer (ovaHRDscar). ovaHRDscar improved the prediction of clinical outcomes in three independent validation cohorts (PCAWG, HERCULES, TERVA). Characterization of 98 spatiotemporally distinct tumor samples indicated ovary/adnex as the preferred site to assess HRD. In conclusion, ovaHRDscar improves the detection of HRD in ovarian cancer with the premise to improve patient selection for HR-targeted therapies.

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A Pan-Cancer Analysis of SLC12A5 Reveals Its Correlations with Tumor Immunity

Disease Markers ◽

10.1155/2021/3062606 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yi Jiang ◽

Hong-li Liao ◽

Li-ya Chen

Keyword(s):

Tumor Immunity ◽

Tumor Stage ◽

Mechanisms Of Action ◽

Solute Carrier Family ◽

Tumor Mutation Burden ◽

Human Cancers ◽

Mutation Burden ◽

Solute Carrier ◽

Pan Cancer ◽

Disease Specific

Background. Solute carrier family 12 member 5 (SLC12A5) has been reported to play an oncogenic role in certain malignancies. Its prognostic roles and immune mechanisms of action in human cancers, however, remain largely unknown. Methods. Data derived from TCGA, GEPIA, and TIMER databases were utilized to delve into the expressing patterns, prognostic values, clinical significances, and tumor immunity of SLC12A5 in tumors. Additionally, the association of SLC12A5 expressions with tumor mutation burden (TMB), methyltransferases, and mismatch repairs (MMRs) was also analyzed. Results. Herein, we observed that SLC12A5 was significantly overexpressed in various malignancies, and SLC12A5 levels correlated with overall survival, disease-specific survival, and tumor stage of certain cancers. Furthermore, we noticed that SLC12A5 was distinctly associated with methyltransferases, mismatch repair proteins, TMB, and MSI in human cancers. Conclusions. SLC12A5 may act as a potential prognostic and immunological biomarker and therapeutic target for human cancers.

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