Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Histaminergic System and Inflammation-Related Genes in Normal Large Intestine and Adenocarcinoma Tissues: Transcriptional Profiles and Relations

Transcriptional analyses such as microarray data have contributed to the progress in the diagnostics and therapy of colorectal cancer (CRC). The need for such research is still present because of the disease being common in both men and women with a high second position in cancer rankings. Little is known about the relations between the histaminergic system and inflammation in the large intestine and CRC. Therefore, the aim of this study was to evaluate the expression of genes related to the histaminergic system and inflammation in the CRC tissues at three cancer development designs: all tested CRC samples, low (LCS) and high (HCS) clinical stage, and four clinical stages (CSI-CSIV), to the control. The research was carried out at the transcriptomic level, analysing hundreds of mRNAs from microarrays, as well as carrying out RT-PCR analysis of histaminergic receptors. The following histaminergic mRNAs: GNA15, MAOA, WASF2A, and inflammation-related: AEBP1, CXCL1, CXCL2, CXCL3, CXCL8, SPHK1, TNFAIP6, were distinguished. Among all analysed transcripts, AEBP1 can be considered the most promising diagnostic marker in the early stage of CRC. The results showed 59 correlations between differentiating genes of the histaminergic system and inflammation in the control, control and CRC, and CRC. The tests confirmed the presence of all histamine receptor transcripts in both the control and colorectal adenocarcinoma. Significant differences in expression were stated for HRH2 and HRH3 in the advanced stages of CRC adenocarcinoma. The relations between the histaminergic system and inflammation-linked genes in both the control and the CRC have been observed.

CCL23 in Balancing the Act of Endoplasmic Reticulum Stress and Antitumor Immunity in Hepatocellular Carcinoma

Endoplasmic reticulum (ER) stress is a cellular process in response to stress stimuli in protecting functional activities. However, sustained hyperactive ER stress influences tumor growth and development. Hepatocytes are enriched with ER and highly susceptible to ER perturbations and stress, which contribute to immunosuppression and the development of aggressive and drug-resistant hepatocellular carcinoma (HCC). ER stress-induced inflammation and tumor-derived chemokines influence the immune cell composition at the tumor site. Consequently, a decrease in the CCL23 chemokine in hepatic tumors is associated with poor survival of HCC patients and could be a mechanism hepatic tumor cells use to evade the immune system. This article describes the prospective role of CCL23 in alleviating ER stress and its impact on the HCC tumor microenvironment in promoting antitumor immunity. Moreover, approaches to reactivate CCL23 combined with immune checkpoint blockade or chemotherapy drugs may provide novel opportunities to target hepatocellular carcinoma.

Downregulation of GNA15 Inhibits Cell Proliferation via P38 MAPK Pathway and Correlates with Prognosis of Adult Acute Myeloid Leukemia With Normal Karyotype

Background

The prognosis of acute myeloid leukemia (AML) with a normal karyotype is highly heterogonous, and the current risk stratification is still insufficient to differentiate patients from high-risk to standard-risk. Changes in some genetic profiles may contribute to the poor prognosis of AML. Although the prognostic value of G protein subunit alpha 15 (GNA15) in AML has been reported based on the GEO (Gene Expression Omnibus) database, the prognostic significance of GNA15 has not been verified in clinical samples. The biological functions of GNA15 in AML development remain open to investigation. This study explored the clinical significance, biological effects and molecular mechanism of GNA15 in AML.

Methods

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the mRNA expression level of GNA15 in blasts of bone marrow specimens from 154 newly diagnosed adult AML patients and 26 healthy volunteers. AML cell lines, Kasumi-1 and SKNO-1, were used for lentiviral transfection. Cell Counting Kit-8 (CCK8) and colony formation assays were used to determine cell proliferation. Cell cycle and apoptosis were analyzed by flow cytometry. The relevant signaling pathways were evaluated by Western blot. The Log-Rank test and Kaplan-Meier were used to evaluate survival rate, and the Cox regression model was used to analyze multivariate analysis. Xenograft tumor mouse model was used for in vivo experiments.

Results

The expression of GNA15 in adult AML was significantly higher than that in healthy individuals. Subjects with high GNA15 expression showed lower overall survival and relapse-free survival in adult AML with normal karyotype. High GNA15 expression was independently correlated with a worse prognosis in multivariate analysis. Knockdown of GNA15 inhibited cell proliferation and cell cycle progression, and induced cell apoptosis in AML cells. GNA15-knockdown induced down-regulation of p-P38 MAPK and its downstream p-MAPKAPK2 and p-CREB. Rescue assays confirmed that P38 MAPK signaling pathway was involved in the inhibition of proliferation mediated by GNA15 knockdown.

Conclusions

In summary, GNA15 was highly expressed in adult AML, and high GNA15 expression was independently correlated with a worse prognosis in adult AML with normal karyotype. Knockdown of GNA15 inhibited the proliferation of AML regulated by the P38 MAPK signaling pathway. Therefore, GNA15 may serve as a potential prognostic marker and a therapeutic target for AML in the future.

frDriver: A Functional Region Driver Identification for Protein Sequence

Identifying cancer drivers is a crucial challenge to explain the underlying mechanisms of cancer development. There are many methods to identify cancer drivers based on the single mutation site or the entire gene. But they ignore a large number of functional elements with medium in size. It is hypothesized that mutations occurring in different regions of the protein sequence have different effects on the progression of cancer. Here, we develop a novel functional region driver(frDriver) identification method based on Bayesian probability and multiple linear regression models to identify protein regions that can regulate gene expression levels and have high functional impact potential. Combining gene expression data and somatic mutation data, with functional impact scores(SIFT, PROVEAN) as a priori knowledge, we identified cancer driver regions that are most accurate in predicting gene expression levels. We evaluated the performance of frDriver on the BRCA and GBM datasets from TCGA. The results showed that frDriver identified known cancer drivers and outperformed the other three state-of-the-art methods(eDriver, ActiveDriver and OncodriveCLUST). In addition, we performed KEGG pathway and GO term enrichment analysis, and the results indicated that the cancer drivers predicted by frDriver were related to processes such as cancer formation and gene regulation.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

A novel method to identify gene interaction patterns

BACKGROUND

Gene interaction patterns, including modules and motifs, can be used to identify cancer specific biomarkers and to reveal the mechanism of tumorigenesis. Most of the existing module network inferencing methods focus on gene independent functional patterns, while the studies of overlapping characteristics between modules are lacking. The objective of this study was to reveal the functional overlapping patterns in gene modules, helping elucidate the regulatory relationship between overlapping genes and communities, as well as to explore cancer formation and progression.

RESULTS

We analyzed six cancer datasets from The Cancer Genome Atlas and obtained three kinds of gene functional modules for each cancer, including Independent-Community, Dependent-Community and Merged-Community. In the six cancers, 59(3.5%) Independent-Communities were identified, while 1631(96.5%) Dependent-Communities were acquired. Compared with Lemon-Tree and K-Means, the gene communities identified by our method were enriched in more known GO categories with lower p-values. Meanwhile, those identified distinguishing communities can significantly distinguish the survival prognostic of patients by Kaplan-Meier analysis. Furthermore, identified driver genes in the gene communities can be considered as biomarkers which can accurately distinguish the tumour or normal samples for each cancer type.

CONCLUSIONS

In all identified communities, Dependent-Communities are the majority. Our method is more effective than the other two methods which do not consider the overlapping characteristics of modules. This indicates that overlapping genes are located in different specific functional groups, and a communication bridge is established between the communities to construct a comprehensive carcinogenesis.

Identification of Potential Bisphenol A (BPA) Exposure Biomarkers in Ovarian Cancer

Endocrine-disrupting chemicals (EDCs) can exert multiple deleterious effects and have been implicated in carcinogenesis. The xenoestrogen Bisphenol A (BPA) that is found in various consumer products has been involved in the dysregulation of numerous signalling pathways. In this paper, we present the analysis of a set of 94 genes that have been shown to be dysregulated in presence of BPA in ovarian cancer cell lines since we hypothesised that these genes might be of biomarker potential. This study sought to identify biomarkers of disease and biomarkers of disease-associated exposure. In silico analyses took place using gene expression data extracted from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. Differential expression was further validated at protein level using immunohistochemistry on an ovarian cancer tissue microarray. We found that 14 out of 94 genes are solely dysregulated in the presence of BPA, while the remaining 80 genes are already dysregulated (p-value < 0.05) in their expression pattern as a consequence of the disease. We also found that seven genes have prognostic power for the overall survival in OC in relation to their expression levels. Out of these seven genes, Keratin 4 (KRT4) appears to be a biomarker of exposure-associated ovarian cancer, whereas Guanylate Binding Protein 5 (GBP5), long intergenic non-protein coding RNA 707 (LINC00707) and Solute Carrier Family 4 Member 11 (SLC4A11) are biomarkers of disease. BPA can exert a plethora of effects that can be tissue- or cancer-specific. Our in silico findings generate a hypothesis around biomarkers of disease and exposure that could potentially inform regulation and policy making.

Integrated WGCNA and PPI Network to Screen Hub Genes Signatures for Infantile Hemangioma

BACKGROUND

Infantile hemangioma (IH) is characterized by proliferation and regression.

METHODS

Based on the GSE127487 dataset, the differentially expressed genes (DEGs) between 6, 12, or 24 months and normal samples were screened, respectively. STEM software was used to screen the continued up-regulated or down-regulated in common genes. The modules were assessed by weighted gene co-expression network analysis (WGCNA). The enrichment analysis was performed to identified the biological function of important module genes. The area under curve (AUC) value and protein-protein interaction (PPI) network were used to identify hub genes. The differential expression of hub genes in IH and normal tissues was detected by qPCR.

RESULTS

There were 5,785, 4,712, and 2,149 DEGs between 6, 12, and 24 months and normal tissues. We found 1,218 DEGs were up-regulated or down-regulated expression simultaneously in common genes. They were identified as 10 co-expression modules. Module 3 and module 4 were positively or negatively correlated with the development of IH, respectively. These two module genes were significantly involved in immunity, cell cycle arrest and mTOR signaling pathway. The two module genes with AUC greater than 0.8 at different stages of IH were put into PPI network, and five genes with the highest degree were identified as hub genes. The differential expression of these genes was also verified by qRTPCR.

CONCLUSION

Five hub genes may distinguish for proliferative and regressive IH lesions. The WGCNA and PPI network analyses may help to clarify the molecular mechanism of IH at different stages.

GADD45B Facilitates Metastasis of Ovarian Cancer Through Epithelial-Mesenchymal Transition

Background

Growth arrest and DNA-damage-inducible 45 beta (GADD45B) is overexpressed and is associated with poor clinical outcomes in many human cancers, but the clinical implication of GADD45B in epithelial ovarian cancer (EOC) remains unclear.

Methods

Bioinformatics analysis of The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) cohorts was used to illustrate the relationship between GADD45B expression and metastasis, as well as the survival time of EOC. GADD45B was downregulated by siRNAs in EOC cells, and migration ability was determined by a transwell assay and wound-healing assay. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and gene set enrichment analysis (GSEA) were conducted to discover the downstream pathway of GADD45B. The regulation of epithelial–mesenchymal transition (EMT) by GADD45B was verified by Western blotting and qRT-PCR. Finally, the correlation of GADD45B expression with EOC metastasis was investigated in EOC tissues by immunohistochemistry.

Results

Overexpression of GADD45B indicates shorter overall survival time and progression-free survival time, and it is an independent risk factor for poor survival in EOC patients. Elevated GADD45B is related to venous invasion, lymphatic invasion and peritoneal carcinomatosis. Downregulation of GADD45B decreases the migration of ES2 and SKOV3 cells. Further KEGG enrichment analysis and GSEA revealed that EMT may be the downstream pathway of GADD45B. In addition, reduced GADD45B increases the expression of E-cadherin and decreases that of N-cadherin and vimentin. Finally, immunohistochemical analysis of GADD45B expression revealed that the expression of GADD45B in omental metastatic tissues was higher than that in matched primary ovarian cancer tissues. These results suggest that elevated GADD45B promotes the motility of ovarian cancer cells through EMT and is associated with EOC metastasis.

Conclusion

GADD45B can promote the motility of ovarian cancer cells through EMT, is associated with EOC metastasis, and may be a new biomarker of metastasis and prognosis.

Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments

Lung cancer is one of the most common types of cancer worldwide. Understanding the molecular mechanisms underlying the development and progression of lung cancer may improve early diagnosis, treatment and prognosis. The aim of the present study was to examine the pathogenesis of lung cancer and to identify potentially novel biomarkers. Gene expression datasets of patients with lung cancer were obtained from the Gene Expression Omnibus. Genes which were most closely associated with lung cancer (core genes) were screened by weighted gene co‑expression network analysis. In vitro cell based experiments were further utilized to verify the effects of the core genes on the proliferation of lung cancer cells, adhesion between cells and the matrix, and the associated metabolic pathways. Based on WGCNA screening, two gene modules and five core genes closely associated with lung cancer, including immunoglobulin superfamily member 10 (IGSF10) from the turquoise module, and ribonucleotide reductase regulatory subunit M2, protein regulator of cytokinesis 1, kinesin family member (KIF)14 and KIF2C from the brown module were identified as relevant. Survival analysis and differential gene expression analysis showed that there were significant differences in IGSF10 expression levels between the healthy controls and patients with lung cancer. In patients with lung cancer, IGSF10 expression was decreased, and the overall survival time of patients with lung cancer was significantly shortened. An MTT and colony formation assay showed that IGSF10‑knockout significantly increased proliferation of lung cancer cells, and Transwell assays and adhesion experiments further suggested that the adhesion between cells and the matrix was significantly increased in IGSF10‑knockout cells. Gene Set Enrichment Analysis showed that the expression level of IGSF10 was significantly associated with the activation of the integrin‑β1/focal adhesion kinase (FAK) pathway. Western blotting revealed that knockout of IGSF10 resulted in the activation of the integrin‑β1/FAK pathway, as the protein expression levels of integrin‑β1, phosphorylated (p)‑FAK and p‑AKT were significantly upregulated. Activation of the integrin‑β1/FAK pathway, following knockout of IGSF10, affected the proliferation and adhesion of lung cancer cells. Therefore, IGSF10 my serve as a potential prognostic marker of lung cancer.

DMCM: a Data-adaptive Mutation Clustering Method to identify cancer-related mutation clusters

Motivation

Functional somatic mutations within coding amino acid sequences confer growth advantage in pathogenic process. Most existing methods for identifying cancer-related mutations focus on the single amino acid or the entire gene level. However, gain-of-function mutations often cluster in specific protein regions instead of existing independently in the amino acid sequences. Some approaches for identifying mutation clusters with mutation density on amino acid chain have been proposed recently. But their performance in identification of mutation clusters remains to be improved.

Results

Here we present a Data-adaptive Mutation Clustering Method (DMCM), in which kernel density estimate (KDE) with a data-adaptive bandwidth is applied to estimate the mutation density, to find variable clusters with different lengths on amino acid sequences. We apply this approach in the mutation data of 571 genes in over twenty cancer types from The Cancer Genome Atlas (TCGA). We compare the DMCM with M2C, OncodriveCLUST and Pfam Domain and find that DMCM tends to identify more significant clusters. The cross-validation analysis shows DMCM is robust and cluster cancer type enrichment analysis shows that specific cancer types are enriched for specific mutation clusters.

Availability and implementation

DMCM is written in Python and analysis methods of DMCM are written in R. They are all released online, available through https://github.com/XinguoLu/DMCM.

Supplementary information

Supplementary data are available at Bioinformatics online.

TNF-α Differentially Regulates Cell Cycle Genes in Promyelocytic and Granulocytic HL-60/S4 Cells

Tumor necrosis factor alpha (TNF-α) is a potent cytokine involved in systemic inflammation and immune modulation. Signaling responses that involve TNF-α are context dependent and capable of stimulating pathways promoting both cell death and survival. TNF-α treatment has been investigated as part of a combined therapy for acute myeloid leukemia due to its modifying effects on all-trans retinoic acid (ATRA) mediated differentiation into granulocytes. To investigate the interaction between cellular differentiation and TNF-α, we performed RNA-sequencing on two forms of the human HL-60/S4 promyelocytic leukemia cell line treated with TNF-α. The ATRA-differentiated granulocytic form of HL-60/S4 cells had an enhanced transcriptional response to TNF-α treatment compared to the undifferentiated promyelocytes. The observed TNF-α responses included differential expression of cell cycle gene sets, which were generally upregulated in TNF-α treated promyelocytes, and downregulated in TNF-α treated granulocytes. This is consistent with TNF-α induced cell cycle repression in granulocytes and cell cycle progression in promyelocytes. Moreover, we found evidence that TNF-α treatment of granulocytes shifts the transcriptome toward that of a macrophage. We conclude that TNF-α treatment promotes a divergent transcriptional program in promyelocytes and granulocytes. TNF-α promotes cell cycle associated gene expression in promyelocytes. In contrast, TNF-α stimulated granulocytes have reduced cell cycle gene expression, and a macrophage-like transcriptional program.

Molecular profiling of mucinous epithelial ovarian cancer by weighted gene co-expression network analysis

PURPOSE

In order to identify the molecular characteristics and improve the efficacy of early diagnosis of mucinous epithelial ovarian cancer (mEOC), here, the transcriptome profiling by weighted gene co-expression network analysis (WGCNA) has been proposed as an effective method.

METHODS

The gene expression dataset GSE26193 was reanalyzed with a systematical approach, WGCNA. mEOC-related gene co-expression modules were detected and the functional enrichments of these modules were performed at GO and KEGG terms. Ten hub genes in the mEOC-related modules were validated using two independent datasets GSE44104 and GSE30274.

RESULTS

11 co-expressed gene modules were identified by WGCNA based on 4917 genes and 99 epithelial ovarian cancer samples. The turquoise module was found to be significantly associated with the subtype of mEOC. KEGG pathway enrichment analysis showed genes in the turquoise module significantly enriched in metabolism of xenobiotics by cytochrome P450 and steroid hormone biosynthesis. Ten hub genes (LIPH, BCAS1, FUT3, ZG16B, PTPRH, SLC4A4, MUC13, TFF1, HNF4G and TFF2) in the turquoise module were validated to be highly expressed in mEOC using two independent gene expression datasets GSE44104 and GSE30274.

CONCLUSION

Our work proposed an applicable framework of molecular characteristics for patients with mEOC, which may help us to obtain a precise and comprehensive understanding on the molecular complexities of mEOC. The hub genes identified in our study, as potential specific biomarkers of mEOC, may be applied in the early diagnosis of mEOC in the future.

Integrated Analysis of Transcriptome and Prognosis Data Identifies FGF22 as a Prognostic Marker of Lung Adenocarcinoma

Lung adenocarcinoma is one of the most common cancers worldwide. However, the molecular mechanisms of lung adenocarcinoma development are still unclear. This study aimed to investigate the expression profiles of anti-lung cancer target genes in different cancer stages and to explore their functions in tumor development. Lung adenocarcinoma transcriptome and clinical data were downloaded from Genomic Data Commons Data Portal, and the anti-lung cancer target genes were retrieved from the Thomson Reuters Integrity database. The results showed that 16 anti-lung target genes were deregulated in all stages. Among these target genes, fibroblast growth factor 22 showed the most important role in transcription regulatory networks. Further analysis revealed that APC, BRIP1, and PTTG1 may regulate fibroblast growth factor 22 and subsequently influence MAPK signaling pathway, Rap1 signaling pathways, and other tumorigenic processes in all stages. Moreover, high fibroblast growth factor 22 expression leads to poor overall survival (hazard ratio = 1.55, P = .019). These findings provide valuable information for the pathological research and treatment of lung adenocarcinoma. Future studies are needed to verify these results.

Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm

Background

Gene signatures are important to represent the molecular changes in the disease genomes or the cells in specific conditions, and have been often used to separate samples into different groups for better research or clinical treatment. While many methods and applications have been available in literature, there still lack powerful ones that can take account of the complex data and detect the most informative signatures.

Methods

In this article, we present a new framework for identifying gene signatures using Pareto-optimal cluster size identification for RNA-seq data. We first performed pre-filtering steps and normalization, then utilized the empirical Bayes test in Limma package to identify the differentially expressed genes (DEGs). Next, we used a multi-objective optimization technique, “Multi-objective optimization for collecting cluster alternatives” (MOCCA in R package) on these DEGs to find Pareto-optimal cluster size, and then applied k-means clustering to the RNA-seq data based on the optimal cluster size. The best cluster was obtained through computing the average Spearman’s Correlation Score among all the genes in pair-wise manner belonging to the module. The best cluster is treated as the signature for the respective disease or cellular condition.

Results

We applied our framework to a cervical cancer RNA-seq dataset, which included 253 squamous cell carcinoma (SCC) samples and 22 adenocarcinoma (ADENO) samples. We identified a total of 582 DEGs by Limma analysis of SCC versus ADENO samples. Among them, 260 are up-regulated genes and 322 are down-regulated genes. Using MOCCA, we obtained seven Pareto-optimal clusters. The best cluster has a total of 35 DEGs consisting of all-upregulated genes. For validation, we ran PAMR (prediction analysis for microarrays) classifier on the selected best cluster, and assessed the classification performance. Our evaluation, measured by sensitivity, specificity, precision, and accuracy, showed high confidence.

Conclusions

Our framework identified a multi-objective based cluster that is treated as a signature that can classify the disease and control group of samples with higher classification performance (accuracy 0.935) for the corresponding disease. Our method is useful to find signature for any RNA-seq or microarray data.

Next-generation sequencing unravels extensive genetic alteration in recurrent ovarian cancer and unique genetic changes in drug-resistant recurrent ovarian cancer

BACKGROUND

By using a high-throughput sequencing technique, we sought to delineate genetic alterations in recurrent ovarian cancer patients and further compare genetic changes in drug-resistant and -sensitive recurrent ovarian cancer patients. We also sought to study the specificity, sensitivity, and consistency of DNA biomarkers in liquid biopsy specimens and ovarian cancer tissue DNA.

METHODS

Tumor tissue specimens and blood samples were obtained from pathologically proven recurrent ovarian cancer patients. Genomic DNA was extracted from tumor tissues, blood cells, ascites, and urine samples. The DNA Library was constructed and sequencing was performed using the Illumina HiSeq 4000 high-throughput sequencing platform. Bioinformatic analysis was done using the Torrent Suite software.

RESULTS

Ten patients with pathologically proven drug-resistant recurrent ovarian cancer and 11 patients with sensitive recurrent ovarian cancer were included. The 5-year OS for drug-resistant recurrent ovarian cancer patients (44 ± 11.07 months, 95% CI: 231.24-53.66 months) was significantly lower than that of drug-sensitive recurrent ovarian cancer patients (58 ± 3.97 months; 95% CI: 50.05-65.59 months; p = 0.024) TP53 was the most frequently mutated gene in both drug-resistant (9/10, 90%) and drug-sensitive recurrent ovarian cancers (10/11, 91%). MYC and RB1 had the highest frequency of copy number variations (6/21, 29%) in recurrent ovarian cancers, followed by PIK3CA (3/21, 14%). BRCA2 N372H polymorphism was found in 40% (4/10) of drug-resistant recurrent ovarian cancer patients. The specificity, sensitivity, and consistency of TP53 and BRCA1 in circulating tumor-free DNA and tumor tissue DNA were 100%, 73.7%, 76.2% and 100%, 75%, 95.24%, respectively.

CONCLUSION

We uncovered extensive genetic alterations in recurrent ovarian cancer and drug-resistant recurrent ovarian cancer exhibited unique genetic changes compared with recurrent ovarian cancer and drug-sensitive recurrent ovarian cancer. We further showed that high-throughput sequencing using liquid biopsy specimens could provide an effective, specific, and sensitive approach for detecting genetic alterations in ovarian cancer.

The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes

With advances in next-generation sequencing(NGS) technologies, a large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer progression is to identify the driver genes from the variant genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profiles and copy number variation (CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.