Hallmark guided identification and characterization of a novel immune-relevant signature for prognostication of recurrence in stage I–III lung adenocarcinoma

Zhang, Yongqiang; Zhao, Yang; Tang, Yuqin; Guo, Chengbin; Lin, Danni; Cheng, Linling; Hu, Xun; Zhang, Kang; Li, Gen

doi:10.1016/j.gendis.2022.07.005

Cited by 7 publications

(8 citation statements)

References 72 publications

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“…The disease-specific survival (DSS) and corresponding clinicopathologic characteristics including age, stage, grade, tumor status, and lymphatic invasion status were also achieved. The RNA-seq data were preprocessed as we previously reported ( Zhang et al, 2022 ). 12 literature-derived CCGs in basic mammalian central feedback loop were collected ( ARNTL , CLOCK , PER1 , PER2 , PER3 , CRY1 , CRY2 , NR1D1 , NR1D2 , RORA , RORB , and RORC ) ( Shen et al, 2020 ; Cash et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Integrative analysis of circadian clock with prognostic and immunological biomarker identification in ovarian cancer

Zhao

Tang

Yang

et al. 2023

Front. Mol. Biosci.

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Objective: To identify circadian clock (CC)-related key genes with clinical significance, providing potential biomarkers and novel insights into the CC of ovarian cancer (OC).Methods: Based on the RNA-seq profiles of OC patients in The Cancer Genome Atlas (TCGA), we explored the dysregulation and prognostic power of 12 reported CC-related genes (CCGs), which were used to generate a circadian clock index (CCI). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to identify potential hub genes. Downstream analyses including differential and survival validations were comprehensively investigated.Results: Most CCGs are abnormally expressed and significantly associated with the overall survival (OS) of OC. OC patients with a high CCI had lower OS rates. While CCI was positively related to core CCGs such as ARNTL, it also showed significant associations with immune biomarkers including CD8+ T cell infiltration, the expression of PDL1 and CTLA4, and the expression of interleukins (IL-16, NLRP3, IL-1β, and IL-33) and steroid hormones-related genes. WGCNA screened the green gene module to be mostly correlated with CCI and CCI group, which was utilized to construct a PPI network to pick out 15 hub genes (RNF169, EDC4, CHCHD1, MRPL51, UQCC2, USP34, POM121, RPL37, SNRPC, LAMTOR5, MRPL52, LAMTOR4, NDUFB1, NDUFC1, POLR3K) related to CC. Most of them can exert prognostic values for OS of OC, and all of them were significantly associated with immune cell infiltration. Additionally, upstream regulators including transcription factors and miRNAs of key genes were predicted.Conclusion: Collectively, 15 crucial CC genes showing indicative values for prognosis and immune microenvironment of OC were comprehensively identified. These findings provided insight into the further exploration of the molecular mechanisms of OC.

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Section: Methodsmentioning

confidence: 99%

Integrative analysis of circadian clock with prognostic and immunological biomarker identification in ovarian cancer

Zhao

Tang

Yang

et al. 2023

Front. Mol. Biosci.

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“…Subsequently, Kaplan-Meier survival analysis and log-rank test were used to identify the prognostic DECSGs for OS. To enhance the stability, we adopted the "multi-split" approach as we reported with some modifications (Tang et al, 2020;Guo et al, 2022;Zhang et al, 2022). Next, the prognostic DECSGs were utilized to perform unsupervised clustering for the patients with complete survival information, and the R package "ConsensusClusterPlus" was used to explore novel CSG patterns of HCC (Wilkerson and Hayes, 2010;Wang et al, 2021;Guo et al, 2022).…”

Section: Identification Of Prognostic Differentially Expressed Csgs (...mentioning

confidence: 99%

“…The Gene set variation analysis process was completed to calculate the enrichment scores of 50 hallmark gene sets of the molecular signature database (Liberzon et al, 2015) for the TCGA-LIHC cohort by the "GSVA" package (Hanzelmann et al, 2013). Differential analysis was then performed by the "limma" package to reveal the relative activities of these hallmark pathways in the two CSG subtypes, which was determined by the criterion of p < 0.01 combined with absolute t > 2.5 (Lambrechts et al, 2018;Zhang et al, 2022). GSEA was further employed to verify the GSVA results using the GSEA software with default settings.…”

Section: Gene Set Variation Analysis (Gsva) and Gene Set Enrichment A...mentioning

confidence: 99%

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Characterization of cellular senescence patterns predicts the prognosis and therapeutic response of hepatocellular carcinoma

Tang

Guo

Chen

et al. 2022

Front. Mol. Biosci.

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Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a high mortality rate. Cellular senescence, an irreversible state of cell cycle arrest, plays a paradoxical role in cancer progression. Here, we aimed to identify Hepatocellular carcinoma subtypes by cellular senescence-related genes (CSGs) and to construct a cellular senescence-related gene subtype predictor as well as a novel prognostic scoring system, which was expected to predict clinical outcomes and therapeutic response of Hepatocellular carcinoma.Methods: RNA-seq data and clinical information of Hepatocellular carcinoma patients were derived from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). The “multi-split” selection was used to screen the robust prognostic cellular senescence-related genes. Unsupervised clustering was performed to identify CSGs-related subtypes and a discriminant model was obtained through multiple statistical approaches. A CSGs-based prognostic model-CSGscore, was constructed by LASSO-Cox regression and stepwise regression. Immunophenoscore (IPS) and Tumor Immune Dysfunction and Exclusion (TIDE) were utilized to evaluate the immunotherapy response. Tumor stemness indices mRNAsi and mDNAsi were used to analyze the relationship between CSGscore and stemness.Results: 238 robust prognostic differentially expressed cellular senescence-related genes (DECSGs) were used to categorize all 336 hepatocellular carcinoma patients of the TCGA-LIHC cohort into two groups with different survival. Two hub genes, TOP2A and KIF11 were confirmed as key indicators and were used to form a precise and concise cellular senescence-related gene subtype predictor. Five genes (PSRC1, SOCS2, TMEM45A, CCT5, and STC2) were selected from the TCGA training dataset to construct the prognostic CSGscore signature, which could precisely predict the prognosis of hepatocellular carcinoma patients both in the training and validation datasets. Multivariate analysis verified it as an independent prognostic factor. Besides, CSGscore was also a valuable predictor of therapeutic responses in hepatocellular carcinoma. More downstream analysis revealed the signature genes were significantly associated with stemness and tumor progression.Conclusion: Two subtypes with divergent outcomes were identified by prognostic cellular senescence-related genes and based on that, a subtype indicator was established. Moreover, a prognostic CSGscore system was constructed to predict the survival outcomes and sensitivity of therapeutic responses in hepatocellular carcinoma, providing novel insight into hepatocellular carcinoma biomarkers investigation and design of tailored treatments depending on the molecular characteristics of individual patients.

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“…In addition, cancer complexity is represented by numerous cancer hallmarks that allow cancer cells to proliferate and metastasize. Cancer hallmark genes are responsible for crucial phenotypic features associated with the malignant development of cancers, and signatures established by cancer hallmark genes are important in various cancer types [ 13 , 14 ]. A hallmark-based six-gene levels can reflect the genetic characteristics of colorectal cancer cells and can be used to evaluate the recurrence risk of colorectal cancer [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets

Zhou,

Zeng,

Zhang

et al. 2024

Heliyon

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Hallmark guided identification and characterization of a novel immune-relevant signature for prognostication of recurrence in stage I–III lung adenocarcinoma

Cited by 7 publications

References 72 publications

Integrative analysis of circadian clock with prognostic and immunological biomarker identification in ovarian cancer

Integrative analysis of circadian clock with prognostic and immunological biomarker identification in ovarian cancer

Characterization of cellular senescence patterns predicts the prognosis and therapeutic response of hepatocellular carcinoma

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets

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