Identification of Tumor Microenvironment-Related Alternative Splicing Events to Predict the Prognosis of Endometrial Cancer

BackgroundEndometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients’ survival, and growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers.MethodsWe downloaded the clinical information and mRNA expression profiles of 542 tumor tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we constructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment.ResultsThe prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group was notably better. Furthermore, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient’s ESTIMATE score and the higher the infiltration of immune cells.ConclusionsWe used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature was combined with tumor microenvironment (TME) and AS events, which allowed a deeper understanding of the immune status of EC patients, and also provided new insights for clinical patients with EC.

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Genome-Wide Analyses of Prognostic and Therapeutic Alternative Splicing Signatures in Bladder Urothelial Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.626858 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhongru Fan ◽

Zhe Zhang ◽

Chiyuan Piao ◽

Zhuona Liu ◽

Zeshu Wang ◽

...

Keyword(s):

Cluster Analysis ◽

Alternative Splicing ◽

Urothelial Carcinoma ◽

Prognostic Value ◽

Gap Analysis ◽

Unsupervised Cluster ◽

Chemotherapy Drugs ◽

Genome Wide ◽

Bladder Urothelial Carcinoma ◽

Unsupervised Cluster Analysis

BackgroundAlternative splicing (AS) is an indispensable post-transcriptional modification applied during the maturation of mRNA, and AS defects have been associated with many cancers. This study was designed to thoroughly analyze AS events in bladder urothelial carcinoma (BLCA) at the genome-wide level.MethodsWe adopted a gap analysis to screen for significant differential AS events (DASEs) associated with BLCA. DASEs with prognostic value for OS and the disease-free interval (DFI) were identified by Cox analysis. In addition, a differential AS network and AS clusters were identified using unsupervised cluster analysis. We examined differences in the sensitivity to chemotherapy and immunotherapy between BLCA patients with high and low overall survival (OS) risk.ResultsAn extensive number of DASEs (296) were found to be clinically relevant in BLCA. A prognosis model was established based prognostic value of OS and DFI. CUGBP elav-like family member 2 (CELF2) was identified as a hub splicing factor for AS networks. We also identified AS clusters associated with OS using unsupervised cluster analysis, and we predicted that the effects of cisplatin and gemcitabine chemotherapy would be different between high- and low-risk groups based on OS prognosis.ConclusionWe completed a comprehensive analysis of AS events in BLCA at the genome-wide level. The present findings revealed that DASEs and splicing factors tended to impact BLCA patient survival and sensitivity to chemotherapy drugs, which may provide novel prospects for BLCA therapies.

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Identification of Prognostic lncRNA Related to the Immune Microenvironment of Soft Tissue Sarcoma

10.21203/rs.3.rs-752805/v1 ◽

2021 ◽

Author(s):

Wang-Ying Dai ◽

Bin Wang ◽

Jian-Yi Li ◽

Jun-Cheng Zhu ◽

Zong-ping Luo

Keyword(s):

Cluster Analysis ◽

High Risk ◽

Soft Tissue ◽

Tumor Microenvironment ◽

Soft Tissue Sarcoma ◽

Expression Analysis ◽

Risk Group ◽

Immune Microenvironment ◽

Kaplan Meier ◽

Unsupervised Cluster Analysis

Abstract Background: Soft tissue sarcoma is a malignant tumor with high degree of malignancy and poor prognosis, originating from mesenchymal tissue. Long non-coding RNAs (lncRNAs) are involved in various biological and pathological processes in the body. They target mRNA through transcription or post-transcription, resulting in the occurrence, invasion, and metastasis of tumors. Therefore, they are highly relevant with regard to early diagnoses and as prognostic indicators.Objective: The objective of the present study was to identify immune-related lncRNAs associated with the tumor microenvironment that can be used to predict soft tissue sarcomas.Methods: Clinical data and follow-up data were obtained from the cBioPortal database, and RNA sequencing data used for the model structure can be accessed from. The Cancer Genome Atlas (TCGA) database. LncRNAs were screened by differential expression analysis and co-expression analysis. The Cox regression model and Kaplan–Meier analysis were used to study the association between lncRNAs and soft tissue sarcoma prognosis in the immune microenvironment. Unsupervised cluster analysis was then completed to discover the impact of screening lncRNAs on disease. Lastly, we constructed an mRNA-lncRNA network by Cytoscape software.Results: Unsupervised cluster analysis revealed that the 210 lncRNAs screened showed strong correlation with the tumor immune microenvironment. Two signatures containing seven and five lncRNAs related to the tumor microenvironment were constructed and used to predict overall survival (OS) and disease-free survival (DFS). The Kaplan–Meier（K-M） survival curve showed that the prognoses of patients in the high-risk and low-risk groups differed significantly, and the prognosis associated with the low-risk group was better than that associated with the high-risk group. Two nomograms with predictive capabilities were established.Conclusion: The results indicate that seven OS- and five DFS-related lncRNAs are correlated with the prognosis of soft tissue sarcoma.

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Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma

10.21203/rs.2.22618/v1 ◽

2020 ◽

Author(s):

liu jinhui ◽

Li siyue ◽

Gao feng ◽

meng huangyang ◽

Nie sipei ◽

...

Keyword(s):

Endometrial Cancer ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Cellular Respiration ◽

Immunohistochemistry Staining ◽

Glycolysis Pathway

Abstract Background: Endometrial cancer is the fourth most common cancer in women. The death rate for endometrial cancer has increased. Glycolysis of cellular respiration is a complex reaction and is the first step in most carbohydrate catabolism, which was proved to participate in tumors. Methods: We analyzed the sample data of over 500 patients from TCGA database. The bioinformatic analysis included GSEA, cox and lasso regression analysis to select prognostic genes, as well as construction of a prognostic model and a nomogram for OS evaluation. The immunohistochemistry staining, survival analysis and expression level validation were also performed. Maftools package was for mutation analysis. GSEA identified Glycolysis was the most related pathway to EC. Results: According to the prognostic model using the train set, 9 glycolysis-related genes including B3GALT6, PAM, LCT, GMPPB, GLCE, DCN, CAPN5, GYS2 and FBP2 were identified as prognosis-related genes. Based on nine gene signature, the EC patients could be classified into high and low risk subgroups, and patients with high risk score showed shorter survival time. Time-dependent ROC analysis and Cox regression suggested that the risk score predicted EC prognosis accurately and independently. Analysis of test and train sets yielded consistent results A nomogram which incorporated the 9‐mRNA signature and clinical features was also built for prognostic prediction. Immunohistochemistry staining and TCGA validation showed that expression levels of these genes do differ between EC and normal tissue samples. GSEA revealed that the samples of the low-risk group were mainly concentrated on Bile Acid Metabolism. Patients in the low-risk group displayed obvious mutation signatures compared with those in the high-risk group. Conclusion: This study found that the Glycolysis pathway is associated with EC and screened for hub genes on the Glycolysis pathway, which may serve as new target for the treatment of EC.

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Comprehensive transcriptomic characterization reveals core genes and module associated with immunological changes via 1619 samples of brain glioma

Cell Death and Disease ◽

10.1038/s41419-021-04427-8 ◽

2021 ◽

Vol 12 (12) ◽

Author(s):

Ying Zhang ◽

Wenping Ma ◽

Wenhua Fan ◽

Changyuan Ren ◽

Jianbao Xu ◽

...

Keyword(s):

High Risk ◽

Risk Score ◽

Risk Group ◽

Treatment Options ◽

Gene Signature ◽

Immune Checkpoint Blockade ◽

High Risk Group ◽

Hub Genes ◽

Score Model ◽

Prediction Of Prognosis

AbstractGlioma is the most common primary malignant brain tumor with limited treatment options and poor prognosis. To investigate the potential relationships between transcriptional characteristics and clinical phenotypes, we applied weighted gene co-expression network analysis (WGCNA) to construct a free-scale gene co-expression network yielding four modules in gliomas. Turquoise and yellow modules were positively correlated with the most malignant glioma subtype (IDH-wildtype glioblastomas). Of them, genes in turquoise module were mainly involved in immune-related terms and were regulated by NFKB1, RELA, SP1, STAT1 and STAT3. Meanwhile, genes in yellow module mainly participated in cell-cycle and division processes and were regulated by E2F1, TP53, E2F4, YBX1 and E2F3. Furthermore, 14 genes in turquoise module were screened as hub genes. Among them, five prognostic hub genes (TNFRSF1B, LAIR1, TYROBP, VAMP8, and FCGR2A) were selected to construct a prognostic risk score model via LASSO method. The risk score of this immune-related gene signature is associated with clinical features, malignant phenotype, and somatic alterations. Moreover, this signature showed an accurate prediction of prognosis across different clinical and pathological subgroups in three independent datasets including 1619 samples. Our results showed that the high-risk group was characterized by active immune-related activities while the low-risk group enriched in neurophysiological-related pathway. Importantly, the high-risk score of our immune signature predicts an enrichment of glioma-associated microglia/macrophages and less response to immune checkpoint blockade (ICB) therapy in gliomas. This study not only provides new insights into the molecular pathogenesis of glioma, but may also help optimize the immunotherapies for glioma patients.

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Development and Validation of a Prognostic Gene Signature Correlated With M2 Macrophage Infiltration in Esophageal Squamous Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.769727 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiannan Yao ◽

Ling Duan ◽

Xuying Huang ◽

Jian Liu ◽

Xiaona Fan ◽

...

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

M2 Macrophage ◽

Cox Regression Analysis ◽

Qrt Pcr ◽

Score Model ◽

Geo Database

BackgroundEsophageal squamous cell carcinoma (ESCC) is the most common type of esophageal cancer and the seventh most prevalent cause of cancer-related death worldwide. Tumor microenvironment (TME) has been confirmed to play an crucial role in ESCC progression, prognosis, and the response to immunotherapy. There is a need for predictive biomarkers of TME-related processes to better prognosticate ESCC outcomes.AimTo identify a novel gene signature linked with the TME to predict the prognosis of ESCC.MethodsWe calculated the immune/stromal scores of 95 ESCC samples from The Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm, and identified differentially expressed genes (DEGs) between high and low immune/stromal score patients. The key prognostic genes were further analyzed by the intersection of protein–protein interaction (PPI) networks and univariate Cox regression analysis. Finally, a risk score model was constructed using multivariate Cox regression analysis. We evaluated the associations between the risk score model and immune infiltration via the CIBERSORT algorithm. Moreover, we validated the signature using the Gene Expression Omnibus (GEO) database. Within the ten gene signature, five rarely reported genes were further validated with quantitative real time polymerase chain reaction (qRT-PCR) using an ESCC tissue cDNA microarray.ResultsA total of 133 up-regulated genes were identified as DEGs. Ten prognostic genes were selected based on intersection analysis of univariate COX regression analysis and PPI, and consisted of C1QA, C1QB, C1QC, CD86, C3AR1, CSF1R, ITGB2, LCP2, SPI1, and TYROBP (HR>1, p<0.05). The expression of 9 of these genes in the tumor samples were significantly higher compared to matched adjacent normal tissue based on the GEO database (p<0.05). Next, we assessed the ability of the ten-gene signature to predict the overall survival of ESCC patients, and found that the high-risk group had significantly poorer outcomes compared to the low-risk group using univariate and multivariate analyses in the TCGA and GEO cohorts (HR=2.104, 95% confidence interval:1.343-3.295, p=0.001; HR=1.6915, 95% confidence interval:1.053-2.717, p=0.0297). Additionally, receiver operating characteristic (ROC) curve analysis demonstrated a relatively sensitive and specific profile for the signature (1-, 2-, 3-year AUC=0.672, 0.854, 0.81). To identify the basis for these differences in the TME, we performed correlation analyses and found a significant positive correlation with M1 and M2 macrophages and CD8+ T cells, as well as a strong correlation to M2 macrophage surface markers. A nomogram based on the risk score and select clinicopathologic characteristics was constructed to predict overall survival of ESCC patients. For validation, qRT-PCR of an ESCC patient cDNA microarray was performed, and demonstrated that C1QA, C3AR1, LCP2, SPI1, and TYROBP were up-regulated in tumor samples and predict poor prognosis.ConclusionThis study established and validated a novel 10-gene signature linked with M2 macrophages and poor prognosis in ESCC patients. Importantly, we identified C1QA, C3AR1, LCP2, SPI1, and TYROBP as novel M2 macrophage-correlated survival biomarkers. These findings may identify potential targets for therapy in ESCC patients.

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The Predictive and Therapeutic Role of Complement in Gliomas

10.21203/rs.3.rs-820383/v1 ◽

2021 ◽

Author(s):

Jingwei Zhang ◽

Shuwang Li ◽

Fangkun Liu

Keyword(s):

Tumor Microenvironment ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Low Grade ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Exact Function ◽

Score Model

Abstract Background The human complement system plays an essential role in innate immunity in the tumor microenvironment. However, the exact function of complement in gliomas progress is still ambivalent and unclear. Methods A total of 194 complement genes were included in our study to build a risk score model based on the CGGA database and was verified by the validation database and our sequencing data. Kaplan-Meier analysis was used to compare survival differences between groups. CIBERSORT and ESTIMATE algorithms were applied to explore immune infiltrates in the tumor microenvironment. The biological processes and functions were identified by GO and KEGG analysis. Results We build a risk score model using univariate and multivariate Cox regression analysis based on the CGGA database and verified in the TCGA database. Patients with gliomas in the low-risk group have a better prognosis and were associated with low grade, 1p19q codeletion, IDH mutant status, MGMT promoter methylation. In addition, the low-risk group is prone to have more infiltration of CD4 naive T cells and monocytes. Patients with gliomas in the low-risk group exhibit temozolomide sensitivity. Moreover, we explored several vital pathways that were associated with complement genes in this study. Conclusion Complement-related gene signature can predict the malignancy and outcome of patients with gliomas and was related to temozolomide sensitivity, which might act as a promising target for gliomas therapy in the future.

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Identification and Validation of a Tumor Microenvironment-Related Gene Signature for Prognostic Prediction in Advanced-Stage Non-Small-Cell Lung Cancer

BioMed Research International ◽

10.1155/2021/8864436 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Xuening Zhang ◽

Xuezhong Shi ◽

Hao Zhao ◽

Xiaocan Jia ◽

Yongli Yang

Keyword(s):

Tumor Microenvironment ◽

Risk Score ◽

Advanced Nsclc ◽

Cox Regression ◽

Gene Signature ◽

Small Cell ◽

Advanced Stage ◽

Small Cell Lung ◽

Score Model ◽

Nsclc Patients

The development of immunotherapy has greatly changed the advanced-stage non-small-cell lung cancer (NSCLC) treatment landscape. The complexity and heterogeneity of tumor microenvironment (TME) lead to discrepant immunotherapy effects among patients at the same pathologic stages. This study is aimed at exploring potential biomarkers of immunotherapy and accurately predicting the prognosis for advanced NSCLC patients. RNA-seq data and clinical information on stage III/IV NSCLC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). In TCGA-NSCLC with stage III/IV ( n = 192 ), immune scores and stromal scores were calculated by using the ESTIMATE algorithms. Univariate, LASSO, and multivariate Cox regression analyses were performed to screen prognostic TME-related genes (TMERGs) and constructed a gene signature risk score model. It was validated in external dataset including GSE41271 ( n = 91 ) and GSE81089 ( n = 36 ). Additionally, a nomogram incorporating TMERG signature risk score and clinical characteristics was established. Further, we accessed the proportion of 22 types of tumor-infiltrating immune cells (TIIC) from the CIBERSORT website and analyzed the difference between two risk groups. OS of patients with high immune/stromal scores were higher (log-rank P = 0.044 /log-rank P = 0.048 ). Multivariate Cox regression identified six prognostic TMERGs, including CD200, CHI3L2, CNTN1, CTSL, FYB1, and SLC52A1. We developed a six-gene risk score model, which was validated as an independent prognostic factor for OS (HR: 3.32, 95% CI: 2.16-5.09). Time-ROC curves showed useful discrimination for TCGA-NSCLC cohort (1-, 2-, and 3-year AUCs were 0.718, 0.761, and 0.750). The predictive robustness was validated in the external dataset. The C-index and 1-, 2-, and 3-year AUCs of nomogram were the largest, which demonstrated the nomogram had the greatest predictive accuracy and effectiveness and could be used for clinical guidance. Besides, the increased infiltration of T cells regulatory (Tregs) and macrophages M2 in the high-risk group suggested that chronic inflammation may reduce survival probability in patients with advanced NSCLC. We conducted a comprehensive analysis of the tumor microenvironment and identified the TMERG signature, which could predict prognosis accurately and provide a reference for the personalized immunotherapy for advanced NSCLC patients.

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Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

10.21203/rs.3.rs-49217/v1 ◽

2020 ◽

Author(s):

Jianfeng Zheng ◽

Jinyi Tong ◽

Benben Cao ◽

Xia Zhang ◽

Zheng Niu

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Risk Score ◽

Immune Cell ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Training Set ◽

Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

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