Independent Gene Expression Research Articles

Integrative analysis of prognostic long non-coding RNAs with copy number variation in bladder cancer.

Copy number variations (CNVs), which can affect the role of long non-coding RNAs (lncRNAs), are important genetic changes seen in some malignant tumors. We analyzed lncRNAs with CNV to explore the relationship between lncRNAs and prognosis in bladder cancer (BLCA). Messenger RNA (mRNA) expression levels, DNA methylation, and DNA copy number data of 408 BLCA patients were subjected to integrative bioinformatics analysis. Cluster analysis was performed to obtain different subtypes and differently expressed lncRNAs and coding genes. Weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression gene and lncRNA modules. CNV-associated lncRNA data and their influence on cancer prognosis were assessed with Kaplan-Meier survival curve. Multi-omics integration analysis revealed five prognostic lncRNAs with CNV, namely NR2F1-AS1, LINC01138, THUMPD3-AS1, LOC101928489,and TMEM147-AS1,and a risk-score signature related to overall survival in BLCA was identified. Moreover, validated results in another independent Gene Expression Omnibus (GEO) dataset, GSE31684, were consistent with these results. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the mitogen-activated protein kinase (MAPK) signaling pathway, focal adhesion pathway, and Janus kinase-signal transducers and activators of transcription (JAK-STAT) signaling pathway were enriched in a high-risk score pattern, suggesting that imbalance in these pathways is closely related to tumor development. We revealed the prognosis-related lncRNAs by analyzing the expression profiles of lncRNAs and CNVs, which can be used as prognostic biomarkers for BLCA.

CircUBAP2 Promotes MMP9-Mediated Oncogenic Effect via Sponging miR-194-3p in Hepatocellular Carcinoma.

BackgroundThe physiological regulatory functions of circRNAs have become a topic of intensive research in recent years. Increasing evidence supports a significant role of circRNAs during cancer initiation and progression, including hepatocellular carcinoma (HCC).Materials and MethodsA bioinformatics analysis from three independent Gene Expression Omnibus (GEO) databases was performed to profile and screen the dysregulated circRNAs in HCC. RT-qPCR was used to examine the expression level of circUBAP2 in HCC and adjacent non-tumor tissues. Then, proliferation assays (CCK8 and colony formation) and migration assays (transwell and wound healing) were performed to examine effect of circUBAP2 in vitro. Immunoprecipitation, RNA pulldown, FISH, and dual-luciferase reporter assay was conducted to explore the circUBAP2-related mechanism for regulating HCC progression. Moreover, a mouse xenograft model and a mouse lung metastasis model confirmed the effect of circUBAP2 in vivo.ResultsIn this study, we found a novel circRNA: circUBAP2, which was identified by bioinformatics analysis. Among 91 HCC patients, circUBAP2 was significantly upregulated in HCC tissues, and negatively correlated with aggressive clinical characteristics and prognosis. Functional assays demonstrated that circUBAP2 promoted cell proliferation, colony formation, migration, and invasion in vitro. Moreover, circUBAP2 enhanced tumor growth and pulmonary metastasis in vivo. Mechanistically, circUBAP2 acts as a competing endogenous RNA (ceRNA) for miR-194-3p, a tumor suppressor in HCC. We confirmed that MMP9 was direct target for miR-194-3p, which was regulated by circUBAP2.ConclusionCircUBAP2 plays a significant role in promoting HCC via the miR-194-3p/MMP9 pathway and could serve as a promising prognostic biomarker and novel therapeutic target for HCC patients.

Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods.

BackgroundThough considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.MethodsOverlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.ResultsA total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.ConclusionTwo hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.

Comprehensive molecular and clinical characterization of SLC1A5 in human cancers

Although SLC1A5 has been reported to be closely associated with some cancer types, a comprehensive and systematic assessment of SLC1A5 across human cancers is lacking. Thus, Pan-cancer analysis of SLC1A5 was performed across 30 types of human cancers in this study. We examined mRNA expression, protein expression, copy number variation (CNV), DNA methylation, clinical relevance, cell functions, drug response and total immune infiltrates of SLC1A5 in more than 9000 patients across 30 human cancer types from The Cancer Genome Atlas (TCGA) dataset. Additionally, nine independent Gene Expression Omnibus datasets, more than 800 cancer cell lines from the Cancer Cell Line Encyclopedia dataset and the Project Achilles dataset were used to validate our findings in the TCGA dataset. Landscapes of SLC1A5 were established across multiple cancers. We showed that SLC1A5 is upregulated in multiple cancers, particularly in digestive and respiratory system cancers. SLC1A5 upregulation may be driven by CNV gain and DNA hypomethylation in human cancers. Furthermore, SLC1A5 overexpression is associated with tumor progression and poor survival in multiple cancers. Moreover, we systematically explored the potential effects of SLC1A5 expression on cell functions and drug response in human cancers. SLC1A5 knockdown showed significant proliferation-inhibiting effects in most human cancer types, especially in the digestive system and KRAS-mutant cancers. SLC1A5 expression is associated with proliferation activities of KRAS-mutant cancer cell lines and drug response of many anti-cancer drugs. Finally, we demonstrated that SLC1A5-realted tumor immune microenvironment characteristics showed strong heterogeneity in human cancers. Taken together, our findings highlight the important roles of SLC1A5 in tumorigenesis, progression, prognosis and therapy.

Differential Expression and Copy Number Variation of Gasdermin (GSDM) Family Members, Pore-Forming Proteins in Pyroptosis, in Normal and Malignant Serous Ovarian Tissue.

Gasdermins (GSDM) are members of a family of pore-forming effector proteins which lead to membrane permeabilization and pyroptosis, a lytic cell death with pro-inflammatory characteristics. Recently, two members of the gasdermin family, gasdermin B (GSDMB) and gasdermin E (GSDME), were shown to suppress tumor growth, through the involvement of cytotoxic lymphocytes. Other studies also reported the important functions of gasdermins in various cancer types including gastric cancer, hepatocarcinoma, and cervix and breast cancer. However, gasdermins have not been previously studied in the context of serous ovarian cancer. Here, we showed that gasdermin D (GSDMD) and gasdermin C (GSDMC) expression increases in serous ovarian cancer; in contrast, the expression of GSDME and PJVK (Pejvakin, DFNB59) is downregulated, compared to healthy ovaries, in multiple independent gene expression datasets. We found that copy number gains are highly frequent (present in approximately 50% of patients) in genes encoding GSDMD and GSDMC in ovarian cancer, in line with their upregulated expression in serous ovarian cancer. Moreover, we observed that the expression of GSDMB and GSDMD, but not of GSDME, is different among several histotypes of epithelial ovarian cancer. Therefore, we propose that differential expression and copy number variations of certain gasdermins might be associated with the development of serous ovarian cancer, in which different members of the family have distinct functions; however, further research is required in in vivo models to understand how changes in gasdermin family members mechanistically contribute to serous ovarian cancer.

TP53 Mutational Status-Based Genomic Signature for Prognosis and Predicting Therapeutic Response in Pancreatic Cancer.

TP53 mutation is a critical driver mutation that affects the carcinogenesis and prognosis of patients with pancreatic cancer (PC). Currently, there is no driver mutation-derived signature based on TP53 mutational status for prognosis and predicting therapeutic response in PC. In the present study, we characterized the TP53 mutational phenotypes in multiple patient cohorts and developed a prognostic TP53-associated signature based on differentially expressed genes between PC samples with mutated TP53 and wild-type TP53. Comprehensive investigations were carried out in prognostic stratification, genetic variation, immune cell infiltration, and efficacy prediction of chemotherapy and targeted therapy. We found that TP53 mutation commonly occurred as a survival-related driver mutation in PC. In total, 1,154 differentially expressed genes were found between two distinct TP53 mutational phenotypes. A five-gene TP53-associated signature was constructed in The Cancer Genome Atlas (TCGA) cohort by least absolute shrinkage and selection operator (LASSO)–Cox analysis and proven to be a robust prognostic predictor, which performed well in three independent Gene Expression Omnibus (GEO) validating cohorts. Remarkably, patients in the low-risk group were characterized with decreased tumor mutation burden and activity of immunity, with favorable prognosis. Higher fractions of macrophages M0 and impaired CD8 + T cells were observed in patients in the high-risk group, suggesting immunosuppression with poor survival. Patients in the high-risk group also demonstrated enhanced response to specific chemotherapeutic agents, including gemcitabine and paclitaxel. Several targeted inhibitors, like histamine receptor inhibitor, were screened out as promising drugs for PC treatment. Collectively, the TP53-associated signature is a novel prognostic biomarker and predictive indicator of PC. The signature could contribute to optimizing prognostic stratification and guide effective PC treatments.

ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysis

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer with an abysmal prognosis rate over the last few decades. Early diagnosis and prevention could effectively combat this malignancy. Therefore, it is crucial to discover potential biomarkers to identify asymptomatic premalignant or early malignant tumors of PDAC. Gene expression analysis is a powerful technique to identify candidate biomarkers involved in disease progression. In the present study, five independent gene expression datasets, including 321 PDAC tissues and 208 adjacent non-cancerous tissue samples, were subjected to statistical and bioinformatics analysis. A total of 20 differentially expressed genes (DEGs) were identified in PDAC tissues compared to non-cancerous tissue samples. Gene ontology and pathway enrichment analysis showed that DEGs were mainly enriched in extracellular matrix (ECM), cell adhesion, ECM–receptor interaction, and focal adhesion signaling. The protein–protein interaction network was constructed, and the hub genes were evaluated. Collagen type XII alpha 1 chain (COL12A1), fibronectin 1 (FN1), integrin subunit alpha 2 (ITGA2), laminin subunit beta 3 (LAMB3), laminin subunit gamma 2 (LAMC2), thrombospondin 2 (THBS2), and versican (VCAN) were identified as hub genes. The correlation analysis revealed that identified hub genes were significantly interconnected. Wherein COL12A1, FN1, ITGA2, LAMB3, LAMC2, and THBS2 were significantly associated with PDAC pathological stages. The Kaplan–Meier survival plots revealed that ITGA2, LAMB3, and LAMC2 expression were inversely correlated with a prolonged patient survival period. Furthermore, the Human Protein Atlas database was used to validate the expression and cellular origins of hub genes encoded proteins. The protein expression of hub genes was higher in pancreatic cancer tissue than in normal pancreatic tissue samples, wherein ITGA2, LAMB3, and LAMC2 were exclusively expressed in pancreatic cancer cells. Pancreatic cancer cell-specific expression of these three proteins may play pleiotropic roles in cancer progression. Our results collectively suggest that ITGA2, LAMB3, and LAMC2 could provide deep insights into pancreatic carcinogenesis molecular mechanisms and provide attractive therapeutic targets.

Identification of a novel glycolysis-related gene signature for predicting the prognosis of osteosarcoma patients.

Glycolysis ensures energy supply to cancer cells, thereby facilitating tumor progression. Here, we identified glycolysis-related genes that could predict the prognosis of patients with osteosarcoma. We examined 198 glycolysis-related genes that showed differential expression in metastatic and non-metastatic osteosarcoma samples in the TARGET database, and identified three genes (P4HA1, ABCB6, and STC2) for the establishment of a risk signature. Based on the signature, patients in the high-risk group had poor outcomes. An independent Gene Expression Omnibus database GSE21257 was selected as the validation cohort. Receiver operating characteristic curve analysis was performed and the accuracy of predicting the 1- and 3-year survival rates was shown by the areas under the curve. The results were 0.884 and 0.790 in the TARGET database, and 0.740 and 0.759 in the GSE21257, respectively. Furthermore, we applied ESTIMATE algorithm and performed single sample gene set enrichment analysis to compare tumor immunity between high- and low-risk groups. We found that the low-risk group had higher immune scores and immune infiltration levels than the high-risk group. Finally, we chose P4HA1 as a representative gene to verify the function of risk genes in vitro and in vivo and found that P4HA1 could promote the metastasis of osteosarcoma cells. Our study established a novel glycolysis-related risk signature that could predict the prognosis of patients with osteosarcoma.

527 Expression of OPN3 correlating to tumor initiation and development in cutaneous melanoma

The function of OPN3 in melanoma remains largely unknown. To assess the biological role of OPN3 in the initiation and development of cutaneous melanoma, we first systematically investigated the expression patterns of OPN3 gene from human melanoma based on the available three independent gene expression datasets (GEO GSE3189, GSE8401 and GSE4587). We found that the OPN3 mRNA was upregulated in human melanoma when compared to normal skin and nevus tissue samples, especially in vertical growth phase melanoma. Additionally, no statistically significant difference was evident between the primary melanoma and metastasis of melanoma tissues. By the weighted correlation network and protein-protein association networks analysis, it suggested that OPN3 might play a role in the initiation and development of cutaneous melanoma. Next, to further illustrate the possible mechanisms by which OPN3 mediates melanoma cell tumorigenicity, RNA-seq analysis was performed on low expression of OPN3 and untreated M14 human skin melanoma cells. In this present study, our heatmap revealed that 5029 differentially expressed genes (DEGs) were upregulated, while 4495 DEGs were downregulated. Notably, in GO (Gene Ontology) term enrichment analysis our results indicated that the DEGs were evidently enriched in post-transcriptional gene regulation and cell cycle regulation, suggesting that OPN3 was mainly involved in proliferation and cell cycle regulation. The KEGG pathway analysis, OPN3 was dramatically enriched in cell cycle regulation process, neurotrophin signaling pathway and mRNA surveillance pathway. Taken together, we here showed that high expression of OPN3 can promote melanoma initiation and development by regulating cell cycle progression and tumor-associated signaling pathway.

Nuclear factor IB is downregulated in vulvar squamous cell carcinoma (VSCC): Unravelling differentially expressed genes in VSCC through gene expression dataset analysis.

Vulvar squamous cell carcinoma (VSCC) comprises two distinct etiopathological subtypes: i) Human papilloma virus (HPV)-related VSCC, which arises via the precursor high grade squamous intraepithelial lesion (HSIL); and ii) HPV-independent VSCC, which arises via precursor, differentiated vulvar intraepithelial neoplasia (dVIN), driven by TP53 mutations. However, the mechanism of carcinogenesis of VSCC is poorly understood. The current study aimed to gain insight into VSCC carcinogenesis by identifying differentially expressed genes (DEGs) for each VSCC subtype. The expression of certain DEGs was then further assessed by performing immunohistochemistry (IHC) on whole tissue sections of VSCC and its precursors. Statistical analysis of microarrays was performed on two independent gene expression datasets (GSE38228 and a study from Erasmus MC) on VSCC and normal vulva. DEGs were identified that were similarly (up/down) regulated with statistical significance in both datasets. For HPV-related VSCCs, this constituted 88 DEGs, and for HPV-independent VSCCs, this comprised 46 DEGs. IHC was performed on VSCC (n=11), dVIN (n=6), HSIL (n=6) and normal vulvar tissue (n=7) with i) signal transducer and activator of transcription 1 (STAT1; an upregulated DEGs); ii) nuclear factor IB (NFIB; a downregulated DEG); iii) p16 (to determine the HPV status of tissues); and iv) p53 (to confirm the histological diagnoses). Strong and diffuse NFIB expression was observed in the basal and para-basal layers of normal vulvar tissue, whereas NFIB expression was minimal or completely negative in dVIN and in both subtypes of VSCC. In contrast, no discernable difference was observed in STAT1 expression among normal vulvar tissue, dVIN, HSIL or VSCC. By leveraging bioinformatics, the current study identified DEGs that can facilitate research into VSCC carcinogenesis. The results suggested that NFIB is downregulated in VSCC and its relevance as a diagnostic/prognostic biomarker deserves further exploration.

Increased expression of IFI16 predicts adverse prognosis in multiple myeloma.

Multiple myeloma (MM) is a malignancy of terminally differentiated plasma cells and does not have sufficient prognostic indicators. Interferon gamma inducible protein 16 (IFI16) plays a crucial role in B-cell differentiation. Several studies have shown that IFI16 predicted prognosis in many cancers. However, the relationship between MM prognosis and IFI16 expression has not been studied. In our study, we analyzed the prognostic role of IFI16 expression and explored the possible mechanism in MM progression by using 4498 myeloma patients and 52 healthy donors from 13 independent gene expression omnibus (GEO) datasets. The IFI16 expression increased with myeloma progression, ISS stage, 1q21 amplification, and relapse (all P < 0.01). MM patients with higher IFI16 expression had shorter survival in six datasets (all P < 0.05). Furthermore, multivariate analysis indicated that enhanced IFI16 expression was an independent poor prognostic factor for EFS and OS (P = 0.007, 0.009, respectively). And PPI, GO, KEGG, and GSEA also confirmed that IFI16 promoted MM progression by participating in tumor-related pathways. In conclusion, our study confirmed that IFI16 was a poor prognostic biomarker in MM.

Identifying Novel Cell Glycolysis-Related Gene Signature Predictive of Overall Survival in Gastric Cancer.

Background Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. Materials and Methods In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups. Results We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients' OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients. Conclusion The signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.

Abstract PD15-09: Sox4 and smarca4 upregulate glycolysis-driven tumor proliferation through hexokinase 2 in tnbc

Abstract Background Triple-negative breast cancer (TNBC) accounts for nearly 1-in-4 breast cancer related deaths in the United States each year despite representing 10-12% of newly diagnosed cases. This aggressive disease, which is largely synonymous with the basal-like molecular subtype, predominantly affects younger women and women of African American decent. Despite a number of recent advances, overall survival has not drastically improved, highlighting the need to better understand mechanisms regulating tumor growth and progression as a means to identify novel therapeutic opportunities in these patients. Methods In order to identify the functional impact of overexpression of the oncogenic transcription factor SOX4 and its co-factor SMARCA4 in TNBC, RNAseq and ChIPseq analyses were used to identify SMARCA4-dependent and -independent SOX4 gene expression programs. Analyses of more than 3,000 tumor samples from the METABRIC and TCGA cohorts was used to investigate the functional implications and clinical association of these gene expression programs in human tumors. Mass spectrometry (MS)-based metabolomic profiling in conjunction with genetic and pharmacological-based in vitro studies were used to demonstrate the impact of these programs on tumor cell metabolism, cell viability and proliferation. Results We, and others, have previously demonstrated that the oncogenic transcription factor SOX4 is overexpressed in basal-like tumors. In order to identify mechanisms by which this protein mediates breast cancer tumorigenesis, we first identified SOX4 co-factors. Immunoprecipitation follow by MS was used to identify the SWI/SNF ATPase SMARCA4 as a prominent SOX4 cofactor that is concurrently overexpressed in basal-like tumors. Mechanistic studies showed that this complex is required to maintain an active open chromatin conformation at regulatory regions upstream of SOX4-regulated genes. ChIPseq and RNAseq-based analyses were next used to investigate the role of this complex in TNBC signaling and tumorigenesis. Our analyses identified SMARCA4-dependent and -independent SOX4 gene expression programs and demonstrated that SMARCA4-dependent SOX4 signaling is associated with poor clinical outcome. Further in silico analyses of human tumors indicate that each program mediates distinct down-stream signaling; SMARCA4-dependent signaling is associated with altered tumor metabolism and increased proliferative signaling. Consistent with this premise, in vitro studies showed that SOX4 and SMARCA4 are essential for breast tumor cell proliferation, survival and colony formation. Metabolomic profiling of breast cancer cell lines demonstrated that SOX4 overexpression resulted in increased glycolysis. The effect of SOX4 and SMARCA4 on cell metabolism was validated by demonstrating that this complex can mediate intracellular glucose consumption. Finally, we determined that SOX4 and SMARCA4 cooperatively regulate mRNA expression of hexokinase 2 (HK2), which catalyzes for the first step in glucose metabolism, and demonstrated that HK2 activity is essential for SOX4/SMARCA4-regulation of glycolysis. Importantly, in vitro genetic and pharmacological studies identified a link between HK2 activity, glycolysis and SOX4/SMARCA4-mediated cell proliferation and viability in TNBC. Conclusions Our studies have identified SOX4 and its transcriptional cofactor SMARCA4 as being uniformly overexpressed in basal-like or TNBC tumors. These analyses demonstrate that SOX4 and SMARCA4 cooperatively promote tumorigenesis in TNBC cells, in part, by modulating glycolysis and cell proliferation through transcriptional regulation of HK2. Given the drug-ability of the glycolysis pathway, these results have potential clinical implications for treatment of TNBC patients. Citation Format: Pooja Khanna, Gaurav Mehta, Michael L Gatza. Sox4 and smarca4 upregulate glycolysis-driven tumor proliferation through hexokinase 2 in tnbc [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD15-09.

ComHub: Community predictions of hubs in gene regulatory networks

BackgroundHub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.ResultsWe have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets.ConclusionsIn contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.

A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets.

BackgroundGastric cancer (GC) is one of the most common carcinomas of the digestive tract, and the prognosis for these patients may be poor. There is evidence that some long non-coding RNAs(lncRNAs) can predict the prognosis of patients with GC. However, few lncRNA signatures have been used to predict prognosis. Herein, we aimed to construct a risk score model based on the expression of five lncRNAs to predict the prognosis of patients with GC and provide new potential therapeutic targets.MethodsWe performed differentially expressed and survival analyses to identify differentially expressed survival-ralated lncRNAs by using GC patient expression profile data from The Cancer Genome Atlas (TCGA) database. We then established a formula including five lncRNAs to predict the prognosis of patients with GC. In addition, to verify the prognostic value of this risk score model, two independent Gene Expression Omnibus (GEO) datasets, GSE62254 (N = 300) and GSE15459 (N = 200), were employed as validation groups.ResultsBased on the characteristics of five lncRNAs, patients with GC were divided into high or low risk subgroups. The prognostic value of the risk score model with five lncRNAs was confirmed in both TCGA and the two independent GEO datasets. Furthermore, stratification analysis results showed that this model had an independent prognostic value in patients with stage II–IV GC. We constructed a nomogram model combining clinical factors and the five lncRNAs to increase the accuracy of prognostic prediction. Enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the five lncRNAs are associated with multiple cancer occurrence and progression-related pathways.ConclusionThe risk score model including five lncRNAs can predict the prognosis of patients with GC, especially those with stage II-IV, and may provide potential therapeutic targets in future.

A Cluster-Based Approach for Identifying Prognostic microRNA Signatures in Digestive System Cancers.

Cancer remains the second leading cause of death all over the world. Aberrant expression of miRNA has shown diagnostic and prognostic value in many kinds of cancer. This study aims to provide a novel strategy to identify reliable miRNA signatures and develop improved cancer prognostic models from reported cancer-associated miRNAs. We proposed a new cluster-based approach to identify distinct cluster(s) of cancers and corresponding miRNAs. Further, with samples from TCGA and other independent studies, we identified prognostic markers and validated their prognostic value in prediction models. We also performed KEGG pathway analysis to investigate the functions of miRNAs associated with the cancer cluster of interest. A distinct cluster with 28 cancers and 146 associated miRNAs was identified. This cluster was enriched by digestive system cancers. Further, we screened out 8 prognostic miRNA signatures for STAD, 5 for READ, 18 for PAAD, 24 for LIHC, 12 for ESCA and 18 for COAD. These identified miRNA signatures demonstrated strong abilities in discriminating the overall survival time between high-risk group and low-risk group (p-value < 0.05) in both TCGA training and test datasets, as well as four independent Gene Expression Omnibus (GEO) validation datasets. We also demonstrated that these cluster-based miRNA signatures are superior to signatures identified in single cancers for prognosis. Our study identified significant miRNA signatures with improved prognosis accuracy in digestive system cancers. It also provides a novel method/strategy for cancer prognostic marker selection and offers valuable methodological directions to similar research topics.

Identification of Serum Circulating MicroRNAs as Novel Diagnostic Biomarkers of Gastric Cancer.

Gastric cancer (GC) is one of the leading causes of cancer-associated deaths worldwide. Due to the lack of typical symptoms and effective biomarkers for non-invasive screening, most patients develop advanced-stage GC by the time of diagnosis. Circulating microRNA (miRNA)-based panels have been reported as a promising tool for the screening of certain types of cancers. In this study, we performed differential expression analysis of miRNA profiles of plasma samples obtained from gastric cancer and non-cancer patients using two independent Gene Expression Omnibus (GEO) datasets: GSE113486 and GSE124158. We identified three miRNAs, hsa-miR-320a, hsa-miR-1260b, and hsa-miR-6515-5p, to distinguish gastric cancer cases from non-cancer controls. The three miRNAs showed an area under the curve (AUC) over 0.95 with high specificity (>93.0%) and sensitivity (>85.0%) in both the discovery datasets. In addition, we further validated these three miRNAs in two external datasets: GSE106817 [sensitivity: hsa-miR-320a (99.1%), hsa-miR-1260b (97.4%), and hsa-miR-6515-5p (92.2%); specificity: hsa-miR-320a (88.8%), hsa-miR-1260b (89.6%), and hsa-miR-6515-5p (88.7%); and AUC: hsa-miR-320a (96.3%), hsa-miR-1260b (97.4%), and hsa-miR-6515-5p (94.6%)] and GSE112264 [sensitivity: hsa-miR-320a (100.0%), hsa-miR-1260b (98.0%), and hsa-miR-6515.5p (98.0%); specificity: hsa-miR-320a (100.0%), hsa-miR-1260b (100.0%), and hsa-miR-6515.5p (92.7%); and AUC: hsa-miR-320a (1.000), hsa-miR-1260b (1.000), and hsa-miR-6515-5p (0.988)]. On the basis of these findings, the three miRNAs can be used as potential biomarkers for gastric cancer screening, which can provide patients with a higher chance of curative resection and longer survival.

Breast Cancer Type Classification Using Machine Learning.

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.

The Genomic Landscape of Actinic Keratosis

Actinic keratoses (AKs) are lesions of epidermal keratinocyte dysplasia and are precursors for invasive cutaneous squamous cell carcinoma (cSCC). Identifying the specific genomic alterations driving the progression from normal skin to skin with AK to skin with invasive cSCC is challenging because of the massive UVR-induced mutational burden characteristic at all stages of this progression. In this study, we report the largest AK whole-exome sequencing study to date and perform a mutational signature and candidate driver gene analysis on these lesions. We demonstrate in 37 AKs from both immunosuppressed and immunocompetent patients that there are significant similarities between AKs and cSCC in terms of mutational burden, copy number alterations, mutational signatures, and patterns of driver gene mutations. We identify 44 significantly mutated AK driver genes and confirm that these genes are similarly altered in cSCC. We identify azathioprine mutational signature in all AKs from patients exposed to the drug, providing further evidence for its role in keratinocyte carcinogenesis. cSCCs differ from AKs in having higher levels of intrasample heterogeneity. Alterations in signaling pathways also differ, with immune-related signaling and TGFβ signaling significantly more mutated in cSCC. Integrating our findings with independent gene expression datasets confirms that dysregulated TGFβ signaling may represent an important event in AK‒cSCC progression.