scholarly journals A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas Brian Shannon ◽  
Laura Ling Ying Tan ◽  
Qiu Xuan Tan ◽  
Joey Wee-Shan Tan ◽  
Josephine Hendrikson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Molecular Markers ◽  
Surgical Resection ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Validation Dataset ◽  
Gradient Boosting ◽  
Platinum Resistance ◽  
Extreme Gradient Boosting

AbstractOvarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplatin sensitivity was generated using the Genomics of Drug Sensitivity in Cancer and The Cancer Genome Atlas databases. Four potential biomarkers (CYTH3, GALNT3, S100A14, and ERI1) were identified and optimized for immunohistochemistry (IHC). Validation was performed on a cohort of patients (n = 50) treated with surgical resection followed by adjuvant carboplatin. Predictive models were established to predict chemosensitivity. The four biomarkers were also assessed for their ability to prognosticate overall survival in three ovarian cancer microarray expression datasets from The Gene Expression Omnibus. The extreme gradient boosting (XGBoost) algorithm was selected for the final model to validate the accuracy in an independent validation dataset (n = 10). CYTH3 and S100A14, followed by nodal stage, were the features with the greatest importance. The four gene signature had comparable prognostication as clinical information for two-year survival. Assessment of tumour biology by means of gene expression can serve as an adjunct for prediction of chemosensitivity and prognostication. Potentially, the assessment of molecular markers alongside clinical information offers a chance to further optimise therapeutic decision making.

scholarly journals Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guoliang Jia ◽  
Zheyu Song ◽  
Zhonghang Xu ◽  
Youmao Tao ◽  
Yuanyu Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cutaneous Melanoma ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Validation Dataset ◽  
Survival Prognosis ◽  
Gene Markers ◽  
Clinical Prognosis ◽  
Logit Regression

Abstract Background Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. Methods We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein–protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Results Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. Conclusion The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

scholarly journals Transcriptome Analysis Reveals Significant Differences in Gene Expression of Malignant Pheochromocytoma or Paraganglioma

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Joon Suh ◽  
Jung Ho Park ◽  
Sanchir-Erdene Bilegsaikhan ◽  
Dong Jin Lee
Keyword(s):  
Mrna Expression ◽  
Surgical Resection ◽  
Transcriptome Analysis ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Eligibility Criteria ◽  
Mrna Expression Data ◽  
Cancer Genome Atlas ◽  
Tumeurs Endocrines ◽  
Upregulated Genes

Prediction of malignant behavior of pheochromocytoma (PC) or paraganglioma (PG) is of limited value. The Cancer Genome Atlas (TCGA) and the French ‘Cortico et Médullosurrénale: les Tumeurs Endocrines’ (COMETE) network in Paris (France) facilitate accurate differentiation of malignant PC/PG based on genetic information. Therefore, the objective of this transcriptome analysis is to identify the prognostic genes underlying the differentiation of malignant PC/PG in the TCGA and COMETE databases. TCGA carries data pertaining to multigenomic analysis of 173 PC/PG surgical resection samples while the COMETE cohort contains data involving 188 PC/PG surgical resection samples. Clinical information and mRNA expression datasets were downloaded from TCGA and COMETE databases. Based on eligibility criteria, 58 of 173 PC/PG samples in TCGA and 171 of 188 PC/PG samples collected by the COMETE network were selected. Using Ingenuity Pathway Analysis, the mRNA expression of malignant and benign PC/PG was compared. The 58 samples in TCGA included 11 malignant and 47 benign cases. Among the 171 samples obtained from the COMETE cohort, 19 were malignant and 152 were benign. A comparative analysis of the mRNA expression data of the two databases revealed that 11 up/downregulated pathways involved in malignant PC/PG were related to cancer signaling, metabolic alteration, and prominent mitosis, whereas 6 upregulated genes and 1 downregulated gene were significantly enriched in the functional annotation pathways. The TCGA and COMETE databases showed differences in mRNA expression associated with malignant and benign PC/PG. Improved recognition of prognostic genes facilitates the diagnosis and treatment of PC/PG.

scholarly journals Predictive value of m5C regulatory gene expression in pancreatic adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiao Yu ◽  
Qiyao Zhang ◽  
Fang Gao ◽  
Menggang Zhang ◽  
Qingyuan Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Adenocarcinoma ◽  
Regulatory Gene ◽  
Copy Number Variations ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Validation Dataset ◽  
Genes Encoding ◽  
Patient Prognosis ◽  
Regulated Gene Expression

AbstractPancreatic adenocarcinoma (PAAD) is the most malignant digestive tumor. The global incidence of pancreatic cancer has been rapidly trending upwards, necessitating an exploration of potential prognostic biomarkers and mechanisms of disease development. One of the most prevalent RNA modifications is 5-methylcytosine (m5C); however, its contribution to PAAD remains unclear. Data from The Cancer Genome Atlas (TCGA) database, including genes, copy number variations (CNVs), and simple nucleotide variations (SNVs), were obtained in the present study to identify gene signatures and prognostic values for m5C regulators in PAAD. Regulatory gene m5C changes were significantly correlated with TP53, BRCA1, CDKN2A, and ATM genes, which play important roles in PAAD pathogenesis. In particular, there was a significant relationship between m5C regulatory gene CNVs, especially in genes encoding epigenetic “writers”. According to m5C-regulated gene expression in clinically graded cases, one m5C-regulated genes, DNMT3A, showed both a strong effect on CNVs and a significant correlation between expression level and clinical grade (P < 0.05). Furthermore, low DNMT3A expression was not only associated with poor PAAD patient prognosis but also with the ribosomal processing. The relationship between low DNMT3A expression and poor prognosis was confirmed in an International Cancer Genome Consortium (ICGC) validation dataset.

scholarly journals A network view of microRNA and gene interactions in different pathological stages of colon cancer

2019 ◽  
Vol 12 (S7) ◽  
Author(s):  
Jia Wen ◽  
Benika Hall ◽  
Xinghua Shi
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Gene Interactions ◽  
New Strategy ◽  
Pathological Stages

Abstract Background Colon cancer is one of the common cancers in human. Although the number of annual cases has decreased drastically, prognostic screening and translational methods can be improved. Hence, it is critical to understand the molecular mechanisms of disease progression and prognosis. Results In this study, we develop a new strategy for integrating microRNA and gene expression profiles together with clinical information toward understanding the regulation of colon cancer. Particularly, we use this approach to identify microRNA and gene expression networks that are specific to certain pathological stages. To demonstrate the application of our method, we apply this approach to identify microRNA and gene interactions that are specific to pathological stages of colon cancer in The Cancer Genome Atlas (TCGA) datasets. Conclusions Our results show that there are significant differences in network connections between miRNAs and genes in different pathological stages of colon cancer. These findings point to a hypothesis that these networks signify different roles of microRNA and gene regulation in the pathogenesis and tumorigenesis of colon cancer.

An integrated model of clinical information and gene expression for prediction of survival in ovarian cancer patients

2016 ◽  
Vol 172 ◽  
pp. 84-95.e11 ◽  
Author(s):  
Rendong Yang ◽  
Jie Xiong ◽  
Defeng Deng ◽  
Yiren Wang ◽  
Hequn Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cancer Patients ◽  
Clinical Information ◽  
Integrated Model ◽  
Prediction Of Survival

PredCID: prediction of driver frameshift indels in human cancer

2020 ◽  
Author(s):  
Zhenyu Yue ◽  
Xinlu Chu ◽  
Junfeng Xia
Keyword(s):  
Cancer Biology ◽  
Missing Values ◽  
Human Cancer ◽  
Computational Method ◽  
Genomic Research ◽  
Validation Dataset ◽  
Gradient Boosting ◽  
Cancer Driver ◽  
Extreme Gradient Boosting ◽  
Frameshift Indels

Abstract The discrimination of driver from passenger mutations has been a hot topic in the field of cancer biology. Although recent advances have improved the identification of driver mutations in cancer genomic research, there is no computational method specific for the cancer frameshift indels (insertions or/and deletions) yet. In addition, existing pathogenic frameshift indel predictors may suffer from plenty of missing values because of different choices of transcripts during the variant annotation processes. In this study, we proposed a computational model, called PredCID (Predictor for Cancer driver frameshift InDels), for accurately predicting cancer driver frameshift indels. Gene, DNA, transcript and protein level features are combined together and selected for classification with eXtreme Gradient Boosting classifier. Benchmarking results on the cross-validation dataset and independent dataset showed that PredCID achieves better and robust performance compared with existing noncancer-specific methods in distinguishing cancer driver frameshift indels from passengers and is therefore a valuable method for deeper understanding of frameshift indels in human cancer. PredCID is freely available for academic research at http://bioinfo.ahu.edu.cn:8080/PredCID.

scholarly journals PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Linan Xing ◽  
Songyu Tian ◽  
Wanqi Mi ◽  
Yongjian Zhang ◽  
Yunyan Zhang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Ovarian Cancer Cells ◽  
Tissue Cell ◽  
Platinum Resistance ◽  
Western Blot Assay ◽  
Protein Protein Interaction ◽  
Platinum Based Chemotherapy ◽  
Novel Target

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.

scholarly journals Prognostic values of GMPS, PR, CD40, and p21 in ovarian cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6301 ◽  
Author(s):  
Ping Wang ◽  
Zengli Zhang ◽  
Yujie Ma ◽  
Jun Lu ◽  
Hu Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cancer Patients ◽  
Differential Expression ◽  
Cancer Progression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Progression Model ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Early detection and prediction of prognosis and treatment responses are all the keys in improving survival of ovarian cancer patients. This study profiled an ovarian cancer progression model to identify prognostic biomarkers for ovarian cancer patients. Mouse ovarian surface epithelial cells (MOSECs) can undergo spontaneous malignant transformation in vitro cell culture. These were used as a model of ovarian cancer progression for alterations in gene expression and signaling detected using the Illumina HiSeq2000 Next-Generation Sequencing platform and bioinformatical analyses. The differential expression of four selected genes was identified using the gene expression profiling interaction analysis (http://gepia.cancer-pku.cn/) and then associated with survival in ovarian cancer patients using the Cancer Genome Atlas dataset and the online Kaplan–Meier Plotter (http://www.kmplot.com) data. The data showed 263 aberrantly expressed genes, including 182 up-regulated and 81 down-regulated genes between the early and late stages of tumor progression in MOSECs. The bioinformatic data revealed four genes (i.e., guanosine 5′-monophosphate synthase (GMPS), progesterone receptor (PR), CD40, and p21 (cyclin-dependent kinase inhibitor 1A)) to play an important role in ovarian cancer progression. Furthermore, the Cancer Genome Atlas dataset validated the differential expression of these four genes, which were associated with prognosis in ovarian cancer patients. In conclusion, this study profiled differentially expressed genes using the ovarian cancer progression model and identified four (i.e., GMPS, PR, CD40, and p21) as prognostic markers for ovarian cancer patients. Future studies of prospective patients could further verify the clinical usefulness of this four-gene signature.

scholarly journals Evaluation of FGFR1 as a diagnostic biomarker for ovarian cancer using TCGA and GEO datasets

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10817
Author(s):  
Huiting Xiao ◽  
Kun Wang ◽  
Dan Li ◽  
Ke Wang ◽  
Min Yu
Keyword(s):  
Ovarian Cancer ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Residual Tumor ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Biomarker

Background Malignant ovarian cancer is associated with the highest mortality of all gynecological tumors. Designing therapeutic targets that are specific to OC tissue is important for optimizing OC therapies. This study aims to identify different expression patterns of genes related to FGFR1 and the usefulness of FGFR1 as diagnostic biomarker for OC. Methods We collected data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. In the TCGA cohort we analyzed clinical information according to patient characteristics, including age, stage, grade, longest dimension of the tumor and the presence of a residual tumor. GEO data served as a validation set. We obtained data on differentially expressed genes (DEGs) from the two microarray datasets. We then used gene set enrichment analysis (GSEA) to analyze the DEG data in order to identify enriched pathways related to FGFR1. Results Differential expression analysis revealed that FGFR1 was significantly downregulated in OC specimens. 303 patients were included in the TCGA cohort. The GEO dataset confirmed these findings using information on 75 Asian patients. The GSE105437 and GSE12470 database highlighted the significant diagnostic value of FGFR1 in identifying OC (AUC = 1, p = 0.0009 and AUC = 0.8256, p = 0.0015 respectively). Conclusions Our study examined existing TCGA and GEO datasets for novel factors associated with OC and identified FGFR1 as a potential diagnostic factor. Further investigation is warranted to characterize the role played by FGFR1 in OC.

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