Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma

Abstract Background The incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD. Methods The public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan–Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts. Results A prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29–8.11; P = 6.16E−06), GSE31210 (HR = 11.91, 95% CI 4.15–34.19; P = 4.10E−06), GSE50081 (HR = 3.63, 95% CI 1.90–6.95; P = 9.95E−05), the combined data set (HR = 3.15, 95% CI 1.98–5.02; P = 1.26E−06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49–3.13; P = 4.54E−05) and GSE72094 (HR = 2.95, 95% CI 1.86–4.70; P = 4.79E−06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design. Conclusions Our study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

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An Immune Signature Robustly Predicts Clinical Deterioration for Hepatitis C Virus-Related Early-Stage Cirrhosis Patients

Frontiers in Medicine ◽

10.3389/fmed.2021.716869 ◽

2021 ◽

Vol 8 ◽

Author(s):

Cheng Guo ◽

Chenglai Dong ◽

Junjie Zhang ◽

Rui Wang ◽

Zhe Wang ◽

...

Keyword(s):

Hepatitis C Virus ◽

Hepatitis C ◽

Early Stage ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Calibration Plot ◽

Prognostic Signature ◽

Data Set ◽

Immune Infiltration ◽

Immune Related Genes

Hepatitis C virus (HCV)-related cirrhosis leads to a heavy global burden of disease. Clinical risk stratification in HCV-related compensated cirrhosis remains a major challenge. Here, we aim to develop a signature comprised of immune-related genes to identify patients at high risk of progression and systematically analyze immune infiltration in HCV-related early-stage cirrhosis patients. Bioinformatics analysis was applied to identify immune-related genes and construct a prognostic signature in microarray data set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted with the “clusterProfiler” R package. Besides, the single sample gene set enrichment analysis (ssGSEA) was used to quantify immune-related risk term abundance. The nomogram and calibrate were set up via the integration of the risk score and clinicopathological characteristics to assess the effectiveness of the prognostic signature. Finally, three genes were identified and were adopted to build an immune-related prognostic signature for HCV-related cirrhosis patients. The signature was proved to be an independent risk element for HCV-related cirrhosis patients. In addition, according to the time-dependent receiver operating characteristic (ROC) curves, nomogram, and calibration plot, the prognostic model could precisely forecast the survival rate at the first, fifth, and tenth year. Notably, functional enrichment analyses indicated that cytokine activity, chemokine activity, leukocyte migration and chemotaxis, chemokine signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved in HCV-related cirrhosis progression. Moreover, ssGSEA analyses revealed fierce immune-inflammatory response mechanisms in HCV progress. Generally, our work developed a robust prognostic signature that can accurately predict the overall survival, Child-Pugh class progression, hepatic decompensation, and hepatocellular carcinoma (HCC) for HCV-related early-stage cirrhosis patients. Functional enrichment and further immune infiltration analyses systematically elucidated potential immune response mechanisms.

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Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

10.21203/rs.3.rs-52624/v2 ◽

2021 ◽

Author(s):

tiefeng cao ◽

huimin shen

Keyword(s):

Ovarian Cancer ◽

Overall Survival ◽

Network Analysis ◽

Survival Prediction ◽

Immune Gene ◽

Prognostic Signature ◽

Data Set ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background： Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.

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Identification of a Novel Prognostic Signature of Genome Instability-Related LncRNAs in Early Stage Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.706454 ◽

2021 ◽

Vol 9 ◽

Author(s):

Bo Peng ◽

Huawei Li ◽

Ruisi Na ◽

Tong Lu ◽

Yongchao Li ◽

...

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Genomic Instability ◽

Cox Regression ◽

Genome Instability ◽

Early Stage ◽

Prognostic Signature ◽

Training Set ◽

A Genome ◽

Testing Set

BackgroundIncreasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play a crucial part in maintaining genomic instability. We therefore identified genome instability-related lncRNAs and constructed a prediction signature for early stage lung adenocarcinoma (LUAD) as well in order for classification of high-risk group of patients and improvement of individualized therapies.MethodsEarly stage LUAD RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were randomly divided into training set (n = 177) and testing set (n = 176). A total of 146 genomic instability-associated lncRNAs were identified based on somatic mutation profiles combining lncRNA expression profiles from TCGA by the “limma R” package. We performed Cox regression analysis to develop this predictive indicator. We validated the prognostic signature by an external independent LUAD cohort with microarray platform acquired from the Gene Expression Omnibus (GEO).ResultsA genome instability-related six-lncRNA-based gene signature (GILncSig) was established to divide subjects into high-risk and low-risk groups with different outcomes at statistically significant levels. According to the multivariate Cox regression and stratification analysis, the GILncSig was an independent predictive factor. Furthermore, the six-lncRNA signature achieved AUC values of 0.745, 0.659, and 0.708 in the training set, testing set, and TCGA set, respectively. When compared with other prognostic lncRNA signatures, the GILncSig also exhibited better prediction performance.ConclusionThe prognostic lncRNA signature is a potent tool for risk stratification of early stage LUAD patients. Our study also provided new insights for identifying genome instability-related cancer biomarkers.

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Development of a multi-gene-based immune prognostic signature in ovarian Cancer

Journal of Ovarian Research ◽

10.1186/s13048-021-00766-4 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Tiefeng Cao ◽

Huimin Shen

Keyword(s):

Ovarian Cancer ◽

Overall Survival ◽

Network Analysis ◽

Survival Prediction ◽

Immune Gene ◽

Prognostic Signature ◽

Data Set ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature. Methods This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes. Conclusions We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.

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Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

10.21203/rs.3.rs-52624/v1 ◽

2020 ◽

Author(s):

tiefeng cao ◽

huimin shen

Keyword(s):

Ovarian Cancer ◽

Network Analysis ◽

Prognostic Biomarkers ◽

Survival Prediction ◽

Immune Gene ◽

Prognostic Signature ◽

Data Set ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background：Chemotherapeutic resistance is responsible for treatment failure. Immunotherapy is important in ovarian cancer (OC). Systematic exploration of immunogenic landscape and reliable immune gene-based prognostic biomarkers or signature is necessary to be identified. This study aims to identify the immune gene-based prognostic biomarkers and regulatory factors, further to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles from RNA-seq data set for The Cancer Genome Atlas (TCGA) ovarian cancer. Differentially expressed and survival-associated immune genes and transcription factors (TFs) were identified using immune genes from ImmPort dataset and TFs from Cistoma database. We developed the prognostic signature based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, Network analysis was performed to uncover the potential molecular mechanisms of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, prognosis, even immunotherapy response of OC patients.

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Prognostic phenotypes of early-stage lung adenocarcinoma

European Respiratory Journal ◽

10.1183/13993003.01674-2021 ◽

2021 ◽

pp. 2101674

Author(s):

Anne-Sophie Lamort ◽

Jan Christian Kaiser ◽

Mario A.A. Pepe ◽

Ioannis Lilis ◽

Giannoula Ntaliarda ◽

...

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Early Stage ◽

Machine Learning Algorithms ◽

Prognostic Information ◽

Tissue Samples ◽

Cancer Hallmarks ◽

Patient Subgroups ◽

Prognostic Power

BackgroundSurvival after curative resection of early-stage lung adenocarcinoma (LUAD) varies and prognostic biomarkers are urgently needed.MethodsLarge-format tissue samples from a prospective cohort of 200 patients with resected LUAD were immunophenotyped for cancer hallmarks TP53, NF1, CD45, PD-1, PCNA, TUNEL, and FVIII, and were followed for median (95%CI)=2.34 (1.71–3.49) years.ResultsUnsupervised hierarchical clustering revealed two patient subgroups with similar clinicopathologic features and genotype, but with markedly different survival: “proliferative” patients (60%) with elevated TP53, NF1, CD45, and PCNA expression had 50% 5-year overall survival while “apoptotic” patients (40%) with high TUNEL had 70% 5-year survival [HR95%CI=2.23 (1.33–3.80); p=0.0069]. Cox regression and machine learning algorithms including random forests built clinically useful models: a score to predict overall survival and a formula and nomogram to predict tumour phenotype. The distinct LUAD phenotypes were validated in TCGA and KMplotter data and showed prognostic power supplementary to IASLC TNM stage and WHO histologic classification.ConclusionsTwo molecular subtypes of LUAD exist and their identification provides important prognostic information.

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Co-Expression of Coxsackievirus/Adenovirus Receptors and Desmoglein 2 in Lung Adenocarcinoma: A Comprehensive Analysis of Bioinformatics and Tissue Microarrays

Journal of Clinical Medicine ◽

10.3390/jcm9113693 ◽

2020 ◽

Vol 9 (11) ◽

pp. 3693

Author(s):

Ching-Fu Weng ◽

Chi-Jung Huang ◽

Mei-Hsuan Wu ◽

Henry Hsin-Chung Lee ◽

Thai-Yen Ling

Keyword(s):

Overall Survival ◽

Survival Analysis ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Early Stage ◽

The Cancer Genome Atlas ◽

Cancer Tissue ◽

Mesenchymal Transition ◽

Kaplan Meier

Introduction: Coxsackievirus/adenovirus receptors (CARs) and desmoglein-2 (DSG2) are similar molecules to adenovirus-based vectors in the cell membrane. They have been found to be associated with lung epithelial cell tumorigenesis and can be useful markers in predicting survival outcome in lung adenocarcinoma (LUAD). Methods: A gene ontology enrichment analysis disclosed that DSG2 was highly correlated with CAR. Survival analysis was then performed on 262 samples from the Cancer Genome Atlas, forming “Stage 1A” or “Stage 1B”. We therefore analyzed a tissue microarray (TMA) comprised of 108 lung samples and an immunohistochemical assay. Computer counting software was used to calculate the H-score of the immune intensity. Cox regression and Kaplan–Meier analyses were used to determine the prognostic value. Results: CAR and DSG2 genes are highly co-expressed in early stage LUAD and associated with significantly poorer survival (p = 0.0046). TMA also showed that CAR/DSG2 expressions were altered in lung cancer tissue. CAR in the TMA was correlated with proliferation, apoptosis, and epithelial–mesenchymal transition (EMT), while DSG2 was associated with proliferation only. The Kaplan–Meier survival analysis revealed that CAR, DSG2, or a co-expression of CAR/DSG2 was associated with poorer overall survival. Conclusions: The co-expression of CAR/DSG2 predicted a worse overall survival in LUAD. CAR combined with DSG2 expression can predict prognosis.

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Use of a proliferation-based mRNA signature to predict outcome in early-stage non-small cell lung adenocarcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.7023 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. 7023-7023

Author(s):

Carmen Behrens ◽

Francesca Lombardi ◽

Susanne Wagner ◽

Junya Fujimoto ◽

Maria G Raso ◽

...

Keyword(s):

Cell Cycle ◽

Multivariate Analysis ◽

Lung Adenocarcinoma ◽

Quantitative Pcr ◽

Prognostic Value ◽

Early Stage ◽

Treatment Decisions ◽

Cancer Center ◽

Data Set ◽

Ffpe Samples

7023 Background: Adjuvant treatment of patients with early-stage lung adenocarcinoma is based on post-surgical pathological staging and patient performance status. Disparate outcomes within each staging group suggest that additional prognostic markers could improve our understanding of risk-benefit and potentially lead to better treatment decisions. A proliferation-based, mRNA expression profile was applied to public microarray data of surgically treated lung adenocarcinomas and a cohort of FFPE samples to test its potential prognostic utility. Methods: Public expression data (Director’s Consortium, DC) were derived from Affymetrix HG-U133A arrays. Clinical FFPE samples were assayed by quantitative PCR. A cell cycle progression (CCP) score was calculated from the expression average of 31 cell cycle genes normalized by 15 housekeeper genes. The prognostic value of the CCP score to predict stage I and II patient outcomes was evaluated by Cox proportional hazards analysis with disease-related death as the primary outcome measure. Results: In 256 DC cases, the CCP score was a significant predictor of death in univariate (p=0.0001) and multivariate analysis (p=0.001, HR 1.57, 95%CI 1.20-2.05) using age, stage, gender, smoking status and treatment as covariates. Similarly, in a second data set (GSE31210, n=204) the CCP score was highly associated with death (univariate, p=0.001; multivariate analysis, p=0.003, HR 1.81, 95% CI 1.24-2.66). Using quantitative PCR, the signature was applied to 381 FFPE samples with a median follow-up of 5 years collected at the MD Anderson Cancer Center and the European Institute for Oncology. In the presence of clinical covariates (as above and tumor size and pleural invasion), the CCP score remained the most significant predictor of death in univariate (p=0.0003) and multivariate analysis (p=0.007, HR 1.50, 95% CI 1.11-2.02). Conclusions: A 46 gene mRNA signature is a significant predictor of disease-related death in early-stage lung adenocarcinoma, providing independent prognostic value in the presence of clinical variables. This molecular predictor of cancer survival will be studied in additional cohorts for its ability to impact clinical treatment decisions.

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Transcriptional characterization of immune microenvironment and their prediction role for the prognosis of local advanced lung adenocarcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e20625 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e20625-e20625

Author(s):

Yuqiao Chen ◽

Xinying Shi ◽

Xue Song ◽

Lingling Gao ◽

Beibei Mao ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Survival Data ◽

Primary Tumor ◽

Antigen Processing ◽

Early Stage ◽

Locally Advanced ◽

Immune Microenvironment ◽

Immune Related Genes ◽

The Impact ◽

Local Advanced

e20625 Background: The resection of early stage NSCLC offers patients the best hope of a cure. However, the recurrence rate post-resection remains high. As the mechanisms involved in the process is still not clear due to the unavailability of accurate targets, our study was aimed to integrate the impact of different immune context present in lung adenocarcinoma (LUAD) microenvironment on patients’ prognosis. Methods: RNA targeted sequencing was performed on 24 primary tumor specimens from the resected local advanced LUADs . Transcripts of 395 immune related genes expressed in FFPE tumor samples were analyzed. The limma package was used to analyze the different expressed genes (DEGs) between patients with different prognosis. The gene set variance analysis (GSVA) analysis was performed to explore gene sets enrichment related to the prognosis (PFS, progression free survival) post-resection. Results: 23 DEGs were detected in primary tumor between the better (PFS > 18months, n = 12 ) and worse (PFS≤18months, n = 12 ) prognosis group. The combined prediction model containing MPO, IL-6, CXCR2, FCGR3B, ADGRE5 could identify the favorable prognosis of patients. GSVA and Log Rank test of survival data demonstrated that the antigen processing and lymphocyte activation pathway enrichment may associate with better prognosis (p = 0.01), whereas higher Neutrophils cell infiltration in primary tumor demonstrated a shorter PFS (p = 0.008). Conclusions: In LUAD, the immune related genes such as MPO, IL-6, CXCR2, FCGR3B, ADGRE5, can effectively profile the landscape of tumor immune microenvironment and predict the survival in early stage of lung adenocarcinoma. Accordingly immune pathways were correlated with prognosis of these patients. Our findings suggest that immune-related RNA expression pattern in locally advanced LUAD may provide a potential predictive marker for early recurrence after surgical resection.

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