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

Development and validation of an individualized gene expression-based signature to predict overall survival of patients with high-grade serous ovarian carcinoma

BACKGROUND

High-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis.

METHODS

The RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets.

RESULTS

A total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples.

CONCLUSION

This study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC.

A prognostic risk model for ovarian cancer based on gene expression profiles from gene expression omnibus database

This study explored prognostic genes of ovarian cancer and built a prognostic model based on these genes to predict patient's survival, which is of great significance for improving treatment of ovarian cancer. GSE26712 dataset was downloaded from Gene Expression Omnibus database as training set, while OV-AU dataset was downloaded from ICGC website as validation set. All genes in GSE26712 were analyzed by univariate Cox regression, Lasso regression, and multivariate Cox regression analyses. Then prognosis-related feature genes were screened to construct a multivariate risk model. Meanwhile, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed on samples in the high/low-risk groups using Gene Set Enrichment Analysis (GSEA) software. Finally, survival curve and receiver operating characteristic curve were drawn to verify the validity of the model. Ten feature genes related to prognosis of ovarian cancer were obtained: CMTM6, COLGALT1, F2R, GPR39, IGFBP3, RNF121, MTMR9, ORAI2, SNAI2, ZBTB16. GSEA enrichment analysis showed that there were notable differences in biological pathways such as gap junctions and homologous recombination between the high/low-risk groups. Through further verification of training set and validation set, the 10-gene prognostic model was found to be effective for the prognosis of ovarian cancer patients. In this study, we constructed a 10-gene prognostic model which predicted the prognosis of ovarian cancer patients well by integrating clinical prognostic parameters. It may have certain reference value for subsequent clinical treatment research of ovarian cancer patients and help in clinical treatment decision-making.

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Background

Ovarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.

Methods

In this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.

Results

Forty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.

Conclusion

These findings reveal a specific risk model that could serve as effective biomarkers to identify patients’ platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00969-3.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

Background

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

Methods

The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

Results

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

Conclusion

Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

A Combined Long Noncoding RNA Signature as a Candidate Prognostic Biomarker for Ovarian Cancer

Aims

Dysregulated long noncoding RNAs (lncRNAs) contributing to ovarian cancer (OC) development may serve as prognostic biomarker. We aimed to explore a lncRNA signature to serve as prognostic biomarker of OC.

Methods

Univariate Cox regression was conducted on the lncRNA expression dataset from the TCGA cohort, and 246 genes significantly associated with survival were retained for building a model. A random forest survival model was carried out, and a model was developed using 6 genes with the highest frequency. The selected genes were applied in a Cox multivariate regression model for prognostic prediction by calculating the risk score. We also used CCK-8, EdU, and colony formation assays to validate the function of these lncRNAs in OC cells.

Results

This study confirmed that the 6-lncRNA combined signature was related to OC prognosis. Systematic analysis demonstrated that lncRNA-associated genes were enriched in oncogenic signalling pathways. Five out of the 6 lncRNAs participated in OC proliferation.

Conclusion

We established a 6-lncRNA combined signature for OC prognosis, which may serve as powerful prognostic biomarker for OC after further validation.

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

Growing evidence suggest that transcription factors (TFs) play vital roles in serous ovarian cancer (SOC). In the present study, TFs mRNA expression profiles of 564 SOC subjects in the TCGA database, and 70 SOC subjects in the GEO database were screened. A 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 overall survival of SOC. Analysis of genomic alterations were used to elaborate on the association between the 17-TFs related prognostic signature and genomic aberrations. 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. The potential 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. The results indicated 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. In 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.

Development and verification of an immune-related gene pairs prognostic signature in ovarian cancer

Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17-IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17-IRGP signature noticeably split patients into high- and low-risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll-like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17-IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.

PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients

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.

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

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.

A Novel Scoring System for Pivotal Autophagy-Related Genes Predicts Outcomes after Chemotherapy in Advanced Ovarian Cancer Patients

BACKGROUND

In the clinical practice of ovarian cancer, the application of autophagy, an important regulator of carcinogenesis and chemoresistance, is still limited. This study aimed to establish a scoring system based on expression profiles of pivotal autophagy-related (ATG) genes in patients with stage III/IV ovarian cancer who received chemotherapy.

METHODS

Data of ovarian serous cystadenocarcinoma in The Cancer Genome Atlas (TCGA-OV) were used as training dataset. Two validation datasets comprised patients in a Chinese local database and a dataset from the Gene Expression Omnibus (GEO). ATG genes significantly (P < 0.1) associated with overall survival (OS) were selected and aggregated into an ATG scoring scale, of which the abilities to predict OS and recurrence-free survival (RFS) were examined.

RESULTS

Forty-three ATG genes were selected to develop the ATG score. In TCGA-OV, patients with lower ATG scores had better OS [HR = 0.41; 95% confidence interval (CI), 0.26-0.65; P < 0.001] and RFS [HR = 0.47; 95% CI, 0.27-0.82; P = 0.007]. After complete or partial remission to primary therapy, the rate of recurrence was 47.2% in the low-score group and 68.3% in the high-score group (odds ratio = 0.42; 95% CI, 0.18-0.92; P = 0.03). Such findings were verified in the two validation datasets.

CONCLUSIONS

We established a novel scoring system based on pivotal ATG genes, which accurately predicts the outcomes of patients with advanced ovarian cancer after chemotherapy.

IMPACT

The present ATG scoring system may provide a novel perspective and a promising tool for the development of personalized therapy in the future.