scholarly journals A Mutation-Related Long Noncoding RNA Signature of Genome Instability Predicts Immune Infiltration and Hepatocellular Carcinoma Prognosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianhua Wu ◽  
Xueting Ren ◽  
Nan Wang ◽  
Ruina Zhou ◽  
Mengsha Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Gene Set Enrichment Analysis ◽  
Training Cohort ◽  
Gene Set ◽  
Immune Infiltration ◽  
Potential Biomarker ◽  
Risk Patients ◽  
Patient Prognosis ◽  
Functional Analyses

Background: Long noncoding RNAs (lncRNAs) have been discovered to play a regulatory role in genomic instability (GI), which participates in the carcinogenesis of various cancers, including hepatocellular carcinoma (HCC). We endeavored to establish a GI-derived lncRNA signature (GILncSig) as a potential biomarker and explore its impact on immune infiltration and prognostic significance.Methods: Combining expression and somatic mutation profiles from The Cancer Genome Atlas database, we identified GI-related lncRNAs and conducted functional analyses on co-expressed genes. Based on Cox regression analysis, a GILncSig was established in the training cohort (n = 187), and an independent testing patient cohort (n = 183) was used to validate its predictive ability. Kaplan-Meier method and receiver operating characteristic curves were adopted to evaluate the performance. The correlation between GI and immune infiltration status was investigated based on the CIBERSORT algorithm and single sample gene set enrichment analysis. In addition, a comprehensive nomogram integrating the GILncSig and clinicopathological variables was constructed to efficiently assess HCC patient prognosis in clinical applications.Results: A total of 88 GI-related lncRNAs were screened out and the functional analyses indicated diversified effects on HCC progression. The GILncSig was established using four independent lncRNAs (AC116351.1, ZFPM2-AS1, AC145343.1, and MIR210HG) with significant prognostic value (p < 0.05). Following evaluation with the GILncSig, low-risk patients had significantly better clinical outcomes than high-risk patients in the training cohort (p < 0.001), which was subsequently validated in the independent testing cohort. High-risk group exhibited more immunocyte infiltration including B cells memory, macrophages M0 and neutrophils and higher expression of HLA gene set and immune checkpoint genes. Compared to existing HCC signatures, the GILncSig showed better prognosis predictive performance [area under the curve (AUC) = 0.709]. Furthermore, an integrated nomogram was constructed and validated to efficiently and reliably evaluate HCC patient prognosis (3-years survival AUC = 0.710 and 5-years survival AUC = 0.707).Conclusion: The GILncSig measuring GI and impacting immune infiltration serves as a potential biomarker and independent predictor of HCC patient prognosis. Our results highlight further investigation of GI and HCC molecular mechanisms.

scholarly journals Identification of A Novel Six Autophagy-Related Genes Signature for The Prognostic and A miRNA-Related Autophagy Predictor for Anti-PD-1 Therapy Responses in Prostate Cancer

2020 ◽  
Author(s):  
Lei Wu ◽  
Guojun Yue ◽  
Wen Quan ◽  
Qiong Luo ◽  
Dongxu Peng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Regression Model ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Potential Biomarker ◽  
Risk Patients

Abstract Background: Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear.Methods: An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2-3). Based on STRING analysis results, we found that the NKX2-3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2-3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2-3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa.Results: We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2-3). Further analysis demonstrated that NKX2-3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa.Conclusions: The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

scholarly journals Identification of a Nine Immune-Related lncRNA Signature as a Novel Diagnostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mengqin Yuan ◽  
Yanqing Wang ◽  
Qinqian Sun ◽  
Shiyi Liu ◽  
Shu Xian ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Value ◽  
Expression Profiles ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Potential Biomarker ◽  
Increased Risk

Hepatocellular carcinoma (HCC) ranks fifth among common cancers and is the second most common cause of cancer-related mortality worldwide. This study is aimed at identifying an immune-related long noncoding RNA (lncRNA) signature as a potential biomarker with prognostic value to improve early diagnosis and provide potential therapeutic targets for HCC patients. The subjects of this study were HCC samples with complete transcriptome data and clinical information downloaded from The Cancer Genome Atlas (TCGA) database. We then extracted the immune-related mRNA and lncRNA expression profiles. Based on the expression profiles of immune-related lncRNAs, we identified a nine-lncRNA signature that was related to the progression of HCC. The risk score was calculated based on the expression level of the nine lncRNAs of each sample, which divided patients into high-risk and low-risk groups. We found that the increased risk score was associated with a poor prognosis of HCC patients. To assess the accuracy of the survival model, we calculated a receiver operating characteristic (ROC) for validation. The curve showed that the area under the curve (AUC) for the risk score was 0.792. Besides, both principal component analysis (PCA) and gene set enrichment analysis (GSEA) were further used for functional annotation. We found that the distribution patterns were different between the low-risk and high-risk groups in PCA, and the underlying mechanism by which the nine lncRNAs promoted the progression of HCC involved an abnormal immune status. Finally, we analyzed the infiltration of twenty-nine kinds of immune cells and the activation of immune function in HCC using the ssGSEA algorithm. The results showed that aDCs, iDCs, macrophages, Tfh, Th1, Treg, and NK cells were correlated with the progress of HCC patients. And the immune functions including APC costimulation, CCR, check point, HLA, MHC class I, and Type II IFN responses were also significantly different between the high-risk and low-risk groups. In conclusion, our study identified a nine-lncRNA signature with potential prognostic value for patients with HCC, which could be used as a new biomarker for the diagnosis and immunotherapy of HCC.

scholarly journals SNHG3/miR-148a-3p Axis–mediated High Expression of DNMT1 Correlates with Poor Prognosis and Tumor Immune Infiltration of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Ninghua Yao ◽  
Wei Jiang ◽  
Jie Sun ◽  
Chen Yang ◽  
Wenjie Zheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Immune Cell Infiltration ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Immune Infiltration ◽  
Computer Analyses ◽  
Dnmt1 Expression

Abstract Background Epigenetic reprogramming plays an important role in the occurrence, development, and prognosis of hepatocellular carcinoma (HCC). DNA methylation is a key epigenetic regulatory mechanism, and DNA methyltransferase 1 (DNMT1) is the major enzyme responsible for maintenance methylation. Nevertheless, the role and mechanism of DNMT1 in HCC remains poorly defined. Methods In the current study, we conducted pan-cancer analysis for DNMT1’s expression and prognosis using The Cancer Genome Atlas (TCGA) data set. We conducted gene Set Enrichment Analysis (GSEA) between high-and-low DNMT1 expression groups to identify DNMT1-related functional significance. We also investigated the relationship between DNMT1 expression and tumor immune microenvironment, including immune cell infiltration and the expression of immune checkpoints. Through a combination series of computer analyses (including expression analyses, correlation analyses, and survival analyses), the noncoding RNAs (ncRNAs) that contribute to the overexpression of DNMT1 were ultimately identified. Results We found that DNMT1 was upregulated in 16 types of human carcinoma including HCC, and DNMT1 might be a biomarker predicting unfavorable prognosis in HCC patients. DNMT1 mRNA expression was statistically associated with age, histological grade, and the level of serum AFP. Moreover, DNMT1 level was significantly and positively linked to tumor immune cell infiltration, immune cell biomarkers, and immune checkpoint expression. Meanwhile, Gene Set Enrichment Analysis (GSEA) revealed that high-DNMT1 expression was associated with epithelial mesenchymal transition (EMT), E2F target, G2M checkpoint, and inflammatory response. Finally, through a combination series of computer analyses the SNHG3/hsa-miR-148a-3p/DNMT1 axis was confirmed as the potential regulatory pathway in HCC. Conclusion SNHG3/miR-148a-3p axis upregulation of DNMT1 may be related to poor outcome, tumor immune infiltration, and regulated malignant properties in HCC.

scholarly journals Identification of a novel six autophagy-related genes signature for the prognostic and a miRNA-related autophagy predictor for anti-PD-1 therapy responses in prostate cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lei Wu ◽  
Wen Quan ◽  
Guojun Yue ◽  
Qiong Luo ◽  
Dongxu Peng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Regression Model ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Potential Biomarker ◽  
Risk Patients

Abstract Background Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear. Methods An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2–3). Based on STRING analysis results, we found that the NKX2–3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2–3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2–3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa. Results We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2–3). Further analysis demonstrated that NKX2–3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa. Conclusions The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

scholarly journals 8DEstablishment and validation of a hypoxia-related signature predicting prognosis in hepatocellular carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Congbo Cai ◽  
Lei Yang ◽  
Kena Zhou
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Prognostic Factor ◽  
Tumor Microenvironment ◽  
Crucial Role ◽  
Prognostic Model ◽  
Immune Infiltration ◽  
Risk Patients ◽  
Immunosuppressive Microenvironment

Abstract Background Hypoxia plays a crucial role in immunotherapy of hepatocellular carcinoma (HCC) by changing the tumor microenvironment. Until now the association between hypoxia genes and prognosis of HCC remains obscure. We attempt to construct a hypoxia model to predict the prognosis in HCC. Results We screened out 3 hypoxia genes (ENO1, UGP2, TPI1) to make the model, which can predict prognosis in HCC. And this model emerges as an independent prognostic factor for HCC. A Nomogram was drawn to evaluate the overall survival in a more accurate way. Furthermore, immune infiltration state and immunosuppressive microenvironment of the tumor were detected in high-risk patients. Conclusion We establish and validate a risk prognostic model developed by 3 hypoxia genes, which could effectively evaluate the prognosis of HCC patients. This prognostic model can be used as a guidance for hypoxia modification in HCC patients undergoing immunotherapy.

scholarly journals Identification of a novel six autophagy-related genes signature for the prognostic and a miRNA-related autophagy predictor for anti-PD-1 therapy responses in prostate cancer

2021 ◽  
Author(s):  
Lei Wu ◽  
Wen Quan ◽  
Guojun Yue ◽  
Qiong Luo ◽  
Dongxu Peng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Regression Model ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Potential Biomarker ◽  
Risk Patients

Abstract Background: Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear.Methods: An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2-3). Based on STRING analysis results, we found that the NKX2-3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2-3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2-3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa.Results: We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2-3). Further analysis demonstrated that NKX2-3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa.Conclusions: The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

A nomogram based on Psoas muscle index and prognostic nutritional index predicts the prognosis of intrahepatic cholangiocarcinoma after surgery: a multi-center cohort study

2021 ◽  
Author(s):  
Wenming Bao ◽  
Liming Deng ◽  
haitao Yu ◽  
bangjie He ◽  
Zixia Lin ◽  
...  
Keyword(s):  
High Risk ◽  
Intrahepatic Cholangiocarcinoma ◽  
Poor Prognosis ◽  
High Risk Patient ◽  
Malignant Neoplasm ◽  
Psoas Muscle ◽  
Prognostic Nutritional Index ◽  
Nutritional Index ◽  
Risk Patients ◽  
Patient Prognosis

Abstract Background Intrahepatic cholangiocarcinoma (ICC) is a malignant neoplasm with a poor prognosis. Prediction of prognosis is critical for the individualized clinical management of patients with ICC. The purpose of this study is to establish a nomogram based on the psoas muscle index (PMI) and prognostic nutritional index (PNI) to identify the high risk-patient with ICC after curative resection. Methods ICC Patients after hepatectomy in multi-hospital from August 2012 to October 2019 were enrolled. The overall survival (OS) and recurrence-free survival (RFS) rates were analyzed by Kaplan-Meier. The independent factors were identified by univariate and multivariate Cox regression analyses. A nomogram based on independent factors was established to predict ICC patient prognosis. Results 178 ICC patients were included. The OS was worst in the patients with a combination of low PMI combined low PNI (p < 0.01). PMI, PNI, lymph node metastasis and tumor differentiation were the independent prognostic risk factors; these factors were used to establish the nomogram was established by it. The calibration curve revealed that the nomogram survival probability prediction model was in good agreement with the actual observation results. The nomogram has good reliability in predicting ICC patient prognosis (OS C-index = 0.692). The area under the receiver operating characteristic curve (AUC) for the nomogram's 3-year predicted survival was 0.752. Based on the stratified by nomogram, the median survival for low-risk patients was 59.8 months, compared with 16.2 months for high-risk patients (p༜0.001). Conclusion The nomogram based on the PMI and PNI can identify patients with the highest risk of poor prognosis after curative hepatectomy. It is a good decision-making tool for individualized treatment.

scholarly journals High Expression of PRMT6 Associated With Prognosis and Immune Infiltration in Glioma

2020 ◽  
Author(s):  
Yi Yang ◽  
Zhenshuang Wang ◽  
Shengrong Long ◽  
Jinhai Huang ◽  
Chengran Xu ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Clinical Work ◽  
High Expression ◽  
Clinical Indicators ◽  
Immune Infiltration ◽  
Tumor Tissues ◽  
Potential Biomarker ◽  
Patient Prognosis ◽  
The Relationship

Abstract Background: Gliomas are characterised by easy invasion of surrounding tissues, high mortality and poor prognosis. Moreover, with the increase of grade, the prognosis of glioma is increasingly poor and not optimistic. Therefore, biological markers for glioma are needed in clinical work, which can be utilized to detect and evaluate the situation and prognosis of glioma patients. Many studies have found that the protein arginine methyltransferase 6 (PRMT6) expression is elevated in various tumors and is associated with patient prognosis. However, the role of PRMT6 in glioma has not been reported or analyzed. Methods: In this study, we used a variety of tumor related databases to analyze the mechanism of PRMT6 in tumors, especially gliomas, from the perspective of bioinformatics, and carried out relevant experimental verification with tumor tissues extracted from patients during surgery. In addition, we analyzed the relationship between PRMT6 expression and immune infiltration and immune-related cells, and discussed the possible mechanisms. We also discussed the role of PRMT6 expression in glioma from the perspectives of mutation, clinical indicators, enrichment analysis, and immunohistochemical results. Results: PRMT6 is significantly differentially expressed in a variety of tumors and is associated with survival and prognosis. Especially in gliomas, the expression of PRMT6 gradually increased with the increase of grade. In addition, PRMT6 can be used as an independent prognostic risk factor in the nomogram and has been verified in a variety of databases. Conclusions: Our results indicate that high expression of PRMT6 is a potential biomarker for predicting glioma prognosis and progression.

scholarly journals Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

2020 ◽  
Author(s):  
Lili Fan ◽  
Han Lei ◽  
Ying Lin ◽  
Zhengwei Zhou ◽  
Guang Shu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Immune Cell ◽  
Good Prognosis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Survival Prognosis ◽  
Immune Activity ◽  
Gene Set ◽  
Immune Infiltration ◽  
Keywords Ovarian Cancer

Abstract Background : Ovarian cancer (OC) is a serious tumor disease in gynecology. Many papers have reported that high tumor mutational burden (TMB) can generate many neoantigens to result in a higher degree of tumor immune infiltration, so our study aims to predict the key molecules in OC immunotherapy by combined TMB with immunoactivity-related gene. Method: We divided OC cases into two groups: the low & high TMB group hinged on the somatic mutation data from the Cancer Genome Atlas (TCGA). We also used single-sample gene set enrichment analysis (ssGSEA) scores of immune cell types to conduct unsupervised clustering of OC patients in the TCGA cohort and some of them were defined as the low & high immunity group. Besides, to further understand the function of these genes, we conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, protein-protein interaction network, survival prognosis analysis and immune infiltration analysis. Finally, the effects on prognosis and immunotherapy in OC patients were explored by the Group on Earth Observations verification the patients' responses to immunotherapy. Results: We found that the higher the TMB was associated with the higher OC grades. Moreover, both high TMB and high immunity were significantly correlated with a good prognosis of OC. Then, 14 up-regulated differential expression genes (Up-DEGs) that were closely related to the prognosis of OC patients were screened according to the high TMB group and the high immunity group. Next, pathway analysis revealed that Up-DGEs were mainly involved in immune response and T cell proliferation. Finally, four genes had a good prognosis and were validated in the GEO dataset which included CXCL13, FCRLA, PLA2G2D, and MS4A1. We also identified that four genes had a good prognosis in melanoma patients treated with anti-PD-L1 and anti-CTLA-4 in the TIDE database. Conclusion: High TMB can promote immune cell infiltration and increases immune activity. And our analysis also demonstrated that the higher the TMB, the higher the immune activity, the better the prognosis of OC. Altogether, we found that CXCL13, FCRLA, PLA2G2D, and MS4A1 may be biomarkers for OC immunotherapy. Keywords: ovarian cancer, TMB, immune cells infiltration, survival prognosis.

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