Identification of a Hypoxia-Related LncRNA Signature for the Prognosis of Colorectal Cancer

Abstract Background:Colorectal cancer (CRC), the commonly seen malignancy, ranks 3rd place among the causes of cancer-associated mortality. As suggested by more and more studies, long non-coding RNAs (lncRNAs)have been considered as prognostic biomarkers for CRC. But the significance of hypoxic lncRNAs in predicting CRC prognosis remains unclear.Methods:The gene expressed profiles for CRC cases were obtained based on the Cancer Genome Atlas (TCGA) and applied to estimate the hypoxia score using a single-sample gene set enrichment analysis (ssGSEA) algorithm. Overall survival (OS) of the high- and low-hypoxia score group was analyzed by the Kaplan–Meier (KM) plot. To identify differentially expressed lncRNAs (DELs) between two hypoxia score groups, this study carried out differential expression analysis, and then further integrated with the DELs between controls and CRC patients to generate the hypoxia-related lncRNAs for CRC. Besides, prognostic lncRNAs were screened by the univariate Cox regression, which was later utilized for constructing the prognosis nomogram for CRC by adopting the least absolute shrinkage and selection operator (LASSO) algorithm. In addition, both accuracy and specificity of the constructed prognostic signature were detected through the receiver operating characteristic (ROC) analysis. Moreover, our constructed prognosis signature also was validated in the internal testing test. This study operated gene set enrichment analysis (GSEA) for exploring potential biological functions associated with the prognostic signature. Finally, the ceRNA network of the prognostic lncRNAs was constructed.Results:Among 2299 hypoxia-related lncRNAs of CRC in total, LINC00327, LINC00163, LINC00174, SYNPR-AS1, and MIR31HG were identified as prognostic lncRNAs by the univariate Cox regression and adopted for constructing the prognosis signature for CRC. ROC analysis showed the predictive power and accuracy of the prognostic signature. Additionally, the GSEA revealed that ECM-receptor interaction, PI3K-Akt pathway, phagosome, and Hippo pathway were mostly associated with the high-risk group. 352 miRNAs-mRNAs pairs and 177 lncRNAs-miRNAs were predicted.Conclusion:To conclude, we identified 5 hypoxia-related lncRNAs to establish an accurate prognostic signature for CRC, providing important prognostic markers and therapeutic targets.

Download Full-text

Identification of a Hypoxia - Related lncRNA Signature For The Prognosis of Colorectal Cancer

10.21203/rs.3.rs-757504/v1 ◽

2021 ◽

Author(s):

HUA HUANG ◽

Shanshan Xu ◽

Youran Li ◽

Yunfei Gu ◽

Lijiang Ji

Keyword(s):

Colorectal Cancer ◽

Roc Analysis ◽

Cox Regression ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Receptor Interaction ◽

Prognostic Signature ◽

Gene Set Enrichment ◽

Gene Set

Abstract Background: Colorectal cancer (CRC), the commonly seen malignancy, ranks the 3rd place among the causes of cancer-associated mortality. As suggested by more and more studies, long coding RNAs (lncRNAs) have been considered as prognostic biomarkers for CRC. But the significance of hypoxic lncRNAs in predicting CRC prognosis remains unclear.Methods: The gene expressed profiles for CRC cases were obtained based on the Cancer Genome Atlas (TCGA) and applied to estimate the hypoxia score using a single-sample gene set enrichment analysis (ssGSEA) algorithm. Overall survival (OS) of high- and low-hypoxia score group was analyzed by the Kaplan–Meier (KM) plot. To identify differentially expressed lncRNAs (DELs) between two hypoxia score groups, this study carried out differential expression analysis, and then further integrated with the DELs between controls and CRC patients to generate the hypoxia-related lncRNAs for CRC. Besides, prognostic lncRNAs were screened by the univariate Cox regression, which were later utilized for constructing the prognosis nomogram for CRC by adopting the least absolute shrinkage and selection operator (LASSO) algorithm. In addition, both accuracy and specificity of the constructed prognostic signature were detected through the receiver operating characteristic (ROC) analysis. Moreover, our constructed prognosis signature also was validated in the internal testing test. This study operated gene set enrichment analysis (GSEA) for exploring potential biological functions associated with the prognostic signature. Finally, the ceRNA network of the prognostic lncRNAs was constructed.Results: Among 2299 hypoxia-related lncRNAs of CRC in total, LINC00327, LINC00163, LINC00174, SYNPR-AS1, and MIR31HG were identified as prognostic lncRNAs by the univariate Cox regression, and adopted for constructing the prognosis signature for CRC. ROC analysis showed the predictive power and accuracy of the prognostic signature. Additionally, the GSEA revealed that ECM-receptor interaction, PI3K-Akt pathway, phagosome, and Hippo pathway were mostly associated with the high-risk group. 352 miRNAs-mRNAs pairs and 177 lncRNAs-miRNAs were predicted.Conclusion: To conclude , we identified 5 hypoxia-related lncRNAs to establish an accurate prognostic signature for CRC, providing important prognostic markers and therapeutic target.

Download Full-text

Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.618976 ◽

2021 ◽

Vol 11 ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

Download Full-text

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.

Download Full-text

Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.681277 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guomin Wu ◽

Qihao Wang ◽

Ting Zhu ◽

Linhai Fu ◽

Zhupeng Li ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Clinical Features ◽

Cox Regression ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

Download Full-text

Identification of Seven Novel Ferroptosis-Related Long Non - Coding RNA Signatures as a Diagnostic Biomarker for Acute Myeloid Leukemia

10.21203/rs.3.rs-502041/v1 ◽

2021 ◽

Author(s):

Zhiyuan Zheng ◽

Wei Wu ◽

Zehang Lin ◽

Shuhan Liu ◽

Qiaoqian Chen ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Risk Score ◽

Myeloid Leukemia ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Kaplan Meier ◽

Acute Myeloid

Abstract Background: Ferroptosis is a newly discovered type of programmed cell death that participates in the biological processes of various cancers. However, the mechanism by which ferroptosis modulates acute myeloid leukemia (AML) remains unclear. This study aimed to investigate the role of ferroptosis-related long non-coding RNAs (lncRNAs) in AML and establish a corresponding prognostic model.Methods: RNA-sequencing data and clinicopathological characteristics were obtained from The Cancer Genome Atlas database, and ferroptosis-related genes were obtained from the FerrDb database. The “limma” R package, Cox regression, and the least absolute shrinkage and selection operator were used to determine the ferroptosis-related lncRNA signature with the lowest Akaike information criteria (AIC). The risk score of ferroptosis-related lncRNAs was calculated and patients with AML were divided into high- and low-risk groups based on the median risk score. The Kaplan-Meier curve and Cox regression were used to evaluate the prognostic value of the risk score. Finally, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the biological functions of the ferroptosis-related lncRNAs.Results: Seven ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan-Meier and Cox regression analyses confirmed that risk scores were independent prognostic predictors of AML in both the training and validation groups (All P < 0.05). In addition, the area under the curve (AUC) analysis confirmed that the signatures had a good predictive ability for the prognosis of AML. GSEA and ssGSEA showed that the seven ferroptosis-related lncRNAs were related to glutathione metabolism and tumor immunity.Conclusions: In this study, seven novel ferroptosis-related lncRNA signatures (AP001266.2, AC133961.1, AF064858.3, AC007383.2, AC008906.1, AC026771.1, and KIF26B-AS1) were established. These signatures were shown to accurately predict the prognosis of AML, which would provide new insights into strategies for the development of new AML therapies.

Download Full-text

PVT1 is a prognostic marker associated with immune invasion of bladder urothelial carcinoma

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022009 ◽

2021 ◽

Vol 19 (1) ◽

pp. 169-190

Author(s):

Peiyuan Li ◽

◽

Gangjie Qiao ◽

Jian Lu ◽

Wenbin Ji ◽

...

Keyword(s):

Urothelial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Gene Set ◽

Bladder Urothelial Carcinoma

<abstract> <p>Plasmacytoma variant translocation 1 (PVT1) is involved in multiple signaling pathways and plays an important regulatory role in a variety of malignant tumors. However, its role in the prognosis and immune invasion of bladder urothelial carcinoma (BLCA) remains unclear. This study investigated the expression of PVT1 in tumor tissue and its relationship with immune invasion, and determined its prognostic role in patients with BLCA. Patients were identified from the cancer genome atlas (TCGA). The enrichment pathway and function of PVT1 were explained by gene ontology (GO) term analysis, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA), and the degree of immune cell infiltration was quantified. Kaplan–Meier analysis and Cox regression were used to analyze the correlation between PVT1 and survival rate. PVT1-high BLCA patients had a lower 10-year disease-specific survival (DSS P < 0.05) and overall survival (OS P < 0.05). Multivariate Cox regression analysis showed that PVT1 (high vs. low) (P = 0.004) was an independent prognostic factor. A nomogram was used to predict the effect of PVT1 on the prognosis. PVT1 plays an important role in the progression and prognosis of BLCA and can be used as a medium biomarker to predict survival after cystectomy.</p> </abstract>

Download Full-text

Identification of an epithelial-mesenchymal transition-related lncRNA prognostic signature for patients with glioblastoma

Scientific Reports ◽

10.1038/s41598-021-03213-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

XinJie Yang ◽

Sha Niu ◽

JiaQiang Liu ◽

Jincheng Fang ◽

ZeYu Wu ◽

...

Keyword(s):

Immune Cell ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Prognostic Signature ◽

Mesenchymal Transition

AbstractGlioblastoma (GBM) is a strikingly heterogeneous and lethal brain tumor with very poor prognosis. LncRNAs play critical roles in the tumorigenesis of GBM through regulation of various cancer-related genes and signaling pathways. Here, we focused on the essential role of EMT and identified 78 upregulated EMT-related genes in GBM through differential expression analysis and Gene set enrichment analysis (GSEA). A total of 301 EMT-related lncRNAs were confirmed in GBM through Spearman correlation analysis and a prognostic signature consisting of seven EMT-related lncRNAs (AC012615.1, H19, LINC00609, LINC00634, POM121L9P, SNHG11, and USP32P3) was established by univariate and multivariate Cox regression analyses. Significantly, Kaplan–Meier analysis and receiver-operating-characteristic (ROC) curve validated the accuracy and efficiency of the signature to be satisfactory. Quantitative real-time (qRT)-PCR assay demonstrated the expression alterations of the seven lncRNAs between normal glial and glioma cell lines. Functional enrichment analysis revealed multiple EMT and metastasis-related pathways were associated with the EMT-related lncRNA prognostic signature. In addition, we observed the degree of immune cell infiltration and immune responses were significantly increased in high-risk subgroup compared with low-risk subgroup. In conclusion, we established an effective and robust EMT-related lncRNA signature which was expected to predict the prognosis and immunotherapy response for GBM patients.

Download Full-text

Identification of Key Claudin Genes Associated With Survival Prognosis and Diagnosis in the Colon Cancer Through Integrated Bioinformatic Analysis‏.

10.21203/rs.3.rs-1007190/v1 ◽

2021 ◽

Author(s):

Rana Alghamdi ◽

Maryam Al-Zahrani

Keyword(s):

Colon Cancer ◽

Differential Expression Analysis ◽

Colon Adenocarcinoma ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Survival Prognosis ◽

Diagnostic Efficacy ◽

Gene Set ◽

Protein Protein Interaction

Abstract Background: Claudin’s gene are associated with various aberrant physiological and cellular signaling. However, the association of claudins with survival prognosis, signaling pathways, and diagnostic efficacy in colon cancer remain lacking. Methods: We used various bioinformatics methods, including differential expression analysis, gene set enrichment analysis (GSEA), protein-protein interaction (PPI), survival analysis, single sample gene set enrichment analysis (ssGSEA), mutation analysis, and identifying receiver operating characteristic (ROC) curve of claudins in the TCGA colon adenocarcinoma (COAD). Results: We found that: CLDN2, CLDN1, CLDN14, CLDN16, CLDN18, CLDN9, CLDN12, and CLDN6 are elevated in COAD. In contrast, the CLDN8, CLDN23, CLDN5, CLDN11, CLDN7, and CLDN15 are downregulated in COAD. Various claudin’s genes are mutated and associated with diagnostic efficacy in the COAD. Conclusions: Claudin’s genes are associated with prognosis, immune regulation, signaling pathway regulations, and diagnosis. These findings may provide new molecular insight into the treatment of colon cancer.

Download Full-text

Phospholipase C Isozymes Are Deregulated in Colorectal Cancer – Insights Gained from Gene Set Enrichment Analysis of the Transcriptome

PLoS ONE ◽

10.1371/journal.pone.0024419 ◽

2011 ◽

Vol 6 (9) ◽

pp. e24419 ◽

Cited By ~ 34

Author(s):

Stine A. Danielsen ◽

Lina Cekaite ◽

Trude H. Ågesen ◽

Anita Sveen ◽

Arild Nesbakken ◽

...

Keyword(s):

Colorectal Cancer ◽

Phospholipase C ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set

Download Full-text

A Novel Pyroptosis-related Genes Prognostic Signature Affects the Immune Infiltration Patterns in Hepatocellular Carcinoma

10.21203/rs.3.rs-1020197/v1 ◽

2021 ◽

Author(s):

Yiqun Jin ◽

Bai. Xue-song

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Checkpoint ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract PurposePyroptosis is an inflammatory form of cell death associated with tumorigenesis and progression. However, the prognostic value of pyroptosis-related genes (PRGs) in hepatocellular carcinoma (HCC) have not been elucidated.MethodsWe downloaded mRNA expression profiles and clinical information from TCGA and ICGC database. Then, differently expressed PRGs were screened to construct a multigene prognostic signature by least absolute contraction and selection operator (LASSO) Cox regression method in TCGA cohort. Date from ICGC was used to validate the robustness of this signature. Kaplan-Meier analysis was used to compare overall survival (OS) between high- and low-risk group. Univariate and multivariate Cox analysis were performed to identify the independent prognostic value of the signature. Gene set enrichment analysis (GSEA) was utilized to conduct GO and KEGG analysis. Single-sample gene set enrichment analysis was implemented to assess the immune cell infiltration and immune-related function. TIDE algorithm evaluated the significance of this signature in predicting immunotherapeutic sensitivity. ResultsAn 8-PRGs prognostic model was established. The OS of low-risk group was significantly increased compared to high-risk group. Receiver operating characteristic curve showed the model had a good prognostic predictive accuracy. Cox regression analysis proved the model an independent predictor for OS in HCC. GSEA indicated that the risk score was associated with immune response. Furthermore, different subgroups exhibited different immunoinfiltration patterns, different immune-checkpoint levels and different potential responses for immune-checkpoint blockade therapy.ConclusionAn 8-PRGs signature can predict the prognosis of HCC patients and may act as an immunotherapeutic potential target for HCC.

Download Full-text