A Novel Established Pyroptosis-participant Genes Signature for Predicting the Prognosis of Idiopathic Pulmonary Fibrosis

Abstract Idiopathic pulmonary fibrosis (IPF), caused by a strange reason and characterized by a bad clinical prognosis, is an advanced, long-time, and irreversible interstitial lung disease (ILD). In recent years, it has been confirmed that a pyroptosis is a phlogistic form of programmed cellular demise. At the same time, the expression of the pyroptosis-participant gene (PPG) in IPF and their pertinence behind prognosis remains indistinct. In this research, we identified 17 pyroptosis regulators that were distinguishingly expressed entre IPF and controls. The ground on these distinguishingly expressed genes (DEGs), entire IPF conditions could be uncoupled into two subtypes. The prognostic significance of respective PPG for surviving was assessed to find a polygenes signature doing with a Gene Expression Omnibus (GEO) pack (GSE28042). Via putting into use the least absolute shrinkage and selection operator (LASSO) Cox regression approach, a 6-gene signature was structured and ranged entire IPF sicks in GSE28042 into a shallow-threat or high-threat group. IPF sicks in the shallow-threat group revealed meaningfully upper survival chances than those in the high-threat group (pvalue < 0.001). Exploiting the mid threat-score from GSE28042, IPF sicks from another GEO cohort(GSE70866-GPL17077) were separated into two threat subgroups, and the shallow-threat set had augmented overall survival (OS) time ( P = 0.0018). United the clinical characteristics, the threat-score was a sole element for forecasting the overall survival of IPF sicks. Function enrichment analyses bespoke that modification of morphology or physiology of other organisms, killing of cellular of other organisms, and disruption of cellular of other organisms biological processes were increased in the high-threat group. GSEA(Gene Set Enrichment Analysis) showed that cancer- and autoimmune disease-participant “KEGG” gene sets were highly enriched in the high−threat phenotype. In general, PPGs play a crucial role in IPF and can be done to forecast the prognosis of IPFs, and our consequences suggest that the high-threat group of IPF may be linked with the response of other organisms and autoimmunity as well.

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Establishment of an Autophagy-Related Clinical Prognosis Model for Predicting the Overall Survival of Osteosarcoma

BioMed Research International ◽

10.1155/2021/5428425 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Jianyi Li ◽

Xiaojie Tang ◽

Yukun Du ◽

Jun Dong ◽

Zheng Zhao ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Molecular Mechanisms ◽

Cox Regression ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Data Set ◽

Primary Tumor Site ◽

Clinical Prognosis

Purpose. Osteosarcoma is the most common primary and highly invasive bone tumor in children and adolescents. The purpose of this study is to construct a multi-gene expression feature related to autophagy, which can be used to predict the prognosis of patients with osteosarcoma. Materials and methods. The clinical and gene expression data of patients with osteosarcoma were obtained from the target database. Enrichment analysis of autophagy-related genes related to overall survival (OS-related ARGs) screened by univariate Cox regression was used to determine OS-related ARGs function and signal pathway. In addition, the selected OS-related ARGs were incorporated into multivariate Cox regression to construct prognostic signature for the overall survival (OS) of osteosarcoma. Use the dataset obtained from the GEO database to verify the signature. Besides, gene set enrichment analysis (GSEA) were applied to further elucidate the molecular mechanisms. Finally, the nomogram is established by combining the risk signature with the clinical characteristics. Results. Our study eventually included 85 patients. Survival analysis showed that patients with low riskScore had better OS. In addition, 16 genes were included in OS-related ARGs. We also generate a prognosis signature based on two OS-related ARGs. The signature can significantly divide patients into low-risk groups and high-risk groups, and has been verified in the data set of GEO. Subsequently, the riskScore, primary tumor site and metastasis status were identified as independent prognostic factors for OS and a nomogram were generated. The C-index of nomogram is 0.789 (95% CI: 0.703~0.875), ROC curve and calibration chart shows that nomogram has a good consistency between prediction and observation of patients. Conclusions. ARGs was related to the prognosis of osteosarcoma and can be used as a biomarker of prognosis in patients with osteosarcoma. Nomogram can be used to predict OS of patients and improve treatment strategies.

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Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.11273 ◽

2021 ◽

Vol 9 ◽

pp. e11273

Author(s):

Lei Yang ◽

Weilong Yin ◽

Xuechen Liu ◽

Fangcun Li ◽

Li Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Overall Survival ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Enrichment Score ◽

Hub Genes ◽

Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

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Identification of a Glycolysis-Related LncRNA Signature to Predict Survival in Diffuse Glioma Patients

Frontiers in Oncology ◽

10.3389/fonc.2020.597877 ◽

2021 ◽

Vol 10 ◽

Author(s):

Yangyang Wang ◽

Wenjianlong Zhou ◽

Shunchang Ma ◽

Xiudong Guan ◽

Dainan Zhang ◽

...

Keyword(s):

Cell Cycle ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

Pearson Correlation ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Diffuse Glioma ◽

Roc Curve Analysis

Glycolysis refers to one of the critical phenotypes of tumor cells, regulating tumor cell phenotypes and generating sufficient energy for glioma cells. A range of noticeable genes [such as isocitrate dehydrogenase (IDH), phosphatase, and tensin homolog (PTEN), or Ras] overall impact cell proliferation, invasion, cell cycle, and metastasis through glycolysis. Moreover, long non-coding RNAs (LncRNAs) are increasingly critical to disease progression. Accordingly, this study aimed to identify whether glycolysis-related LncRNAs have potential prognostic value for glioma patients. First, co-expression network between glycolysis-related protein-coding RNAs and LncRNAs was established according to Pearson correlation (Filter: |r| > 0.5 & P < 0.001). Furthermore, based on univariate Cox regression, the Least Absolute Shrinkage and Selection Operator (LASSO) analysis and multivariate Cox regression, a predictive model were built; vital glycolysis-related LncRNAs were identified; the risk score of every single patient was calculated. Moreover, receiver operating characteristic (ROC) curve analysis, gene set enrichment analysis (GSEA), GO and KEGG enrichment analysis were performed to assess the effect of risk score among glioma patients. 685 cases (including RNA sequences and clinical information) from two different cohorts of the Chinese Glioma Genome Atlas (CGGA) database were acquired. Based on the mentioned methods, the risk score calculation formula was yielded as follows: Risk score = (0.19 × EXPFOXD2-AS1) + (−0.27 × EXPAC062021.1) + (−0.16 × EXPAF131216.5) + (−0.05 × EXPLINC00844) + (0.11 × EXPCRNDE) + (0.35 × EXPLINC00665). The risk score was independently related to prognosis, and every single mentioned LncRNAs was significantly related to the overall survival of patients. Moreover, functional enrichment analysis indicated that the biologic process of the high-risk score was mainly involved in the cell cycle and DNA replication signaling pathway. This study confirmed that glycolysis-related LncRNAs significantly impact poor prognosis and short overall survival and may act as therapeutic targets in the future.

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Identification of Pro-Fibrotic Macrophage Populations by Single-Cell Transcriptomic Analysis in West Highland White Terriers Affected With Canine Idiopathic Pulmonary Fibrosis

Frontiers in Immunology ◽

10.3389/fimmu.2020.611749 ◽

2020 ◽

Vol 11 ◽

Author(s):

Aline Fastrès ◽

Dimitri Pirottin ◽

Laurence Fievez ◽

Alexandru-Cosmin Tutunaru ◽

Géraldine Bolen ◽

...

Keyword(s):

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Single Cell ◽

Enrichment Analysis ◽

Lavage Fluid ◽

Gene Set Enrichment Analysis ◽

Cell Populations ◽

Gene Set Enrichment ◽

Gene Set ◽

Macrophage Populations

Canine idiopathic pulmonary fibrosis (CIPF) affects old dogs from the West Highland white terrier (WHWT) breed and mimics idiopathic pulmonary fibrosis (IPF) in human. The disease results from deposition of fibrotic tissue in the lung parenchyma causing respiratory failure. Recent studies in IPF using single-cell RNA sequencing (scRNA-seq) revealed the presence of profibrotic macrophage populations in the lung, which could be targeted for therapeutic purpose. In dogs, scRNA-seq was recently validated for the detection of cell populations in bronchoalveolar lavage fluid (BALF) from healthy dogs. Here we used the scRNA-seq to characterize disease-related heterogeneity within cell populations of macrophages/monocytes (Ma/Mo) in the BALF from five WHWTs affected with CIPF in comparison with three healthy WHWTs. Gene set enrichment analysis was also used to assess pro-fibrotic capacities of Ma/Mo populations. Five clusters of Ma/Mo were identified. Gene set enrichment analyses revealed the presence of pro-fibrotic monocytes in higher proportion in CIPF WHWTs than in healthy WHWTs. In addition, monocyte-derived macrophages enriched in pro-fibrotic genes in CIPF compared with healthy WHWTs were also identified. These results suggest the implication of Ma/Mo clusters in CIPF processes, although, further research is needed to understand their role in disease pathogenesis. Overexpressed molecules associated with pulmonary fibrosis processes were also identified that could be used as biomarkers and/or therapeutic targets in the future.

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Identification of Biomarkers of Hepatocellular Carcinoma Gene Prognosis Based on Immune-Related lncRNA Signature of Transcriptome Data

10.21203/rs.3.rs-57303/v1 ◽

2020 ◽

Author(s):

Andi Ma ◽

Yukai Sun ◽

Racheal O. Ogbodu ◽

Ling Xiao ◽

Haibing Deng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis

Abstract Background: It is well known that long non-coding RNAs (lncRNAs) play a vital role in cancer. We aimed to explore the prognostic value of potential immune-related lncRNAs in hepatocellular carcinoma (HCC). Methods: Validated the established lncRNA signature of 343 patients with HCC from The Cancer Genome Atlas (TCGA) and 81 samples from Gene Expression Omnibus (GEO). Immune-related lncRNAs for HCC prognosis were evaluated using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. LASSO analysis was performed to calculate a risk score formula to explore the difference in overall survival between high- and low-risk groups in TCGA, which was verified using GEO, Gene Ontology (GO), and pathway-enrichment analysis. These analyses were used to identify the function of screened genes and construct a co-expression network of these genes. Results: Using computational difference algorithms and lasso Cox regression analysis, the differentially expressed and survival-related immune-related genes (IRGs) among patients with HCC were established as five novel immune-related lncRNA signatures (AC099850.3, AL031985.3, PRRT3-AS1, AC023157.3, MSC-AS1). Patients in the low‐risk group showed significantly better survival than patients in the high‐risk group ( P = 3.033e−05). The signature identified can be an effective prognostic factor to predict patient survival. The nomogram showed some clinical net benefits predicted by overall survival. In order to explore its underlying mechanism, several methods of enrichment were elucidated using Gene Set Enrichment Analysis. Conclusion: Identifying five immune-related lncRNA signatures has important clinical implications for predicting patient outcome and guiding tailored therapy for patients with HCC with further prospective validation.

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Overexpression of HOXA10 is associated with unfavorable prognosis of acute myeloid leukemia

10.21203/rs.3.rs-16941/v1 ◽

2020 ◽

Author(s):

Chao Guo ◽

Qian-qian Ju ◽

Chun-xia Zhang ◽

Ming Gong ◽

Zhen-ling Li ◽

...

Keyword(s):

Cox Regression ◽

Cox Model ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

High Group ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

The Impact

Abstract Background HOXA family genes were crucial transcription factors involving cell proliferation and apoptosis. While few studies have focused on HOXA10 in AML. We aimed to investigate the prognostic significance of HOXA10. Methods We downloaded datasets from GEO and BeatAML database, to compare HOXA expression level between AML patients and controls. Kaplan-Meier curves were used to estimate the impact of HOXA10 expression on AML survival. The differentially expressed genes, miRNAs, lncRNAs and methylated regions between HOXA10-high and -low groups were obtained using R (version 3.6.0). Accordingly, the gene set enrichment analysis (GSEA) was accomplished using MSigDB database. Moreover, the regulatory TFs/microRNAs/lncRNAs of HOXA10 were identified. A LASSO-Cox model fitted OS to clinical and HOXA10-associated genetic variables by glmnet package. Results HOXA10 was overexpressed in AML patients than that in controls. The HOXA10-high group is significantly associated with shorter OS and DFS. A total of 1219 DEGs, 131 DEmiRs, 282 DElncRs were identified to be associated with HOXA10. GSEA revealed that 12 suppressed and 3 activated pathways in HOXA10-high group. Furthermore, the integrated regulatory network targeting HOXA10 was established. The LASSO-Cox model fitted OS to AML-survival risk scores, which included age, race, molecular risk, expression of IKZF2/LINC00649/LINC00839/FENDRR and has-miR-424-5p. The time dependent ROC indicated a satisfying AUC (1-year AUC 0.839, 3-year AUC 0.871 and 5-year AUC 0.813). Conclusions Our study identified HOXA10 overexpression as an adverse prognostic factor for AML. The LASSO-COX regression analysis revealed novel prediction model of OS with superior diagnostic utility.

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Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma

Scientific Reports ◽

10.1038/s41598-020-72178-1 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Qingmiao Shi ◽

Chen Xue ◽

Xin Yuan ◽

Yuting He ◽

Zujiang Yu

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Enrichment Analysis ◽

Cancer Genome ◽

Regulatory Genes ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Clinical Prognosis ◽

Cox Regression Analysis ◽

Genes Expression

Abstract Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. N1-methyladenosine (m1A), a methylation modification on RNA, is gaining attention for its role across diverse biological processes. However, m1A-related regulatory genes expression, its relationship with clinical prognosis, and its role in HCC remain unclear. In this study, we utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database to investigate alterations within 10 m1A-related regulatory genes and observed a high mutation frequency (23/363). Cox regression analysis and least absolute shrinkage and selection operator were used to explore the association between m1A-related regulatory genes expression and HCC patient survival and identified four regulators that were remarkably associated with HCC patient prognosis. Additionally, an independent cohort from International Cancer Genome Consortium was studied to validate our discoveries and found to be consistent with those in the TCGA dataset. In terms of mechanism, gene set enrichment analysis linked these four genes with various physiological roles in cell division, the MYC pathway, protein metabolism, and mitosis. Kyoto Encyclopedia of Genes and Genomes analysis revealed that PI3K/Akt signaling pathway had potential relevance to m1A-related regulatory genes in HCC. These findings indicate that m1A-related regulatory genes may play crucial roles in regulating HCC progression and be exploited for diagnostic and prognostic purposes.

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Prognostic Value of BIRC5 in Lung Adenocarcinoma Lacking EGFR, KRAS, and ALK Mutations by Integrated Bioinformatics Analysis

Disease Markers ◽

10.1155/2019/5451290 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 8

Author(s):

Yajuan Cao ◽

Weikang Zhu ◽

Wanqing Chen ◽

Jianchun Wu ◽

Guozhen Hou ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Lung Adenocarcinoma ◽

Prognostic Value ◽

Expression Profiles ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes

Objective. This study was aimed at investigating the prognostic significance of Baculoviral IAP repeat containing 5 (BIRC5) in lung adenocarcinoma (LAD) lacking EGFR, KRAS, and ALK mutations (triple-negative (TN) adenocarcinomas). Methods. The gene expression profiles were obtained from Gene Expression Omnibus (GEO). The identification of the differentially expressed genes (DEGs) was performed by GeneSpring GX. Gene set enrichment analysis (GSEA) was used to execute gene ontology function and pathway enrichment analysis. The protein interaction network was constructed by Cytoscape. The hub genes were extracted by MCODE and cytoHubba plugin from the network. Then, using BIRC5 as a candidate, the prognostic value in LAD and TN adenocarcinomas was verified by the Kaplan-Meier plotter and The Cancer Genome Atlas (TCGA) database, respectively. Finally, the mechanism of BIRC5 was predicted by a coexpressed network and enrichment analysis. Results. A total of 38 upregulated genes and 121 downregulated genes were identified. 9 hub genes were extracted. Among them, the mRNA expression of 5 genes, namely, BIRC5, MCM4, CDC20, KIAA0101, and TRIP13, were significantly upregulated among TN adenocarcinomas (all P<0.05). Notably, only the overexpression of BIRC5 was associated with unfavorable overall survival (OS) in TN adenocarcinomas (log rank P=0.0037). TN adenocarcinoma patients in the BIRC5 high-expression group suffered from a significantly high risk of distant metastasis (P=0.046), advanced N stage (P=0.033), and tumor-bearing (P=0.031) and deceased status (P=0.003). The mechanism of BIRC5 and coexpressed genes may be linked closely with the cell cycle. Conclusion. Overexpressed in tumors, BIRC5 is associated with unfavorable overall survival in TN adenocarcinomas. BIRC5 is a potential predictor and therapeutic target in TN adenocarcinomas.

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Identification and validation of a five-gene prognostic signature for hepatocellular carcinoma

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02202-9 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Huibin Yang ◽

Junyu Huo ◽

Xin Li

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Human Cancer ◽

External Validation ◽

Enrichment Analysis ◽

Risk Classification ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature

Abstract Background ARID1A is a commonly mutated tumor suppressor gene found in all human cancer types, but its clinical significance, oncogenic functions, and relevant mechanisms in hepatocellular carcinoma (HCC) are not well understood. Objective We aimed to improving the prognosis risk classification of HCC from the perspective of ARID1A mutations. Materials and methods We examined the interaction between ARID1A mutations and the overall survival via Kaplan-Meier survival analysis. We used gene set enrichment analysis (GSEA) to elucidate the influence of ARID1A mutations on signaling pathways. A prognostic model was constructed using LASSO and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was used to estimate the performance and accuracy of the model. Results HCC patients with ARID1A mutations presented poor prognosis. By GSEA, we showed that genes upregulated by reactive oxygen species (ROS) and regulated by MYC were positively correlated with ARID1A mutations. A prognostic signature consisting of 5 genes (SRXN1, LDHA, TFDP1, PPM1G, and EIF2S1) was constructed in our research. The signature showed good performance in predicting overall survival (OS) for HCC patients by internal and external validation. Conclusion Our research proposed a novel and robust approach for the prognostic risk classification of HCC patients, and this approach may provide new insights to improve the treatment strategy of HCC.

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Identification and Validation of a Hypoxia-related lncRNA Signature for Prognostic Prediction in Endometrial Cancer

10.21203/rs.3.rs-633056/v1 ◽

2021 ◽

Author(s):

Hongyang Liu ◽

Junhu Wan ◽

Quanling Feng ◽

Jingyu Li ◽

Jun Liu ◽

...

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Roc Curve Analysis ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Selection Operator

Abstract Background: Endometrial cancer (EC) is one of the most common types of gynecological cancer. Hypoxia is an important clinical feature and regulates various tumor processes. However, the prognostic value of hypoxia-related lncRNA in EC remains to be further elucidated. Here, we aimed to characterize the molecular features of EC by the development of a classification system based on the expression profile of hypoxia-related lncRNA.Methods: Univariate Cox regression analysis was used to identify hypoxia-related lncRNAs associated with overall survival. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct gene signature. Multivariate Cox regression analysis and receiver operating characteristic (ROC) curve analysis were also performed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway, and Gene Set Enrichment Analysis (GESA) were used to identify hypoxia-related lncRNA pathway. Western blot and real-time PCR were used to detect target gene expression. The cell proliferation was determined by using WST-1 assay.Results: Based on univariate Cox regression analysis, we identified 17 hypoxia-related lncRNAs significantly associated with overall survival. Next, the least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct a multigene signature in the TCGA EC cohort. The risk score was confirmed as an independent predictor for overall survival in multivariate Cox regression analysis and receiver operating characteristic (ROC) curve analysis. Besides, the survival time of EC patients in different risk group was significantly correlated to clinicopathologic factors, such as age, stage and grade. Furthermore, hypoxia-related lncRNA associated with the high-risk group were involved in various aspects of the malignant progression of EC via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway, and Gene Set Enrichment Analysis (GESA). Besides, using CIBERSORT analysis, we found a different immune cell environment characterization of EC between different cluster and risk group. Moreover, the risk score was closely correlated to immunotherapy response, microsatellite instability and tumor mutation burden (TMB). Finally, we select one hypoxia-related lncRNA SOS1-IT1 to validate its role in hypoxia and EC progression. Interestingly, we found SOS1-IT1 was overexpressed in tumor tissues, and closely correlated with clinicopathological parameters of EC. The expression level of SOS1-IT1 was significantly increased under hypoxia condition. Additionally, the important hypoxia regulatory factor HIF-1α can directly bind SOS1-IT1 promoter region, and affect its expression level. Conclusions: In summary, this study established a new EC classification based on the hypoxia-related lncRNA signature, thereby provide a novel sight to understand the potential mechanism of human EC development.

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