An Immune-Related Prognostic Signature for Predicting Clinical Outcomes and Immune Landscape in IDH-Mutant Lower-Grade Gliomas

Background. IDH mutation is the most common in diffuse LGGs, correlated with a favorable prognosis. However, the IDH-mutant LGGs patients with poor prognoses need to be identified, and the potential mechanism leading to a worse outcome and treatment options needs to be investigated. Methods. A six-gene immune-related prognostic signature in IDH-mutant LGGs was constructed based on two public datasets and univariate, multivariate, and LASSO Cox regression analysis. Patients were divided into low- and high-risk groups based on the median risk score in the training and validation sets. We analyzed enriched pathways and immune cell infiltration, applying the GSEA and the immune evaluation algorithms. Results. Stratification and multivariate Cox analysis unveiled that the six-gene signature was an independent prognostic factor. The signature (0.806/0.795/0.822) showed a remarkable prognostic performance, with 1-, 3-, and 5-year time-dependent AUC, higher than for grade (0.612/0.638/0.649) and 1p19q codeletion status (0.606/0.658/0.676). High-risk patients had higher infiltrating immune cells. However, the specific immune escape was observed in the high-risk group after immune activation, owing to increasing immunosuppressive cells, inhibitory cytokines, and immune checkpoint molecules. Moreover, a novel nomogram model was developed to evaluate the survival in IDH-mutant LGGs patients. Conclusion. The six-gene signature could be a promising prognostic biomarker, which is promising to promote individual therapy and improve the clinical outcomes of IDH-mutant gliomas. The study also refined the current classification system of IDH-mutant gliomas, classifying patients into two subtypes with distinct immunophenotypes and overall survival.

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Comprehensive Analysis of the Prognostic Significance of Hsa-miR-100-5p and Its Related Gene Signature in Stomach Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.736274 ◽

2021 ◽

Vol 9 ◽

Author(s):

Gaoming Wang ◽

Ludi Yang ◽

Miao Hu ◽

Renhao Hu ◽

Yongkun Wang ◽

...

Keyword(s):

High Risk ◽

Cox Regression ◽

Prognostic Significance ◽

Tumor Development ◽

Risk Groups ◽

Gene Signature ◽

Risk Scores ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Stomach Adenocarcinoma

Stomach adenocarcinoma (STAD) is one of the most common cancers in the world. However, the prognosis of STAD remains poor, and the therapeutic effect of chemotherapy and immunotherapy varies from person to person. MicroRNAs (miRNAs) play vital roles in tumor development and metastasis and can be used for cancer diagnosis and prognosis. In this study, hsa-miR-100-5p was identified as the only dysregulated miRNA in STAD samples through an analysis of three miRNA expression matrices. A weighted gene co-expression network analysis (WGCNA) was performed to select hsa-miR-100-5p-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a miR-100-5p-related prognostic signature. Kaplan–Meier analyses, nomograms, and univariate and multivariate Cox regression analyses were used to evaluate the prognostic signature, which was subsequently identified as an independent risk factor for STAD patients. We investigated the tumor immune environment between low- and high-risk groups and found that, among component types, M2 macrophages contributed the most to the difference between these groups. A drug sensitivity analysis suggested that patients with high-risk scores may be more sensitive to docetaxel and cisplatin chemotherapy and that patients in the low-risk group may be more likely to benefit from immunotherapy. Finally, external cohorts were evaluated to validate the robustness of the prognostic signature. In summary, this study may provide new ideas for developing more individualized therapeutic strategies for STAD patients.

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Reverse engineering a predictive signature characterized by proliferation, DNA damage, and immune escape from stage I lung adenocarcinoma recurrence

Acta Biochimica et Biophysica Sinica ◽

10.1093/abbs/gmaa036 ◽

2020 ◽

Vol 52 (6) ◽

pp. 638-653

Author(s):

Jiannan Yao ◽

Xinying Xue ◽

Dongfeng Qu ◽

C Benedikt Westphalen ◽

Yang Ge ◽

...

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Immune Escape ◽

Early Stage ◽

Gene Signature ◽

Response To Therapy ◽

Stage I ◽

Cox Regression Analysis ◽

Patients At Risk

Abstract Identifying early-stage cancer patients at risk for progression is a major goal of biomarker research. This report describes a novel 19-gene signature (19-GCS) that predicts stage I lung adenocarcinoma (LAC) recurrence and response to therapy and performs comparably in pancreatic adenocarcinoma (PAC), which shares LAC molecular traits. Kaplan–Meier, Cox regression, and cross-validation analyses were used to build the signature from training, test, and validation sets comprising 831 stage I LAC transcriptomes from multiple independent data sets. A statistical analysis was performed using the R language. Pathway and gene set enrichment were used to identify underlying mechanisms. 19-GCS strongly predicts overall survival and recurrence-free survival in stage I LAC (P=0.002 and P<0.001, respectively) and in stage I–II PAC (P<0.0001 and P<0.0005, respectively). A multivariate cox regression analysis demonstrated the independence of 19-GCS from significant clinical factors. Pathway analyses revealed that 19-GCS high-risk LAC and PAC tumors are characterized by increased proliferation, enhanced stemness, DNA repair deficiency, and compromised MHC class I and II antigen presentation along with decreased immune infiltration. Importantly, high-risk LAC patients do not appear to benefit from adjuvant cisplatin while PAC patients derive additional benefit from FOLFIRINOX compared with gemcitabine-based regimens. When validated prospectively, this proof-of-concept biomarker may contribute to tailoring treatment, recurrence reduction, and survival improvements in early-stage lung and pancreatic cancers.

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A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.742578 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chunmei Zhu ◽

Shuyuan Zhang ◽

Di Liu ◽

Qingqing Wang ◽

Ningning Yang ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Dna Methylation ◽

High Risk ◽

Cox Regression ◽

Methylation Status ◽

Gene Signature ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

The Impact

Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC).Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database.Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset.Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.

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Development of a miR-3648-Targeted Immune-Related 4-Gene Signature as a Prognostic Biomarker in Esophageal Adenocarcinoma Based on ehe WGCNA Algorithm

10.21203/rs.3.rs-549122/v1 ◽

2021 ◽

Author(s):

Donglei Zhang ◽

Hang Yin ◽

Ping Xu ◽

Xiaozhe Qian

Keyword(s):

Esophageal Adenocarcinoma ◽

Immune Cell ◽

Cox Regression ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Normal Tissues

Abstract Background: Esophageal adenocarcinoma (EA) has a poor prognosis and is a typical immunogenic malignant tumor. Abnormal expression of miR-3648 has been reported in EA, but its value in prognosis prediction and immune cell infiltration imbalance mediation is still unknown. We aimed to mine immune-related genes (IRGs) targeted by miR-3648 and construct a multigene signature to improve the prognostic prediction of EA.Methods: The gene expression data of EA tumor or normal tissues from The Cancer Genome Atlas (TCGA) database and GTEx database were downloaded. Weighted gene coexpression network analysis (WGCNA), the CIBERSORT algorithm and Cox regression analysis were applied to identify IRGs and to construct a prognostic signature and nomogram.Results: miR-3648 was obviously highly expressed in EA tumor tissues and was correlated with patient survival time [hazard ratio (HR) = 1.28, 95% confidence interval (CI): 1.09-1.49, p = 0.002]. A total of 70 miR-3648-targeted genes related to immune cell infiltration were identified, and a novel 4-gene signature (C10orf55, DLL4, PANX2 and NKAIN1) was established. The prognostic signature-based risk score has superior capability to predict overall survival (AUC = 0.740 for 1 year; AUC = 0.717 for 3 years; AUC = 0.622 for 5 years). A higher score was indicative of a poorer prognosis than a lower score (HR = 1.69, 95% CI: 1.08-2.64, p = 0.20, adjusted by TNM stage).Conclusion: miR-3648 might play a crucial role in the progression of EA. The novel miR-3648-targeted immune-related 4-gene signature is expected to become a potential prognostic marker in EA.

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A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

10.21203/rs.3.rs-335140/v1 ◽

2021 ◽

Author(s):

Yanjia Hu ◽

Jing Zhang ◽

Jing Chen

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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Analysis of DNA damage response gene signature for prognosis prediction of esophageal squamous cell carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e16073 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e16073-e16073

Author(s):

Weitao Zhuang ◽

Xiao-song Ben ◽

Dan Tian ◽

Zihao Zhou ◽

Gang Chen ◽

...

Keyword(s):

High Risk ◽

Dna Damage Response ◽

Squamous Cell ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Prognostic Signature ◽

Training Set ◽

Prognosis Prediction ◽

Damage Response

e16073 Background: Esophageal squamous cell cancer (ESCC) is a malignant tumor with a poor 5-year relative survival. A prognosis prediction signature associated with DNA Damage Response (DDR) genes in ESCC was explored in this study. Methods: The clinical and gene expression profiles of ESCC patients were downloaded from the GEO and TCGA database. Univariate Cox regression and 1000 iterations of 10-fold cross-validation of LASSO Cox regression with binomial deviance minimization criteria were used to identify DDR genes as potential object and a prognostic signature for ESCC survival prediction, followed by validation of the signature via TCGA cohort and identification of independent prognostic predictors. A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: A signature of 8 DDR genes were constructed as being significantly associated with overall survival (OS) among patients with esophageal squamous cell carcinoma. The pronostic signature stratified ESCC patients into low- vs high-risk groups in terms of OS in the training set, testing set and the validation cohorts, and remained as an independent prognostic factor in multivariate analyses (hazard ratio (HR) in training set, 0.17 [95% CI, 0.09-0.35; P < 0 .001], HR in testing set, 0.38 [95% CI, 0.16-0.93; P = 0.029], HR in discovery cohort, 0.171 [95% CI, 0.03-0.48; P < 0 .001]) after adjusting for clinicopathological factors. The 8-DDR gene signature achieved a higher accuracy (C-index, 0.69; AUCs for 1-, 3- and 5-year OS, 0.74, 0.77 and 0.76, respectively) than 7 previously reported multigene signatures (C-index range, 0.53 to 0.60; AUCs range, 0.55to 0.66, 0.54 to 0.64 and 0.62 to 0.66, respectively) for estimation of survival in comparable cohorts. A nomogram incorporating tumor location, grade, adjuvant therapy and signature-based risk group showed better predictive performance for 1- and 3- year survival than for 5 year survival. Moreover, GSEA revealed that the DNA repair was more prominently enriched in the high-risk group while the low-risk group had not enrichment of any process (P > 0.05 for all). Conclusions: Taken together, our study identified 8 DDR genes related to the prognosis of ESCC patients, and constructed a robust prognostic signature to effectively stratify ESCC patients with different survival rates, which may help recognize high-risk patients potentially benefiting from more aggressive treatment.

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Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

Cancer Cell International ◽

10.1186/s12935-021-02231-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Pu Wu ◽

Jinyuan Shi ◽

Wei Sun ◽

Hao Zhang

Keyword(s):

Thyroid Cancer ◽

Risk Score ◽

Prognostic Model ◽

Immune Cell ◽

Cox Regression ◽

Checkpoint Inhibitors ◽

Risk Groups ◽

Low Risk ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

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Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

10.21203/rs.3.rs-524410/v1 ◽

2021 ◽

Author(s):

Jixiang Cao ◽

Xi Chen ◽

Guang Lu ◽

Haowei Wang ◽

Xinyu Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Tumor Microenvironment ◽

Immune Cells ◽

Prognostic Model ◽

Cox Regression ◽

Gene Signature ◽

Risk Scores ◽

Tumor Infiltration ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.637743 ◽

2021 ◽

Vol 8 ◽

Author(s):

Mingqin Ge ◽

Jie Niu ◽

Ping Hu ◽

Aihua Tong ◽

Yan Dai ◽

...

Keyword(s):

Thyroid Cancer ◽

High Risk ◽

Risk Score ◽

Immune Cell ◽

Cox Regression ◽

Low Risk ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Immune Microenvironment ◽

Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

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Development and validation of an immune-related prognostic signature in cervical cancer

10.21203/rs.3.rs-227250/v1 ◽

2021 ◽

Author(s):

Rongjia Su ◽

Chengwen Jin ◽

Hualei Bu ◽

Xiaoyun Wang ◽

Menghua Kuang ◽

...

Keyword(s):

Cervical Cancer ◽

Cox Regression ◽

Clinical Information ◽

Risk Groups ◽

Gene Signature ◽

Cancer Prognosis ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Gynecological Malignancy ◽

Immune Infiltration

Abstract Background Cervical cancer is the fourth most frequently gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods Raw data and clinical information of cervical cancer samples were download from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results There were 8 immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing PD-L1 expression and PD-1 checkpoint pathway differences between high risk and low risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusion The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

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