Identification of immune-related gene signature predicting survival in the tumor microenvironment of lung adenocarcinoma

2020 ◽  
Vol 72 (9-10) ◽  
pp. 455-465
Author(s):  
Mengnan Zhao ◽  
Ming Li ◽  
Zhencong Chen ◽  
Yunyi Bian ◽  
Yuansheng Zheng ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Lung Adenocarcinoma ◽  
Gene Signature ◽  
Related Gene ◽  
Immune Related Gene

scholarly journals Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chunlei Wu ◽  
Quanteng Hu ◽  
Dehua Ma
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Pathological Subtype

AbstractLung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

scholarly journals Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood

2021 ◽  
Vol 12 ◽  
Author(s):  
Xin Jin ◽  
Jun Wang ◽  
Lina Ge ◽  
Qing Hu
Keyword(s):  
Peripheral Blood ◽  
Training Model ◽  
Gene Signature ◽  
Training Dataset ◽  
Support Vector ◽  
Consensus Clustering ◽  
Related Gene ◽  
Clustering Method ◽  
Immune Related Gene ◽  
Immune Related Genes

Objective: Sciatica pertains to neuropathic pain that has been associated with inflammatory response. We aimed to identify significant immune-related biomarkers for sciatica in peripheral blood.Methods: We utilized the GSE150408 expression profiling data from the Gene Expression Omnibus (GEO) database as the training dataset and extracted immune-related genes for further analysis. Differentially expressed immune-related genes (DEIRGs) between healthy controls and patients with sciatica were selected using the “limma” package and verified in clinical specimens by quantitative reverse transcription PCR (RT-qPCR). A diagnostic immune-related gene signature was established using the training model and random forest (RF), generalized linear model (GLM), and support vector machine (SVM) models. Sciatica patient subtypes were identified using the consensus clustering method.Results: Thirteen significant DEIRGs were acquired, of which five (CRP, EREG, FAM19A4, RLN1, and WFIKKN1) were selected to establish a diagnostic immune-related gene signature according to the most appropriate training model, namely, the RF model. A clinical application nomogram model was established based on the expression level of the five DEIRGs. The sciatica patients were divided into two subtypes (C1 and C2) according to the consensus clustering method.Conclusions: Our research established a diagnostic five immune-related gene signature to discriminate sciatica and identified two sciatica subtypes, which may be beneficial to the clinical diagnosis and treatment of sciatica.

Prognostication of a 13-immune-related-gene signature in patients with early triple-negative breast cancer

2020 ◽  
Vol 184 (2) ◽  
pp. 325-334
Author(s):  
Ji-Yeon Kim ◽  
Hae Hyun Jung ◽  
Insuk Sohn ◽  
Sook Young Woo ◽  
Hyun Cho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Gene Signature ◽  
Related Gene ◽  
Immune Related Gene

scholarly journals Development of a Predictive Immune-Related Gene Signature Associated With Hepatocellular Carcinoma Patient Prognosis

2020 ◽  
Vol 27 (1) ◽  
pp. 107327482097711
Author(s):  
Jiasheng Lei ◽  
Dengyong Zhang ◽  
Chao Yao ◽  
Sheng Ding ◽  
Zheng Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Cox Regression ◽  
R Package ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Related Gene ◽  
Patient Cohort ◽  
Immune Related Gene ◽  
Related Risk

Background: Hepatocellular carcinoma (HCC) remains the third leader cancer-associated cause of death globally, but the etiological basis for this complex disease remains poorly clarified. The present study was thus conceptualized to define a prognostic immune-related gene (IRG) signature capable of predicting immunotherapy responsiveness and overall survival (OS) in patients with HCC. Methods: Five differentially expressed IRG associated with HCC were established the immune-related risk model through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Patients were separated at random into training and testing cohorts, after which the association between the identified IRG signature and OS was evaluated using the “survival” R package. In addition, maftools was leveraged to assess mutational data, with tumor mutation burden (TMB) scores being calculated as follows: (total mutations/total bases) × 106. Immune-related risk term abundance was quantified via “ssGSEA” algorithm using the “gsva” R package. Results: HCC patients were successfully stratified into low-risk and high-risk groups based upon a signature composed of 5 differentially expressed IRGs, with overall survival being significantly different between these 2 groups in training cohort, testing cohort and overall patient cohort ( P = 1.745e-06, P = 1.888e-02, P = 4.281e-07). No association was observed between TMB and this IRG risk score in the overall patient cohort ( P = 0.461). Notably, 19 out of 29 immune-related risk terms differed substantially in the overall patient dataset. These risk terms mainly included checkpoints, human leukocyte antigens, natural killer cells, dendritic cells, and major histocompatibility complex class I. Conclusion: In summary, an immune-related prognostic gene signature was successfully developed and used to predict survival outcomes and immune system status in patients with HCC. This signature has the potential to help guide immunotherapeutic treatment planning for patients affected by this deadly cancer.

scholarly journals Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients

2020 ◽  
Vol 11 ◽  
Author(s):  
Siqi Dai ◽  
Shuang Xu ◽  
Yao Ye ◽  
Kefeng Ding
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Expression Profiles ◽  
Gene Signature ◽  
Clinical Risk Factors ◽  
Survival Prediction ◽  
Related Gene ◽  
Cancer Management ◽  
Clinical Risk ◽  
Immune Related Gene

BackgroundDespite recent advance in immune therapy, great heterogeneity exists in the outcomes of colorectal cancer (CRC) patients. In this study, we aimed to analyze the immune-related gene (IRG) expression profiles from three independent public databases and develop an effective signature to forecast patient’s prognosis.MethodsIRGs were collected from the ImmPort database. The CRC dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another two CRC datasets from the Gene Expression Omnibus (GEO). Gene function enrichment analysis was conducted. A prognostic nomogram was built incorporating the IRG signature with clinical risk factors.ResultsThe three datasets had 487, 579, and 224 patients, respectively. A prognostic six-gene-signature (CCL22, LIMK1, MAPKAPK3, FLOT1, GPRC5B, and IL20RB) was developed through feature selection that showed good differentiation between the low- and high-risk groups in the training set (p < 0.001), which was later confirmed in the two validation groups (log-rank p < 0.05). The signature outperformed tumor TNM staging for survival prediction. GO and KEGG functional annotation analysis suggested that the signature was significantly enriched in metabolic processes and regulation of immunity (p < 0.05). When combined with clinical risk factors, the model showed robust prediction capability.ConclusionThe immune-related six-gene signature is a reliable prognostic indicator for CRC patients and could provide insight for personalized cancer management.

Sign in / Sign up

Export Citation Format

Share Document