1
|
Guo L, Li S, Qian B, Wang Y, Duan R, Jiang W, Kang Y, Dou Y, Yang G, Shen L, Wang J, Liang T. Integrative omics analysis reveals relationships of genes with synthetic lethal interactions through a pan-cancer analysis. Comput Struct Biotechnol J 2020; 18:3243-54. [PMID: 33240468 DOI: 10.1016/j.csbj.2020.10.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 10/10/2020] [Accepted: 10/12/2020] [Indexed: 02/07/2023] Open
Abstract
Synthetic lethality is thought to play an important role in anticancer therapies. Herein, to understand the potential distributions and relationships between synthetic lethal interactions between genes, especially for pairs deriving from different sources, we performed an integrative analysis of genes at multiple molecular levels. Based on inter-species phylogenetic conservation of synthetic lethal interactions, gene pairs from yeast and humans were analyzed; a total of 37,588 candidate gene pairs containing 7,816 genes were collected. Of these, 49.74% of genes had 2–10 interactions, 22.93% were involved in hallmarks of cancer, and 21.61% were identified as core essential genes. Many genes were shown to have important biological roles via functional enrichment analysis, and 65 were identified as potentially crucial in the pathophysiology of cancer. Gene pairs with dysregulated expression patterns had higher prognostic values. Further screening based on mutation and expression levels showed that remaining gene pairs were mainly derived from human predicted or validated pairs, while most predicted pairs from yeast were filtered from analysis. Genes with synthetic lethality were further analyzed with their interactive microRNAs (miRNAs) at the isomiR level which have been widely studied as negatively regulatory molecules. The miRNA–mRNA interaction network revealed that many synthetic lethal genes contributed to the cell cycle (seven of 12 genes), cancer pathways (five of 12 genes), oocyte meiosis, the p53 signaling pathway, and hallmarks of cancer. Our study contributes to the understanding of synthetic lethal interactions and promotes the application of genetic interactions in further cancer precision medicine.
Collapse
Key Words
- ACC, adrenocortical carcinoma
- BLCA, bladder urothelial carcinoma
- BRCA, breast invasive carcinoma
- CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma
- CHOL, cholangiocarcinoma
- COAD, colon adenocarcinoma
- Cancer therapy
- DLBC, lymphoid neoplasm diffuse large B-cell lymphoma
- ESCA, esophageal carcinoma
- GBM, glioblastoma multiforme
- HNSC, head and neck squamous cell carcinoma
- KICH, kidney chromophobe
- KIRC, kidney renal clear cell carcinoma
- KIRP, kidney renal papillary cell carcinoma
- LAML, acute myeloid leukemia
- LGG, brain lower grade glioma
- LIHC, liver hepatocellular carcinoma
- LUAD, lung adenocarcinoma
- LUSC, lung squamous cell carcinoma
- MESO, mesothelioma
- OV, ovarian serous cystadenocarcinoma
- PAAD, pancreatic adenocarcinoma
- PCPG, pheochromocytoma and paraganglioma
- PRAD, prostate adenocarcinoma
- Pan-cancer analysis
- READ, rectum adenocarcinoma
- RNA interaction
- SARC, sarcoma
- SKCM, skin cutaneous melanoma
- STAD, stomach adenocarcinoma
- Synthetic lethality
- TGCT, testicular germ cell tumors
- THCA, thyroid carcinoma
- THYM, thymoma
- TSG, tumor suppressor gene
- UCEC, uterine corpus endometrial carcinoma
- UCS, uterine carcinosarcoma
- UVM, uveal melanoma
Collapse
|
2
|
Zhang Z, Li L, Li M, Wang X. The SARS-CoV-2 host cell receptor ACE2 correlates positively with immunotherapy response and is a potential protective factor for cancer progression. Comput Struct Biotechnol J. 2020;18:2438-2444. [PMID: 32905022 PMCID: PMC7462778 DOI: 10.1016/j.csbj.2020.08.024] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/23/2020] [Accepted: 08/26/2020] [Indexed: 02/06/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 29 million people and has caused more than 900,000 deaths worldwide as of September 14, 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. 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 13 cancer cohorts. We found that 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. ACE2 upregulation was associated with favorable survival in pan-cancer and in multiple individual cancer types. These results suggest that ACE2 is a potential protective factor for cancer progression. Our data may provide potential clinical implications for treating cancer patients infected with SARS-CoV-2.
Collapse
Key Words
- ACE2 expression
- ACE2, angiotensin-converting enzyme 2
- CESC, cervical squamous-cell carcinoma
- COAD, colon adenocarcinoma
- DFI, disease-free interval
- DSS, disease-specific survival
- EMT, epithelial-mesenchymal transition
- ESCA, esophageal carcinoma
- FDR, false discovery rate
- GO, gene ontology
- GSEA, gene set enrichment analysis
- HNSC, head and neck squamous cell carcinoma
- KIRC, kidney renal clear cell carcinoma
- KIRP, kidney renal papillary cell carcinoma
- LUAD, lung adenocarcinoma
- LUSC, lung squamous cell carcinoma
- OS, overall survival
- OV, ovarian carcinoma
- PAAD, pancreatic adenocarcinoma
- PFI, progression-free interval
- Pan-cancer
- SARS-CoV-2, severe acute respiratory syndrome coronavirus 2
- SKCM, skin cutaneous melanoma
- Survival prognosis
- TCGA, The Cancer Genome Atlas
- TF, transcription factor
- THYM, thymoma
- Tumor immunity and immunotherapy
- Tumor progression
- UCEC, uterine corpus endometrial carcinoma
- WGCNA, weighted gene co-expression network analysis
Collapse
|
3
|
Liang T, Han L, Guo L. Rewired functional regulatory networks among miRNA isoforms (isomiRs) from let-7 and miR-10 gene families in cancer. Comput Struct Biotechnol J 2020; 18:1238-1248. [PMID: 32542110 PMCID: PMC7280754 DOI: 10.1016/j.csbj.2020.05.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 05/05/2020] [Accepted: 05/05/2020] [Indexed: 01/05/2023] Open
Abstract
Classical microRNA (miRNA) has been so far believed as a single sequence, but it indeed contains multiple miRNA isoforms (isomiR) with various sequences and expression patterns. It is not clear whether these diverse isomiRs have potential relationships and whether they contribute to miRNA:mRNA interactions. Here, we aimed to reveal the potential evolutionary and functional relationships of multiple isomiRs based on let-7 and miR-10 gene families that are prone to clustering together on chromosomes. Multiple isomiRs within gene families showed similar functions to their canonical miRNAs, indicating selection of the predominant sequence. IsomiRs containing novel seed regions showed increased/decreased biological function depending on whether they had more/less specific target mRNAs than their annotated seed. Few gene ontology(GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were shared among the target genes of the annotated seeds and the novel seeds. Various let-7 isomiRs with novel seed regions may cause opposing drug responses despite the fact that they are generated from the same miRNA locus and have highly similar sequences. IsomiRs, especially the dominant isomiRs with shifted seeds, may disturb the coding-non-coding RNA regulatory network. These findings provide insight into the multiple isomiRs and isomiR-mediated control of gene expression in the pathogenesis of cancer.
Collapse
Key Words
- ACC, adrenocortical carcinoma
- BLCA, bladder urothelial carcinoma
- BRCA, breast invasive carcinoma
- CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma
- CHOL, cholangiocarcinoma
- COAD, colon adenocarcinoma
- ESCA, esophageal carcinoma
- Function
- GBM, glioblastoma multiforme
- HNSC, head and neck squamous cell carcinoma
- IsomiR
- KICH, kidney chromophobe
- KIRC, kidney renal clear cell carcinoma
- KIRP, kidney renal papillary cell carcinoma
- LAML, acute myeloid leukemia
- LGG, brain Lower grade glioma
- LIHC, liver hepatocellular carcinoma
- LUAD, lung adenocarcinoma
- LUSC, lung squamous cell carcinoma
- Let-7
- MESO, mesothelioma
- MicroRNA (miRNA)
- Network
- OV, ovarian serous cystadenocarcinoma
- PAAD, pancreatic adenocarcinoma
- PCPG, pheochromocytoma and paraganglioma
- PRAD, prostate adenocarcinoma
- READ, rectum adenocarcinoma
- SARC, sarcoma
- SKCM, skin cutaneous melanoma
- STAD, stomach adenocarcinoma
- TGCT, testicular germ cell tumors
- THCA, thyroid carcinoma
- THYM, thymoma
- TSG, tumor suppressor gene
- UCEC, uterine corpus endometrial carcinoma
- UCS, uterine carcinosarcoma
- UVM, uveal melanoma
- miR-10
Collapse
Affiliation(s)
- Tingming Liang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing 210023, China
| | - Leng Han
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030, USA
| | - Li Guo
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China
| |
Collapse
|
4
|
Li L, Feng Q, Wang X. PreMSIm: An R package for predicting microsatellite instability from the expression profiling of a gene panel in cancer. Comput Struct Biotechnol J 2020; 18:668-675. [PMID: 32257050 PMCID: PMC7113609 DOI: 10.1016/j.csbj.2020.03.007] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 03/06/2020] [Accepted: 03/08/2020] [Indexed: 01/10/2023] Open
Abstract
Microsatellite instability (MSI) is a genomic property of the cancers with defective DNA mismatch repair and is a useful marker for cancer diagnosis and treatment in diverse cancer types. In particular, MSI has been associated with the active immune checkpoint blockade therapy response in cancer. Most of computational methods for predicting MSI are based on DNA sequencing data and a few are based on mRNA expression data. Using the RNA-Seq pan-cancer datasets for three cancer cohorts (colon, gastric, and endometrial cancers) from The Cancer Genome Atlas (TCGA) program, we developed an algorithm (PreMSIm) for predicting MSI from the expression profiling of a 15-gene panel in cancer. We demonstrated that PreMSIm had high prediction performance in predicting MSI in most cases using both RNA-Seq and microarray gene expression datasets. Moreover, PreMSIm displayed superior or comparable performance versus other DNA or mRNA-based methods. We conclude that PreMSIm has the potential to provide an alternative approach for identifying MSI in cancer.
Collapse
Key Words
- ACC, adrenocortical carcinoma
- AUC, area under the curve
- Algorithm
- BLCA, bladder urothelial carcinoma
- BRCA, breast invasive carcinoma
- CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma
- CHOL, cholangiocarcinoma
- COAD, colon adenocarcinoma
- CV, cross validation
- Cancer
- Classification
- DLBC, lymphoid neoplasm diffuse large B-cell lymphoma
- ESCA, esophageal carcinoma
- GBM, glioblastoma multiforme
- GEO, Gene Expression Omnibus
- GO, gene ontology
- Gene expression profiling
- HNSC, head and neck squamous cell carcinoma
- KICH, kidney chromophobe
- KIRC, kidney renal clear cell carcinoma
- KIRP, kidney renal papillary cell carcinoma
- LAML, acute myeloid leukemia
- LGG, brain lower grade glioma
- LIHC, liver hepatocellular carcinoma
- LUAD, lung adenocarcinoma
- LUSC, lung squamous cell carcinoma
- MESO, mesothelioma
- MSI, microsatellite instability
- MSS, microsatellite stability
- Machine learning
- Microsatellite instability
- OV, ovarian serous cystadenocarcinoma
- PAAD, pancreatic adenocarcinoma
- PCPG, pheochromocytoma and paraganglioma
- PPI, protein-protein interaction
- PRAD, prostate adenocarcinoma
- READ, rectum adenocarcinoma
- RF, random forest
- ROC, receiver operating characteristic
- SARC, sarcoma
- SKCM, skin cutaneous melanoma
- STAD, stomach adenocarcinoma
- SVM, support vector machine
- TCGA, The Cancer Genome Atlas
- TGCT, testicular germ cell tumors
- THCA, thyroid carcinoma
- THYM, thymoma
- UCEC, uterine corpus endometrial carcinoma
- UCS, uterine carcinosarcoma
- UVM, uveal melanoma
- XGBoost, extreme gradient boosting
- k-NN, k-nearest neighbor
Collapse
Affiliation(s)
- Lin Li
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Big Data Research Institute, China Pharmaceutical University, Nanjing 211198, China
| | - Qiushi Feng
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Big Data Research Institute, China Pharmaceutical University, Nanjing 211198, China
| | - Xiaosheng Wang
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Big Data Research Institute, China Pharmaceutical University, Nanjing 211198, China
| |
Collapse
|