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Zeng Z, Gao Y, Li J, Zhang G, Sun S, Wu Q, Gong Y, Xie C. Violations of proportional hazard assumption in Cox regression model of transcriptomic data in TCGA pan-cancer cohorts. Comput Struct Biotechnol J 2022; 20:496-507. [PMID: 35070171 PMCID: PMC8762368 DOI: 10.1016/j.csbj.2022.01.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 07/12/2021] [Revised: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 11/29/2022] Open
Abstract
Background Cox proportional hazard regression (CPH) model relies on the proportional hazard (PH) assumption: the hazard of variables is independent of time. CPH has been widely used to identify prognostic markers of the transcriptome. However, the comprehensive investigation on PH assumption in transcriptomic data has lacked. Results The whole transcriptomic data of the 9,056 patients from 32 cohorts of The Cancer Genome Atlas and the 3 lung cancer cohorts from Gene Expression Omnibus were collected to construct CPH model for each gene separately for fitting the overall survival. An average of 8.5% gene CPH models violated the PH assumption in TCGA pan-cancer cohorts. In the gene interaction networks, both hub and non-hub genes in CPH models were likely to have non-proportional hazards. Violations of PH assumption for the same gene models were not consistent in 5 non-small cell lung cancer datasets (all kappa coefficients < 0.2), indicating that the non-proportionality of gene CPH models depended on the datasets. Furthermore, the introduction of log(t) or sqrt(t) time-functions into CPH improved the performance of gene models on overall survival fitting in most tumors. The time-dependent CPH changed the significance of log hazard ratio of the 31.9% gene variables. Conclusions Our analysis resulted that non-proportional hazards should not be ignored in transcriptomic data. Introducing time interaction term ameliorated performance and interpretability of non-proportional hazards of transcriptome data in CPH.
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Key Words
- ACC, Adrenocortical carcinoma
- AIC, Akaike information criterion
- BLCA, Bladder Urothelial Carcinoma
- BRCA, Breast invasive carcinoma
- CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma
- CHOL, Cholangiocarcinoma
- COAD, Colon adenocarcinoma
- CON, Concordance regression
- CPH, Cox proportional hazard regression
- Cox regression
- DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma
- ESCA, Esophageal carcinoma
- GBM, Glioblastoma multiforme
- GEO, Gene Expression Omnibus
- GO, Gene Ontology
- HNSC, Head and Neck squamous cell carcinoma
- KICH, Kidney Chromophobe
- KIRC, Kidney renal clear cell carcinoma
- KIRP, Kidney renal papillary cell carcinoma
- LGG, Brain Lower Grade Glioma
- LIHC, Liver hepatocellular carcinoma
- LUAD, Lung adenocarcinoma
- LUSC, Lung squamous cell carcinoma
- MESO, Mesothelioma
- OS, overall survival
- OV, Ovarian serous cystadenocarcinoma
- PAAD, Pancreatic adenocarcinoma
- PCPG, Pheochromocytoma and Paraganglioma
- PH, proportional hazard
- PRAD, Prostate adenocarcinoma
- Pan-cancer
- Proportional hazard assumption
- READ, Rectum adenocarcinoma
- SARC, Sarcoma
- SKCM, Skin Cutaneous Melanoma
- STAD, Stomach adenocarcinoma
- TCGA
- TCGA, The Cancer Genome Atlas
- TCGA, tumor abbreviations
- TGCT, Testicular Germ Cell Tumors
- THCA, Thyroid carcinoma
- THYM, Thymoma
- Transcriptome
- UCEC, Uterine Corpus Endometrial Carcinoma
- UCS, Uterine Carcinosarcoma
- UVM, Uveal Melanoma
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Affiliation(s)
- Zihang Zeng
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yanping Gao
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jiali Li
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Gong Zhang
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shaoxing Sun
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Qiuji Wu
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yan Gong
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.,Tumor Precision Diagnosis and Treatment Technology and Translational Medicine, Hubei Engineering Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Conghua Xie
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
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Zhang H, Luo YB, Wu W, Zhang L, Wang Z, Dai Z, Feng S, Cao H, Cheng Q, Liu Z. The molecular feature of macrophages in tumor immune microenvironment of glioma patients. Comput Struct Biotechnol J 2021; 19:4603-18. [PMID: 34471502 DOI: 10.1016/j.csbj.2021.08.019] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [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: 03/31/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 12/12/2022] Open
Abstract
Background Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth. Methods Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore. Results Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate. Conclusion Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.
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Key Words
- ACC, Adrenocortical carcinoma
- BBB, brain blood barrier
- BLCA, Bladder Urothelial Carcinoma
- BRCA, Breast invasive carcinoma
- CDF, cumulative distribution function
- CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma
- CGGA, Chinese Glioma Genome Atlas
- CHOL, Cholangiocarcinoma
- CNA, copy number alternations
- CNV, copy number variation
- COAD, Colon adenocarcinoma
- CSF-1, colony-stimulating factor-1
- DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma
- DMP, differentially methylated position
- ESCA, Esophageal carcinoma
- GBM, glioblastoma
- GEO, Gene Expression Omnibus
- GO, gene ontology
- GSEA, gene set enrichment analysis
- GSVA, gene set variation analysis
- Glioma microenvironment
- HNSC, Head and Neck squamous cell carcinoma
- IGR, intergenic region
- IHC, immunohistochemistry
- IL, interleukin
- Immunotherapy
- KEGG, Kyoto Encyclopaedia of Genes and Genomes
- KICH, Kidney Chromophobe
- KIRC, Kidney renal clear cell carcinoma
- KIRP, Kidney renal papillary cell carcinoma
- LGG, low grade glioma
- LIHC, Liver hepatocellular carcinoma
- LUAD, Lung adenocarcinoma
- LUSC, Lung squamous cell carcinoma
- MMP-2, matrix metalloproteinase-2
- MT1, MMP membrane type 1 matrix metalloprotease
- Machine learning
- Macrophage
- OV, Ovarian serous cystadenocarcinoma
- PAAD, Pancreatic adenocarcinoma
- PAM, partition around medoids
- PCA, principal component analysis
- PCPG, Pheochromocytoma and Paraganglioma
- PRAD, Prostate adenocarcinoma
- Prognostic model
- READ, Rectum adenocarcinoma
- SARC, Sarcoma
- SKCM, Skin Cutaneous Melanoma
- SNP, single-nucleotide polymorphism
- SNV, single-nucleotide variant
- STAD, Stomach adenocarcinoma
- SVM, Support Vector Machines
- TAM, tumor associated macrophage
- TCGA, The Cancer Genome Atlas
- TGF-β, tumor growth factor-β
- THCA, Thyroid carcinoma
- THYM, Thymoma
- TIMP-2, tissue inhibitor of metalloproteinase-2
- TLR2, toll-like receptor 2
- TME, tumor microenvironment
- TNFα, tumor necrosis factor α
- TSS, transcription start site
- UCEC, Uterine Corpus Endometrial Carcinoma
- UCS, Uterine Carcinosarcoma
- WGCNA, weighted gene co-expression network analysis
- pamr, prediction analysis for microarrays
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