Seven Genes Based Novel Signature Predicts Clinical Outcome and Platinum Sensitivity of High Grade IIIc Serous Ovarian Carcinoma

Liu, Gang; Chen, Lihua; Ren, He; Liu, Fei; Dong, Chuanpeng; Wu, Aosen; Liu, Zhenhao; Zheng, Yu; Cheng, Xi; Liu, Lei

doi:10.7150/ijbs.28249

Cited by 14 publications

(22 citation statements)

References 38 publications

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“…Yu and his colleagues identified of differential AS genes by expression profiling in gliomas [25]. Novel signature (seven genes) indicated clinical reaction and platinum sensitivity of serous ovarian carcinoma [26]. Xie et al studied prognostic splicing signatures and revealed regulatory network in esophageal carcinoma [27].…”

Section: Discussionmentioning

confidence: 99%

Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer

Wang

et al. 2020

J. Cancer

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Background: Pancreatic cancer (PC) is an aggressive cancer with worse survival in the world. Emerging evidence suggested that the imbalance of alternative splicing (AS) is a hallmark of cancer and indicated poor prognosis of patients. Genes-derived splicing events can produce neoepitopes for immunotherapy. However, the profound study of splicing profiling in PC is still elusive. We aimed to identification of novel prognostic signature across a comprehensive splicing landscape and reveal their relationship with tumor-infiltrating immune cells in pancreatic cancer microenvironment. Methods: Based on integrated analysis of splicing profiling and clinical data, differentially splicing events were filtered out. Then, stepwise Cox regression analysis was applied to identify survival-related splicing events and construct prognostic signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognostic signature and infiltrating immune cells by CIBERSORT. Results: According to systematic analyzing, a final six splicing events were identified and validated the good prognostic capability in entire TCGA dataset, validation set 1 and validation set 2 by Kaplan-Meier curves (P < 0.0001). The area under the curve (AUC) of ROC curves were also confirmed the high predictive efficiency of the prognostic signature in these three cohorts (AUC = 0.857, 0.895 and 0.788). In order to validate whether prognostic signature highlights a correlation between AS and immune contexture, CIBERSORT was performed to analyze the proportion of tumor-infiltrating immune cells in PC. Based on prognostic signature, we identified survival-related immune cells including CD8 T cells (P = 0.0111), activated CD4 memory T cells (P = 0.0329) and resting mast cells (P = 0.0352). Conclusion: In conclusion, our study contribute to provide a promising prognostic signature based on six splicing events and revealed prognosis-related immune cells which indeed represented novel tumor drivers and provide potential targets for personalized therapeutic.

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Section: Discussionmentioning

confidence: 99%

Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer

Wang

et al. 2020

J. Cancer

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“…Multiple gene signatures have been developed to predict overall survival, recurrence, and platinum sensitivity in OC. Some gene signatures were also used to predict the overall survival outcome in SOC [14,35,36]. Liu et al developed a seven gene-based novel signature to predict clinical outcomes of High-Grade IIIc Serous Ovarian Carcinoma with an AUC of 0.715 in the TCGA cohort [35].…”

Section: Discussionmentioning

confidence: 99%

“…Some gene signatures were also used to predict the overall survival outcome in SOC [14,35,36]. Liu et al developed a seven gene-based novel signature to predict clinical outcomes of High-Grade IIIc Serous Ovarian Carcinoma with an AUC of 0.715 in the TCGA cohort [35]. A genomic rearrangement signature was constructed in High-Grade Serous Ovarian Cancer (HGSOC) with an AUC of 0.63 in the TCGA cohort [36].…”

Section: Discussionmentioning

confidence: 99%

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

Liu

et al. 2020

Preprint

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

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“…Liu et al established a prognostic model based on seven genes. After systematic analysis, they indicated that the optimized seven-gene-based model can be used as a valuable marker of the response to platinum-based chemotherapy and a valuable and powerful tool for predicting the survival of FIGO IIIc serous ovarian cancer (Liu et al, 2018 ). Most of the studies mentioned above focused on the differences between platinum-resistant or -sensitive patients through comparison and excluded low-throughput experiments.…”

Section: Introductionmentioning

confidence: 99%

PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients

Xing

Tian

et al. 2021

Front. Cell Dev. Biol.

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Ovarian cancer is the most frequent cause of death among gynecologic malignancies. A total of 80% of patients who have completed platinum-based chemotherapy suffer from relapse and develop resistance within 2 years. In the present study, we obtained patients' complete platinum (cisplatin and carboplatin) medication information from The Cancer Genome Atlas database and then divided them into two categories: resistance and sensitivity. Difference analysis was performed to screen differentially expressed genes (DEgenes) related to platinum response. Subsequently, we annotated DEgenes into the protein–protein interaction network as seed nodes and analyzed them by random walk. Finally, second-ranking protease serine 1 gene (PRSS1) was selected as a candidate gene for verification analysis. PRSS1's expression pattern was continuously studied in Oncomine and cBio Cancer Genomic Portal databases, revealing the key roles of PRSS1 in ovarian cancer formation. Hereafter, we conducted in-depth explorations on PRSS1's platinum response to ovarian cancer through tissue and cytological experiments. Quantitative real-time polymerase chain reaction and Western blot assay results indicated that PRSS1 expression levels in platinum-resistant samples (tissue/cell) were significantly higher than in samples sensitive to platinum. By cell transfection assay, we observed that knockdown of PRSS1 reduced the resistance of ovarian cancer cells to cisplatin. Meanwhile, overexpression of PRSS1 increased the resistance to cisplatin. In conclusion, we identified a novel risk gene PRSS1 related to ovarian cancer platinum response and confirmed its key roles using multiple levels of low-throughput experiments, revealing a new treatment strategy based on a novel target factor for overcoming cisplatin resistance in ovarian cancer.

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Seven Genes Based Novel Signature Predicts Clinical Outcome and Platinum Sensitivity of High Grade IIIc Serous Ovarian Carcinoma

Cited by 14 publications

References 38 publications

Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer

Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer

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

PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients

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