Identification and validation of hypoxia-derived gene signatures to predict clinical outcomes and therapeutic responses in stage I lung adenocarcinoma patients

Shi, Run; Bao, Xuanwen; Unger, Kristian; Sun, Jing; Lü, Shun; Manapov, Farkhad; Wang, Xuanbin; Belka, Claus; Li, Minglun

doi:10.7150/thno.56202

Cited by 64 publications

(62 citation statements)

References 53 publications

(35 reference statements)

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“…The assessment from four different aspects confirmed the superiority of the combined model compared with the original signature and clinical parameters alone ( Xiong et al, 2018 ; Wang et al, 2020 ). Besides this, the decision tree made the subdivision based on the specific clinical parameters, which separated the entire cohorts into three subgroups: high-risk group, median-risk group, and low-risk group ( Sun et al, 2020 ; Shi et al, 2021 ). The subsequent survival analysis revealed the significant differences among the three subgroups, substantiating the necessity of the improved risk stratification.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma

Zhang

et al. 2021

Front. Genet.

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Clear cell renal cell carcinoma (ccRCC) is widely acknowledged to be extremely sensitive to immunotherapy, emphasizing the tremendous impacts on which the tumor microenvironment (TME) has shown. However, the molecular subgroups characterized by the TME features scarcely serve as the risk stratification guides in clinical practice for survival outcomes and immunotherapy response prediction. This study generated fresh insights into a novel TME-related prognostic signature derived from The Cancer Genome Atlas database using integrated bioinformatics analyses. Subsequently, Kaplan–Meier survival analysis, receiver operating characteristic analysis, and univariate and multivariate Cox regression analysis were performed to evaluate and validate the efficacy and the accuracy of the signature in ccRCC prognosis. Furthermore, we discovered that the risk score presented an increased likelihood of correlation with miscellaneous clinicopathological characteristics, natural killer cell-mediated cytotoxicity, immune cell infiltration levels, and immune checkpoint expression. These findings highlighted the notion that the six-gene signature characterized by the TME features may have implications on the risk stratification for personalized and precise immunotherapeutic management.

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Section: Discussionmentioning

confidence: 99%

Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma

Zhang

et al. 2021

Front. Genet.

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“…The co-expression gene and enrichment analysis were applied to WGCNA package of R software, which revealed the correlation between genes 8 . Because the genes with little expression variation usually represent noise, we filter the most variable genes (SD > 1.2) and construct a network.…”

Section: Methodsmentioning

confidence: 99%

PD-L1 correlated gene expression profiles and tumor infiltrating lymphocytes in pancreatic cancer

Li¹,

Yin²,

Chen³

et al. 2021

Int. J. Med. Sci.

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Objective: To study the expression and clinical value of PD-L1 gene in pancreatic cancer, and to predict the role of PD-L1 gene in the development of pancreatic cancer. Methods: The pancreatic cancer datasets were downloaded from the Cancer Genome Atlas (TCGA) and the Oncomine to obtain the PD-L1 gene expression profile and clinical information. Bioinformatics methods were used to analyze the correlation between the expression level of PD-L1 gene in pancreatic cancer and clinicopathological indicators, as well as its influence on prognosis. GSEA and WGCNA analysis was performed to predict the possible pathways of PD-L1 gene regulation in pancreatic cancer. TIMER and MCP-counter were used for PD-L1 with immune infiltration. The genes interact with PD-L1 were also investigated by STING and immunoco-precipitation combined with mass spectrometry analysis (IP-MS). Results: In TCGA database, the overall survival of patients with high expression of PD-L1 gene was significantly lower than that of patients with low expression of PD-L1 gene (χ 2 = 12.52, P < 0.001). The samples with high expression of PD-L1 gene showed enrichment of 8 pathways including toll-like receptor signaling pathway and NOD receptor signaling pathway (P < 0.01, FDR < 0.05). Immune infiltration analysis suggested that PD-L1 were associated with monocytic lineage (r = 0.5). The proteins interacting with PD-L1 are mainly concentrated in RNA binding, ribosome, spliceosome and other biological processes or pathways. Conclusion: PD-L1 gene may play an important role in the development of pancreatic cancer and is expected to be a prognostic indicator of pancreatic cancer.

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“…The co-expression gene and enrichment analysis were applied to WGCNA package of R software, which revealed the correlation between genes [12]. Because the genes with little expression variation usually represent noise, we lter the most variable genes (SD > 1.2) and construct a network.…”

Section: Weighted Correlation Network Analysis (Wgcna)mentioning

confidence: 99%

Identification of Genes Related to 68Ga-DOTATATE Organ Distribution in Homo Sapiens

Huang

2021

Preprint

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Purpose 68Ga-DOTATATE is a somatostatin analogue that has been used for imaging neuroendocrine tumours. However, there is nonspecific uptake in some organs, and the reasons for that are not clear. The aim of the study is to outline the dynamic distribution pattern of 68Ga-DOTATATE in human body, and identify the genes responsible for 68Ga-DOTATATE uptake by bioinformatics analysis.Methods 68Ga-DOTATATE PET/CT was performed in 32 patients, and dynamic total-body PET scanning was performed with uEXPLORER. The gene expression datasets of human organs were downloaded from the Human Protein Atlas. WGCNA analysis was performed to screen the potential genes related to 68Ga-DOTATATE. BindingDB, SEA and SwissTargetPrediction databases were used to predict the potential binding proteins of DOTATATE based on molecular structure. Results Dynamic total-body PET scanning showed that 68Ga-DOTATATE uptake was not consistent with expression of SSTR2 in human organs. WGCNA analysis revealed 800 genes whose expression level was positively correlated to 68Ga-DOTATATE uptake. According to the molecular structure of DOTATATE, 135 proteins with potential binding ability to DOTATATE were screened by analysis based on drug database. Based on the results of the above two parts, 8 proteins were finally obtained, respectively AVPR1A, EPHA8, EPHB4, OPRL1, SSTR2, SSTR5, ST3GAL1 and NPY1R, which had potential binding possibility with DOTATATE, and their expression was positively correlated with 68Ga-DOTATATE uptake.Conclusion WGCNA analysis was combined with the drug database to obtain new potential binding targets of DOTATATE. The bioinformatics approach developed in this study could potentially be used to discover new potential binding targets for molecular imaging agents.

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Identification and validation of hypoxia-derived gene signatures to predict clinical outcomes and therapeutic responses in stage I lung adenocarcinoma patients

Cited by 64 publications

References 53 publications

Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma

Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma

PD-L1 correlated gene expression profiles and tumor infiltrating lymphocytes in pancreatic cancer

Identification of Genes Related to 68Ga-DOTATATE Organ Distribution in Homo Sapiens

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