Development and Validation of a Novel M6A-related LncRNA Prognostic Signature Associated with Immune Microenvironment for Ovarian Serous Cystadenocarcinoma

2021 ◽  
Author(s):  
Rui Geng ◽  
Tian Chen ◽  
Zihang Zhong ◽  
Senmiao Ni ◽  
Jianling Bai ◽  
...  
Keyword(s):  
Immune Modulation ◽  
Cox Regression ◽  
Great Influence ◽  
Clinical Samples ◽  
Consensus Clustering ◽  
Prognostic Signature ◽  
Cancer Stemness ◽  
Gynecological Malignancy ◽  
Pan Cancer ◽  
Geo Database

Abstract Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have great influence on OV development and patients' immunotherapy response. Here, we decided to establish a reliable signature in the light of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis in the light of TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model in the light of the mRLs. K-M analysis, PCA, GSEA, and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune features comparison, consensus clustering analysis, and Pan-cancer analysis.Results: A prognostic signature comprising four mRLs: WAC-AS1, LINC00997, DNM3OS, and FOXN3-AS1, was constructed and verified for OV according to TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, people can identify patients more effectively. All the sample were assigned into two clusters, and the clusters had different overall survival, clinical features, and tumor microenvironment. Finally, Pan-cancer analysis further demonstrated the four mRLs significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusion: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.

scholarly journals A Novel Cancer Stemness-Related Signature for Predicting Prognosis in Patients with Colon Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Wei Wang ◽  
Congrong Xu ◽  
Yan Ren ◽  
Shiwei Wang ◽  
Chunli Liao ◽  
...  
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Colon Adenocarcinoma ◽  
Prognostic Signature ◽  
Cancer Stemness ◽  
Mrna Expression Data ◽  
The Relationship ◽  
Differently Expressed Genes ◽  
Geo Database

Objective. To explore the cancer stemness features and develop a novel cancer stemness-related prognostic signature for colon adenocarcinoma (COAD). Methods. We downloaded the mRNA expression data and clinical data of COAD from TCGA database and GEO database. Stemness index, mRNAsi, was utilized to investigate cancer stemness features. Weighted gene coexpression network analysis (WGCNA) was used to identify cancer stemness-related genes. Univariate and multivariate Cox regression analyses were applied to construct a prognostic risk cancer stemness-related signature. We then performed internal and external validation. The relationship between cancer stemness and COAD immune microenvironment was investigated. Results. COAD patients with higher mRNAsi score or EREG-mRNAsi score have significant longer overall survival (OS). We identified 483 differently expressed genes (DEGs) between the high and low mRNAsi score groups. We developed a cancer stemness-related signature using fifteen genes (including RAB31, COL6A3, COL5A2, CCDC80, ADAM12, VGLL3, ECM2, POSTN, DPYSL3, PCDH7, CRISPLD2, COLEC12, NRP2, ISLR, and CCDC8) for prognosis prediction of COAD. Low-risk score was associated with significantly preferable OS in comparison with high-risk score, and the area under the ROC curve (AUC) for OS prediction was 0.705. The prognostic signature was an independent predictor for OS of COAD. Macrophages, mast cells, and T helper cells were the vital infiltration immune cells, and APC costimulation and type II IFN response were the vital immune pathways in COAD. Conclusions. We developed and validated a novel cancer stemness-related prognostic signature for COAD, which would contribute to understanding of molecular mechanism in COAD.

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

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.

scholarly journals Comprehensive Analysis to Identify SPP1 as a Prognostic Biomarker in Cervical Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Kaidi Zhao ◽  
Zhou Ma ◽  
Wei Zhang
Keyword(s):  
Cervical Cancer ◽  
Roc Curve ◽  
Immune Cell ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
R Package ◽  
Cancer Data ◽  
Immune Infiltration ◽  
Pan Cancer ◽  
Geo Database

Background:SPP1, secreted phosphoprotein 1, is a member of the small integrin-binding ligand N-linked glycoprotein (SIBLING) family. Previous studies have proven SPP1 overexpressed in a variety of cancers and can be identified as a prognostic factor, while no study has explored the function and carcinogenic mechanism of SPP1 in cervical cancer.Methods: We aimed to demonstrate the relationship between SPP1 expression and pan-cancer using The Cancer Genome Atlas (TCGA) database. Next, we validated SPP1 expression of cervical cancer in the Gene Expression Omnibus (GEO) database, including GSE7803, GSE63514, and GSE9750. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of SPP1 as a differentiating factor by the area under curve (AUC) score. Cox regression and logistic regression were performed to evaluate factors associated with prognosis. The SPP1-binding protein network was built by the STRING tool. Enrichment analysis by the R package clusterProfiler was used to explore potential function of SPP1. The single-sample GSEA (ssGSEA) method from the R package GSVA and TIMER database were used to investigate the association between the immune infiltration level and SPP1 expression in cervical cancer.Results: Pan-cancer data analysis showed that SPP1 expression was higher in most cancer types, including cervical cancer, and we got the same result in the GEO database. The ROC curve suggested that SPP1 could be a potential diagnostic biomarker (AUC = 0.877). High SPP1 expression was associated with poorer overall survival (OS) (P = 0.032). Further enrichment and immune infiltration analysis revealed that high SPP1 expression was correlated with regulating the infiltration level of neutrophil cells and some immune cell types, including macrophage and DC.Conclusion:SPP1 expression was higher in cervical cancer tissues than in normal cervical epithelial tissues. It was significantly associated with poor prognosis and immune cell infiltration. Thus, SPP1 may become a promising prognostic biomarker for cervical cancer patients.

scholarly journals A Novel Signature Derived from Metabolism-Related Genes GPT and SMS to Predict Prognosis of Laryngeal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Yujie Shen ◽  
Qiang Huang ◽  
Yifan Zhang ◽  
Chi-Yao Hsueh ◽  
Liang Zhou
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Cox Regression ◽  
External Validation ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Clinical Samples ◽  
Prognostic Signature

Abstract Background A growing body of evidence has suggested the involvement of metabolism in the occurrence and development of tumors. But the link between metabolism and laryngeal squamous cell carcinoma (LSCC) has rarely been reported. This study seeks to understand and explain the role of metabolic biomarkers in predicting the prognosis of LSCC. Methods We identified the differentially expressed metabolism-related genes (MRGs) through RNA-seq data of TCGA (The Cancer Genome Atlas) and GSEA (Gene set enrichment analysis). After the screening of protein-protein interaction (PPI), hub MRGs were analyzed by least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a prognostic signature. Kaplan–Meier survival analysis and the receiver operating characteristic (ROC) was applied to verify the effectiveness of the prognostic signature in four cohorts (TCGA cohort, GSE27020 cohort, TCGA-sub1 cohort and TCGA-sub2 cohort). The expressions of the hub MRGs in cell lines and clinical samples were verified by quantitative reverse transcriptase PCR (qRT-PCR). The immunofluorescence staining of the tissue microarray (TMA) was carried out to further verify the reliability and validity of the prognostic signature. Cox regression analysis was then used to screen for independent prognostic factors of LSCC and a nomogram was constructed based on the results. Results Among the 180 differentially expressed MRGs, 14 prognostic MRGs were identified. A prognostic signature based on two MRGs (GPT and SMS) was then constructed and verified via internal and external validation cohorts. Compared to the adjacent normal tissues, SMS expression was higher while GPT expression was lower in LSCC tissues, indicating poorer outcomes. The risk score proved the prognostic signature as an independent risk factor for LSCC in both internal and external validation cohorts. A nomogram based on these results was developed for clinical application. Conclusions Differentially expressed MRGs were found and proven to be related to the prognosis of LSCC. We constructed a novel prognostic signature based on MRGs in LSCC for the first time and verified via different cohorts from both databases and clinical samples. A nomogram based on this prognostic signature was developed.

scholarly journals Construction of a Ferroptosis-Related Long Non-coding RNA Prognostic Signature and Competing Endogenous RNA Network in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiang Fei ◽  
Congli Hu ◽  
Xinyu Wang ◽  
Chaojing Lu ◽  
Hezhong Chen ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Pearson Correlation ◽  
Low Risk ◽  
Clinical Samples ◽  
Prognostic Signature ◽  
Competing Endogenous Rna

Ferroptosis-related genes play an important role in the progression of lung adenocarcinoma (LUAD). However, the potential function of ferroptosis-related lncRNAs in LUAD has not been fully elucidated. Thus, to explore the potential role of ferroptosis-related lncRNAs in LUAD, the transcriptome RNA-seq data and corresponding clinical data of LUAD were downloaded from the TCGA dataset. Pearson correlation was used to mine ferroptosis-related lncRNAs. Differential expression and univariate Cox analysis were performed to screen prognosis related lncRNAs. A ferroptosis-related lncRNA prognostic signature (FLPS), which included six ferroptosis-related lncRNAs, was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were divided into a high risk-score group and low risk-score group by the median risk score. Receiver operating characteristic (ROC) curves, principal component analysis (PCA), and univariate and multivariate Cox regression were performed to confirm the validity of FLPS. Enrichment analysis showed that the biological processes, pathways and markers associated with malignant tumors were more common in high-risk subgroups. There were significant differences in immune microenvironment and immune cells between high- and low-risk groups. Then, a nomogram was constructed. We further investigated the relationship between six ferroptosis-related lncRNAs and tumor microenvironment and tumor stemness. A competing endogenous RNA (ceRNA) network was established based on the six ferroptosis-related lncRNAs. Finally, we detected the expression levels of ferroptosis-related lncRNAs in clinical samples through quantitative real-time polymerase chain reaction assay (qRT-PCR). In conclusion, we identified the prognostic ferroptosis-related lncRNAs in LUAD and constructed a prognostic signature which provided a new strategy for the evaluation and prediction of prognosis in LUAD.

scholarly journals Development of a novel transcription factors-related prognostic signature for serous ovarian cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
He Li ◽  
Nayiyuan Wu ◽  
Zhao-Yi Liu ◽  
Yong-Chang Chen ◽  
Quan Cheng ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Transcription Factors ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Low Risk ◽  
Consensus Clustering ◽  
Serous Ovarian Cancer ◽  
Prognostic Signature ◽  
Significant Difference

AbstractGrowing evidence suggest that transcription factors (TFs) play vital roles in serous ovarian cancer (SOC). In the present study, TFs mRNA expression profiles of 564 SOC subjects in the TCGA database, and 70 SOC subjects in the GEO database were screened. A 17-TFs related prognostic signature was constructed using lasso cox regression and validated in the TCGA and GEO cohorts. Consensus clustering analysis was applied to establish a cluster model. The 17-TFs related prognostic signature, risk score and cluster models were effective at accurately distinguishing the overall survival of SOC. Analysis of genomic alterations were used to elaborate on the association between the 17-TFs related prognostic signature and genomic aberrations. The GSEA assay results suggested that there was a significant difference in the inflammatory and immune response pathways between the high-risk and low-risk score groups. The potential immune infiltration, immunotherapy, and chemotherapy responses were analyzed due to the significant difference in the regulation of lymphocyte migration and T cell-mediated cytotoxicity between the two groups. The results indicated that patients with low-risk score were more likely to respond anti-PD-1, etoposide, paclitaxel, and veliparib but not to gemcitabine, doxorubicin, docetaxel, and cisplatin. Also, the prognostic nomogram model revealed that the risk score was a good prognostic indicator for SOC patients. In conclusion, we explored the prognostic values of TFs in SOC and developed a 17-TFs related prognostic signature to predict the survival of SOC patients.

Identification of Iron Metabolism-Related Genes Signature and Novel Role of FLVCR1 in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Jianhui Chen ◽  
Chuan HU ◽  
Reguang Pan ◽  
Xuedan Du ◽  
Haotian Fu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Iron Metabolism ◽  
Clustering Analysis ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Prognostic Signature

Abstract Background: Hepatocellular carcinoma (HCC) is the main and highly malignant histological subtype of liver cancer. We tried to construct a novel signature with iron metabolism-related genes to provide new therapeutic targets and improve the prognosis for HCC patients.Methods: The gene expression data of 70 iron metabolism-related genes and its relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Consensus clustering analysis was performed to determine clusters of HCC patients with different OS. Cox regression and LASSO regression analyses were used to establish a prognostic signature. Receiver operating characteristic (ROC) and Kaplan–Meier analyses were carried out to examine the predicated performance of the signature.Results: Consensus clustering analysis determined two clusters of HCC patients with different OS(p<0.01), TNM stage(p<0.05) and pathological grade(p<0.05). A nine-gene prognostic signature established with iron metabolism-related genes can independently predicate the prognostic of HCC patients. The ROC curves showed a great performance of the signature. In addition, FLVCR1, a hub gene with the highest mutation frequency in our signature, showed the significantly prognostic value in HCC patients. High FLVCR1 expression was significantly associated with poor prognosis and aggressive progression in HCC patients. The promoter methylation level of FLVCR1 was lower in HCC samples with aggressive progression status. The FLVCR1 expression was positively correlated with the infiltration level of B cell, CD4+ T cell, macrophage, neutrophil and dendritic cell. Conclusion: Our study first established a signature related to iron metabolism and identified FLVCR1 as a potential therapeutic target. These findings provided more treatment strategies for HCC patients.

scholarly journals Development of a novel transcription factors-related prognostic signature for serous ovarian cancer

2020 ◽  
Author(s):  
He Li ◽  
Zhao-Yi Liu ◽  
Nayiyuan Wu ◽  
Yong-Chang Chen ◽  
Quan Cheng ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Transcription Factors ◽  
Risk Score ◽  
Clustering Analysis ◽  
Cox Regression ◽  
Low Risk ◽  
Consensus Clustering ◽  
Serous Ovarian Cancer ◽  
Prognostic Signature ◽  
Significant Difference

Abstract BackgroundOvarian cancer (OC) is one of the most lethal gynecological cancer globally. Serous ovarian cancer (SOC) is most common pathological type of ovarian cancer. Growing evidence suggests that transcription factors (TFs) play vital roles in serous ovarian cancer (SOC). In the present study, we aimed to develop a transcription factors-related prognostic signature to better predict the survival in patients with SOC.Materials and methodsThe TFs mRNA expression profiles of 564 SOC subjects in the TCGA database, and 70 SOC subjects in the GEO database were screened. Lasso cox regression and consensus clustering analysis were utilized to construct a novel TFs related signature and cluster model, respectively. Genomic alternative analysis was used to elaborate on the association between the TFs related prognostic signature and genomic aberrations. GSEA analysis was applied to identify the biological functional difference between high risk-score group and low-risk score group.ResultsA 17-TFs related prognostic signature was constructed using lasso cox regression and validated in the TCGA and GEO cohorts. Consensus clustering analysis was applied to establish a cluster model. The 17-TFs related prognostic signature, risk score, and cluster models were effective at accurately distinguishing the prognosis of SOC. The GSEA assay results suggested that there was a significant difference in the inflammatory and immune response pathways between the high-risk and low-risk score groups. Immune infiltration, immunotherapy, and chemotherapy responses were analyzed due to the significant difference in the regulation of lymphocyte migration and T cell-mediated cytotoxicity between the two groups; indicating that patients with low-risk score were more likely to respond anti-PD-1, etoposide, paclitaxel, and veliparib but not to gemcitabine, doxorubicin, docetaxel, and cisplatin. Also, the prognostic nomogram model revealed that the risk score was a good prognostic indicator for SOC patients.ConclusionsIn conclusion, we explored the prognostic values of TFs in SOC and developed a 17-TFs related prognostic signature to predict the survival of SOC patients.

scholarly journals Development and Validation of an Immune-related Prognostic Signature in Cervical Cancer

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. Conclusions: The 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Furthermore, the 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

scholarly journals Development and validation of an immune-related prognostic signature for ovarian cancer based on weighted gene co-expression network analysis

2020 ◽  
Author(s):  
Yuanyuan An ◽  
Qing Yang
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Network Analysis ◽  
Immune Responses ◽  
Cancer Patients ◽  
Cox Regression ◽  
M2 Macrophage ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Geo Database

Abstract Background Ovarian cancer is one of the most lethal diseases of women. The prognosis of ovarian cancer patients was closely correlated with immune cell expression and immune responses. Therefore, it is important to identify a robust prognostic signature, which not only correlates with prognoses but also with immune responses in ovarian cancer, thus, providing immune-related patient therapies. Methods Weighted gene co-expression network analysis (WGCNA) was used to identify candidate genes correlated with ovarian cancer prognoses. Univariate and multivariate cox regression analyses were used to construct the prognostic signature. The Kaplan-Meier method was used to predict survival, and the immune-related bioinformatics analysis was performed using R software. The relationship between the signature and clinical parameters was analyzed with GraphPad Prism 7 and SPSS software. Results Gene expression from The Cancer Genome Atlas dataset was used to perform the WGCNA analysis, and identified candidate prognostic-related genes in patients with ovarian cancer. According to the Cox regression analysis, the prognostic signature was constructed, which divided patients into two groups. The high-risk group showed the least favorable prognosis. Three independent cohorts from the Gene Expression Omnibus (GEO) database were used for the validation studies. According to the immune analyses, the GEO database signatures were significantly correlated with the immune statuses of ovarian cancer patients. By analyzing the combination of the prognostic signature and total mutational burden (TMB), ovarian cancer patients were divided into four groups. In these groups, memory B cell, resting memory CD4 T cell, M2 macrophage, resting mast cell and neutrophil were found significant distinctions among these groups. Conclusions This novel signature predicted the prognosis of ovarian cancer patients precisely and independently and showed significant correlations with immune responses. Therefore, this prognostic signature might could indicate targeted immunotherapies for ovarian cancer patients.

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