SURVFIT: Doubly sparse rule learning for survival data

Shakur, Ameer Hamza; Huang, Shuai; Qian, Xiaoning; Chang, Xiangyu

doi:10.1016/j.jbi.2021.103691

Cited by 7 publications

(4 citation statements)

References 47 publications

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“…Briefly, we calculated the optimal cut-off values for NPM3 using the R package maxstat (Maximally selected rank statistics with several p -value approximations version: 0.7–25), and patients were divided into high and low NPM3 expression groups. Prognosis was analyzed using the survfit function of the R package survival [ 18 ], and differences in prognosis between the groups were evaluated using the log-rank test method. We also analyzed the influence of GLRX3 , and CALCOCO1 on the survival of LUAD patients using the UALCAN database [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

NPM3 as a novel oncogenic factor and poor prognostic marker contributes to cell proliferation and migration in lung adenocarcinoma

Wei

Xing

et al. 2023

Hereditas

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Background Lung cancer is the leading cause of cancer-related deaths worldwide, and despite recent advances in targeted therapies and immunotherapies, the clinical benefit remains limited. Therefore, there is an urgent need to further investigate the molecular mechanisms underlying lung cancer. The aim of this study was to investigate the expression and function of NPM3 in the tumor microenvironment of lung adenocarcinoma (LUAD). Methods We utilized bioinformatics tools and databases, including UALCAN, GEPIA2, HPA, and Sangerbox, to analyze NPM3 expression in LUAD samples and its association with prognosis and mutational landscape. NPM3 expression in various cell types was assessed at the single cell level using the TISCH database. We also used algorithms such as TIMER and EPIC to explore the crosstalk between NPM3 expression and immune features. KEGG enrichment analysis was performed to identify potential signaling pathways of NPM3. Finally, we employed siRNA knockdown strategy to investigate the effect of NPM3 on LUAD cell proliferation and migration in vitro. Results NPM3 was significantly upregulated in LUAD tissues and was strongly associated with poor prognosis and TP53 gene mutations. Single-cell sequencing analysis revealed that NPM3 was expressed in immune cells (dendritic cells and monocytes/macrophages) in the tumor microenvironment. Moreover, NPM3 expression was negatively associated with immune B cell and CD4 T cell infiltration, as well as with several immune-related genes (including CCL22, CXCR2, CX3CR1, CCR6, HLA-DOA, HLA-DQA2). KEGG enrichment analysis indicated that NPM3 expression was associated with cell cycle, CAMs, and NSCLC pathway genes. Finally, in vitro experiments showed that NPM3 knockdown inhibited LUAD cell proliferation and migration in NCI-H1299 and SPC-A1 cells, and suppressed the expression of CCNA2 and MAD2L1. Conclusion Elevated NPM3 expression predicts poor clinical outcome and an immunosuppressive microenvironment in LUAD tissues. NPM3 promotes LUAD progression by promoting cell proliferation and migration, and targeting NPM3 may represent a novel therapeutic strategy for LUAD. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

NPM3 as a novel oncogenic factor and poor prognostic marker contributes to cell proliferation and migration in lung adenocarcinoma

Wei

Xing

et al. 2023

Hereditas

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“…Patients were classified into two groups depending on the risk scores obtained by the Cox proportional hazards model, and the prognostic difference of the two groups was analyzed by the survfit function of the R software package ( 18 ). The Kaplan-Meier survival curve and log-rank test was utilized to assess the statistical significance of the prognosis among the high-risk and low-risk groups.…”

Section: Methodsmentioning

confidence: 99%

Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma

et al. 2023

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BackgroundMucinous adenocarcinoma (MAC) is a unique clinicopathological colorectal cancer (CRC) type that has been recognized as a separate entity from non-mucinous adenocarcinoma (NMAC), with distinct clinical, pathologic, and molecular characteristics. We aimed to construct prognostic signatures and identifying candidate biomarkers for patients with MAC.MethodsDifferential expression analysis, weighted correlation network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-Cox regression model were used to identify hub genes and construct a prognostic signature based on RNA sequencing data from TCGA datasets. The Kaplan-Meier survival curve, gene set enrichment analysis (GSEA), cell stemness, and immune infiltration were analyzed. Biomarker expression in MAC and corresponding normal tissues from patients operated in 2020 was validated using immunohistochemistry.ResultsWe constructed a prognostic signature based on ten hub genes. Patients in the high-risk group had significantly worse overall survival (OS) than patients in the low-risk group (p < 0.0001). We also found that ENTR1 was closely associated with OS (p = 0.016). ENTR1 expression was significantly positively correlated with cell stemness of MAC (p < 0.0001) and CD8+ T cell infiltration (p = 0.01), whereas it was negatively associated with stromal scores (p = 0.03). Finally, the higher expression of ENTR1 in MAC tissues than in normal tissues was validated.ConclusionWe established the first MAC prognostic signature, and determined that ENTR1 could serve as a prognostic marker for MAC.

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“…The survival analysis was performed by using the survival package in R ( 38 ). Kaplan–Meier (KM) plots were generated using the survfit function (version 3.2-13) in R ( 47 ); meanwhile, the values of p from log-rank tests were calculated. The clinical OSCC samples with the survival time of <5 years were obtained.…”

Section: Methodsmentioning

confidence: 99%

Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival

Tang

Franceschi

et al. 2022

Front. Med.

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ObjectiveThis study aimed to identify the programmed death ligand-1 (PDL1, also termed as CD274) and its positively correlated immune checkpoint genes (ICGs) and to determine the immune subtypes of CD274-centered ICG combinations in oral and squamous cell carcinoma (OSCC).Materials and MethodsFirstly, the 95 ICGs obtained via literature reviews were identified in the Cancer Genome Atlas (TCGA) database in relation to OSCC, and such 88 ICG expression profiles were extracted. ICGs positively correlated with CD274 were utilized for subsequent analysis. The relationship between ICGs positively correlated with CD274 and immunotherapy biomarkers (tumor mutation burden (TMB), and adaptive immune resistance pathway genes) was investigated, and the relationships of these genes with OSCC clinical features were explored. The prognostic values of CD274 and its positively correlated ICGs and also their associated gene pairs were revealed using the survival analysis.ResultsEight ICGs, including CTLA4, ICOS, TNFRSF4, CD27, B- and T-lymphocyte attenuator (BTLA), ADORA2A, CD40LG, and CD28, were found to be positively correlated with CD274. Among the eight ICGs, seven ICGs (CTLA4, ICOS, TNFRSF4, CD27, BTLA, CD40LG, and CD28) were significantly negatively correlated with TMB. The majority of the adaptive immune resistance pathway genes were positively correlated with ICGs positively correlated with CD274. The survival analysis utilizing the TCGA-OSCC data showed that, although CD274 was not significantly associated with overall survival (OS), the majority of ICGs positively correlated with CD274 (BTLA, CD27, CTLA4, CD40LG, CD28, ICOS, and TNFRSF4) were significantly correlated with OS, whereby their low-expression predicted a favorable prognosis. The survival analysis based on the gene pair subtypes showed that the combination subtypes of CD274_low/BTLA_low, CD274_low/CD27_low, CD274_low/CTLA4_low, CD8A_high/BTLA_low, CD8A_high/CD27_low, and CD8A_high/CTLA4_low predicted favorable OS.ConclusionThe results in this study provide a theoretical basis for prognostic immune subtyping of OSCC and highlight the importance of developing future immunotherapeutic strategies for treating oral cancer.

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SURVFIT: Doubly sparse rule learning for survival data

Cited by 7 publications

References 47 publications

NPM3 as a novel oncogenic factor and poor prognostic marker contributes to cell proliferation and migration in lung adenocarcinoma

NPM3 as a novel oncogenic factor and poor prognostic marker contributes to cell proliferation and migration in lung adenocarcinoma

Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma

Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival

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