Cancer Gene Networks Research Articles

Clinical and molecular response to alpha1-oleate treatment in patients with bladder cancer.

The tumoricidal complex alpha1-oleate targets bladder cancer cells, triggering rapid, apoptosis-like tumor cell death. Clinical effects of alpha1-oleate were recently observed in patients with non-muscle invasive bladder cancer (NMIBC), using a randomized, placebo-controlled study protocol. To investigate if there are dose-dependent effects of alpha1-oleate. Here, patients with NMIBC were treated by intravesical instillation of increasing concentrations of alpha1-oleate (1.7, 8.5, or 17 mM) and the treatment response was defined relative to a placebo group. Strong, dose-dependent anti-tumor effects were detected in alpha1-oleate treated patients for a combination of molecular and clinical indicators; a complete or partial response was detected in 88% of tumors treated with 8.5 mM compared to 47% of tumors treated with 1.7 mM of alpha1-oleate. Uptake of alpha1-oleate by the tumor triggered rapid shedding of tumor cells into the urine and cell death by an apoptosis-like mechanism. RNA sequencing of tissue biopsies confirmed the activation of apoptotic cell death and strong inhibition of cancer gene networks, including bladder cancer related genes. Drug-related side effects were not recorded, except for local irritation at the site of instillation. These dose-dependent anti-tumor effects of alpha1-oleate are promising and support the potential of alpha1-oleate treatment in patients with NMIBC.

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.

Immune Gene Networks from Lung Cancer Patients Treated with Immune Checkpoint Inhibitors.

The association between immune checkpoint inhibitors (ICIs) and immune gene networks in squamous lung cancer (LUSC) and lung adenocarcinoma (LUAD) was studied. Immune gene networks were constructed using RNA-seq data from the gene expression omnibus (GEO) database. Datasets with more than 10 samples of normal control and tumor tissues were selected; of these, GSE87340, GSE120622, and GSE111907 were suitable for analysis. Gene set enrichment for pathway analysis was performed. For immune gene network construction, 998 unique immune genes were selected from 21 pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene function annotation was performed based on the KEGG, Gene Ontology, and Reactome databases. Tumor tissues showed decreased coagulation, hematopoiesis, and innate immune pathways, whereas complement- and coagulation-related genes were prominent in the tumor immune gene network. The average numbers of neighbors, clustering coefficients, network diameters, path lengths, densities, and heterogeneities were highest for normal tissue, followed by LUAD and LUSC. Decreased coagulation genes, which were prominent in tumor immune networks, imply functional attenuation. LUAD was deviated from normal tissue, based on network parameters. Tumor tissues showed decreased immune function, and the deviation of LUSC from normal tissue might explain LUSC's better therapeutic response to ICI treatment.

CanVaxKB: a web-based cancer vaccine knowledgebase.

Cancer vaccines have been increasingly studied and developedto prevent or treat various types of cancers. To systematically survey and analyze different reported cancer vaccines, we developed CanVaxKB (https://violinet.org/canvaxkb), the first web-based cancer vaccine knowledgebase that compiles over 670 therapeutic or preventive cancer vaccines that have been experimentally verified to be effective at various stages. Vaccine construction and host response data are also included. These cancer vaccines are developed against various cancer types such as melanoma, hematological cancer, and prostate cancer. CanVaxKB has stored 263 genes or proteins that serve as cancer vaccine antigen genes, which we have collectively termed 'canvaxgens'. Top three mostly used canvaxgens are PMEL, MLANA and CTAG1B, often targeting multiplecancer types. A total of 193 canvaxgens are also reported in cancer-related ONGene, Network of Cancer Genes and/or Sanger Cancer Gene Consensus databases. Enriched functional annotations and clusters of canvaxgens were identified and analyzed. User-friendly web interfaces are searchable for querying and comparing cancer vaccines. CanVaxKB cancer vaccines are also semantically represented bythe community-based Vaccine Ontology to support data exchange. Overall, CanVaxKB is a timely and vital cancer vaccine source that facilitates efficient collection and analysis, further helping researchers and physicians to better understand cancer mechanisms.

Comprehensive information-based differential gene regulatory networks analysis (CIdrgn): Application to gastric cancer and chemotherapy-responsive gene network identification.

Biological condition-responsive gene network analysis has attracted considerable research attention because of its ability to identify pathways or gene modules involved in the underlying mechanisms of diseases. Although many condition-specific gene network identification methods have been developed, they are based on partial or incomplete gene regulatory network information, with most studies only considering the differential expression levels or correlations among genes. However, a single gene-based analysis cannot effectively identify the molecular interactions involved in the mechanisms underlying diseases, which reflect perturbations in specific molecular network functions rather than disorders of a single gene. To comprehensively identify differentially regulated gene networks, we propose a novel computational strategy called comprehensive analysis of differential gene regulatory networks (CIdrgn). Our strategy incorporates comprehensive information on the networks between genes, including the expression levels, edge structures and regulatory effects, to measure the dissimilarity among networks. We extended the proposed CIdrgn to cell line characteristic-specific gene network analysis. Monte Carlo simulations showed the effectiveness of CIdrgn for identifying differentially regulated gene networks with different network structures and scales. Moreover, condition-responsive network identification in cell line characteristic-specific gene network analyses was verified. We applied CIdrgn to identify gastric cancer and itsf chemotherapy (capecitabine and oxaliplatin) -responsive network based on the Cancer Dependency Map. The CXC family of chemokines and cadherin gene family networks were identified as gastric cancer-specific gene regulatory networks, which was verified through a literature survey. The networks of the olfactory receptor family with the ASCL1/FOS family were identified as capecitabine- and oxaliplatin sensitive -specific gene networks. We expect that the proposed CIdrgn method will be a useful tool for identifying crucial molecular interactions involved in the specific biological conditions of cancer cell lines, such as the cancer stage or acquired anticancer drug resistance.

Role of non-coding RNAs in neuroblastoma.

Neuroblastoma is known as the most prevalent extracranial malignancy in childhood with a neural crest origin. It has been widely accepted that non-coding RNAs (ncRNAs) play important roles in many types of cancer, including glioma and gastrointestinal cancers. They may regulate the cancer gene network. According to recent sequencing and profiling studies, ncRNAs genes are deregulated in human cancers via deletion, amplification, abnormal epigenetic, or transcriptional regulation. Disturbances in the expression of ncRNAs may act either as oncogenes or as anti-tumor suppressor genes, and can lead to the induction of cancer hallmarks. ncRNAs can be secreted from tumor cells inside exosomes, where they can be transferred to other cells to affect their function. However, these topics still need more study to clarify their exact roles, so the present review addresses different roles and functions of ncRNAs in neuroblastoma.

Bioinformatic Analysis of miR-200b/429 and Hub Gene Network in Cervical Cancer

The miR-200b/429 located at 1p36 is a highly conserved miRNA cluster emerging as a critical regulator of cervical cancer. Using publicly available miRNA expression data from TCGA and GEO followed by independent validation, we aimed to identify the association between miR-200b/429 expression and cervical cancer. miR-200b/429 cluster was significantly overexpressed in cancer samples compared to normal samples. miR-200b/429 expression did not correlate with patient survival; however, its overexpression correlated with histological type. Protein–protein interaction analysis of 90 target genes of miR-200b/429 identified EZH2, FLT1, IGF2, IRS1, JUN, KDR, SOX2, MYB, ZEB1, and TIMP2 as the top ten hub genes. PI3K–AKT and MAPK signaling pathways emerged as major target pathways of miR-200b/429 and their hub genes. Kaplan–Meier survival analysis showed the expression of seven miR-200b/429 target genes (EZH2, FLT1, IGF2, IRS1, JUN, SOX2, and TIMP2) to influence the overall survival of patients. The miR-200a-3p and miR-200b-5p could help predict cervical cancer with metastatic potential. The cancer hallmark enrichment analysis showed hub genes to promote growth, sustained proliferation, resistance to apoptosis, induction of angiogenesis, activation of invasion, and metastasis, enabling replicative immortality, evading immune destruction, and tumor-promoting inflammation. The drug–gene interaction analysis identified 182 potential drugs to interact with 27 target genes of miR-200b/429 with paclitaxel, doxorubicin, dabrafenib, bortezomib, docetaxel, ABT-199, eribulin, vorinostat, etoposide, and mitoxantrone emerging as the top ten best candidate drugs. Taken together, miR-200b/429 and associated hub genes can be helpful for prognostic application and clinical management of cervical cancer.

Exploring craniofacial and dental development with microRNAs

microRNAs (miRs) are small RNA molecules that regulate many cellular and developmental processes. They control gene expression pathways during specific developmental time points and are required for tissue homeostasis and stem cell maintenance. miRs as therapeutic reagents in tissue regeneration and repair hold great promise and new technologies are currently being designed to facilitate their expression or inhibition. Due to the large amount of miR research in cells and cancer many cellular processes and gene networks have been delineated however, their in vivo response can be different in complex tissues and organs. Specifically, this report will discuss animal developmental models to understand the role of miRs as well as xenograft, disease, and injury models. We will discuss the role of miRs in clinical studies including their diagnostic function, as well as their potential ability to correct craniofacial diseases.

基于网络和基因差异表达信息的癌症致病基因预测

<p indent="0mm">In genome research, the discovery of disease-causing pathogenic genes is a major challenge in genome research. Recently, using the available biological data, many researchers have employed computational methods for pathogenic gene prediction. However, most of these computational methods are based on gene interaction networks or other similar networks, and the potential connection between the local network of specific genes and their differential expression information is rarely considered. This paper explores the biological properties of cancerous pathogenic genes and their neighbors based on a local network structure and the differential expression information of genes. Furthermore, machine learning methods are employed for predicting cancerous pathogenic genes based on the newly discovered biological properties. First, the expression data of 21 cancers and their pathogenic genes are obtained from the TCGA (The Cancer Genome Atlas) and OMIM (Online Mendelian Inheritance in Man) databases. Human-protein interaction and tissue-specific interaction networks corresponding to each cancer gene are used as background networks to analyze the potential biological properties between the neighborhood information of different networks and the changed information of the gene expression. Then, based on the discovered biological properties, a vector representation method for gene node features is established, and the support vector machine is employed for pathogenic gene prediction. Experimental results are verified using ICGC (International Cancer Genome Consortium), COSMIC (Catalogue Of Somatic Mutations In Cancer), NCG (Network of Cancer Genes), OncoKB (Oncology Knowledge Base), and other standard databases, as well as using related literature, disease annotations and pathway enrichments. Results show that the definition of gene features can be used to distinguish cancerous pathogenic genes from other genes, thus providing powerful hypotheses for cancerous pathogenic gene prediction. Furthermore, reliable pathogenic gene candidates are obtained for related biological experiments to promote research on the pathogenic mechanism of cancer, a complex disease.

Driver gene detection through Bayesian network integration of mutation and expression profiles.

The identification of mutated driver genes and the corresponding pathways is one of the primary goals in understanding tumorigenesis at the patient level. Integration of multi-dimensional genomic data from existing repositories, e.g., The Cancer Genome Atlas (TCGA), offers an effective way to tackle this issue. In this study, we aimed to leverage the complementary genomic information of individuals and create an integrative framework to identify cancer-related driver genes. Specifically, based on pinpointed differentially expressed genes, variants in somatic mutations and a gene interaction network, we proposed an unsupervised Bayesian network integration (BNI) method to detect driver genes and estimate the disease propagation at the patient and/or cohort levels. This new method first captures inherent structural information to construct a functional gene mutation network and then extracts the driver genes and their controlled downstream modules using the minimum cover subset method. Using other credible sources (e.g. Cancer Gene Census and Network of Cancer Genes), we validated the driver genes predicted by the BNI method in three TCGA pan-cancer cohorts. The proposed method provides an effective approach to address tumor heterogeneity faced by personalized medicine. The pinpointed drivers warrant further wet laboratory validation. The supplementary tables and source code can be obtained from https://xavieruniversityoflouisiana.sharefile.com/d-se6df2c8d0ebe4800a3030311efddafe5. Supplementary data are available at Bioinformatics online.

Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource

BackgroundGenetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development.ResultsHere, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation.ConclusionsOur study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

Interval breast cancer is associated with interferon immune response

BackgroundThe aggressive nature of breast cancers detected between planned mammographic screens, so-called interval cancers, remains elusive. Here, we aim to characterise underlying molecular features of interval cancer. MethodsFrom 672 patients with invasive breast cancer, we analysed gene expression differences between 90 ‘true’ interval cancer cases (i.e. women with low-dense breasts defined as per cent mammographic density <25%) and 310 screen-detected tumours while accounting for PAM50 subtypes and thus overall tumour aggressiveness. We computed an interval cancer gene expression profile (IC-Gx) in a total of 2270 breast tumours (regardless of interval cancer status) and tested for association with expression-based immune subtypes in breast cancer. In addition, we investigated the contribution of inherited and somatic genetic variants in distinct features of interval cancer. ResultsWe identified 8331 genes nominally associated with interval cancer (P-value < 0.05, fold-change > 1.5). Gene set enrichment analysis showed immune-related pathways as key processes altered in interval cancer. Our IC-Gx, based on 47 genes with the strongest associations (false discovery rate < 0.05), was found to be associated mainly with immune subtypes involving interferon response. We isolated an interaction network of interval cancer and interferon genes for which a significant load of somatic and germline variants in class I interferon genes was observed. ConclusionWe identified novel molecular features of interval breast cancer highlighting interferon pathways as a potential target for prevention or treatment.

Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors

Network analysis methods can potentially quantify cancer aberrations in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high-grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs) and to rank key genes associated with prognosis. Copy number alterations (CNAs) from targeted and whole-exome sequencing data were extracted for HGSOC patients (n = 45) treated with ICIs. CNAs at a gene level were represented on a protein–protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier–Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan–Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups. The network curvature analysis stratified patients at high risk of mortality with p = 0.00047 in Kaplan–Meier analysis in HGSOC patients receiving ICI. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. Network curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.

A Blueprint for Cancer-Related Inflammation and Host Innate Immunity.

Both in situ and allograft models of cancer in juvenile and adult Drosophila melanogaster fruit flies offer a powerful means for unravelling cancer gene networks and cancer–host interactions. They can also be used as tools for cost-effective drug discovery and repurposing. Moreover, in situ modeling of emerging tumors makes it possible to address cancer initiating events—a black box in cancer research, tackle the innate antitumor immune responses to incipient preneoplastic cells and recurrent growing tumors, and decipher the initiation and evolution of inflammation. These studies in Drosophila melanogaster can serve as a blueprint for studies in more complex organisms and help in the design of mechanism-based therapies for the individualized treatment of cancer diseases in humans. This review focuses on new discoveries in Drosophila related to the diverse innate immune responses to cancer-related inflammation and the systemic effects that are so detrimental to the host.

Impact of KMN network genes on progression and prognosis of non-small cell lung cancer.

The Knl1-Mis12-Ndc80 (KMN) network genes (including KNL, MIS12 and NDC80 complexes) encode a highly conserved network of protein complexes that act in cell mitosis. In recent years, multiple studies revealed that KMN network genes also play a vital role in tumor appearance and growth. However, the role of the KMN gene network in non-small cell lung cancer (NSCLC) remains unknown. In this study, we analyzed the effects of KMN genes expression and clinical phenotype in patients with lung adenocarcinoma (LUAD). The expression of KMN network genes and related clinical information was extracted from The Cancer Genome Atlas. The samples were classified into cluster I and II by consistent clustering. We analyzed the gene distribution by principal component analysis, and the potential risk characteristics were analyzed using the least absolute shrinkage and selection operator Cox regression algorithm. Univariate and multivariate Cox regression analyses were used to analyze the clinical information. The Database for Annotation, Visualization, and Integrated Discovery, Gene MANIA and gene set enrichment analysis were used to analyze function and correlation among genes of the KMN network. The expression levels of nine out of ten KMN genes were significantly up-regulated in LUAD and were associated with poor overall survival (OS). Higher expression of NDC80 and KNL1 was related to low OS in both univariate and multivariate analyses. According to two independent prognostic KMN network genes (KNL1 and NDC80), a risk signature was established to predict the prognosis of patients with LUAD. Additionally, the genes NDC80 and KNL1 were considerably enriched in pathways associated with signaling pathways, biological processes, and the cell cycle. The results indicate that KMN network genes are intimately related to lung adenocarcinoma. KMN network genes are involved in the malignant process of LUAD. Assessment of NDC80 and KNL1 might be helpful for prognostic stratification and treatment strategy development.

Abstract 2134: Bioinformatic analysis of the tumor biomarker thymidine kinase 1: Elucidating its cancer gene network and membrane expression across all cancers

Abstract The purpose of this study was to analyze the regulatory gene cancer network of the tumor biomarker Thymidine Kinase 1 (TK1) across all cancers and identify the genes with meaningful correlations that interact and drive tumorigenesis along with TK1. In addition, we sought to find a feasible mechanism for TK1 membrane expression in cancer cells. Using data from The Cancer Genome Atlas (TCGA), the pathway commons protein interaction database, the Biogrid database and the R graphite library, we analyzed the correlation of mRNA expression levels of all proteins that were involved in TK1 metabolic pathways or physically interacted with TK1. By searching the pathway commons protein interaction database and data drawn from the graphite library in R, we found that 1495 proteins were involved with TK1 metabolism. Using Dijkstra's algorithm we found that 294 proteins had 1-2 degrees of interaction with TK1. Analysis of the Biogrid database showed that 191 proteins physically interact with TK1. The genes with higher average Pearson's correlation values across all 25 cancers had the following functions: cell cycle regulation, DNA synthesis, replication and repair, transcription factors (E2F family), transport of mono and dicarboxylate for energy production pathways, transport of nucleosides, pyrimidine metabolism, DNA methylation of tumor suppressor genes, mRNA stabilization, aging, regulation of cell proliferation, control of cyclin kinases, apoptosis, and formation of channels on cell membranes. Among the genes that had significant correlation values with TK1 (P&amp;lt;0.001) were: RRM2(2), CDK1, CDK2, CDK4(0), CDC20(0), CDC25A, MKI67(0), MCM3, E2Fs, CD6, PXMP2, DTYMK(1), BRCA1, EZH2(0), NUDT1(3), SLC29A2(2), SLC25A10(2), SLC16A3(3), SLC25A19(3), PRIM1, CARHSP1(0), NKIRAS2(0), METTL23(0) CENPB(0), FAF1(0), COPS6 (0), TRIM28 (0), MSH2(0), AAMP (0). Further analysis revealed that 476 of the proteins from the common pathways and Biogrid had transmembrane (tm) domains. Although it had a moderate correlation value, SEZ6L protein, which is overexpressed on the cell membrane of lung, colon and gastric cancers was a physical interactor of TK1. Additional analysis showed that the nucleoside transporter SLC29A2, that transports both adenosine and thymidine, had a tm domain and 2 degrees of interaction with TK1. Like the purinosome, we hypothesize that TK1 in malignant cells may be complexing with other pyrimidine synthesis proteins. Moreover, the physical interaction with other proteins containing tm domains could provide a path for TK1 membrane expression. Overexpression, secretion and membrane association of TK1 may be an evolutionary advantage that supply enough enzymes to modify the nucleotide pool balance necessary for DNA replication in tumor proliferating cells. Future directions will focus on the TK1 protein-protein interactions found in this analysis. Citation Format: Edwin J. Velazquez, David M. Bellini, Rachel A. Skabelund, Tyler B. Humpherys, Jonathan R. Skidmore, Brett E. Pickett, Stephen R. Piccolo, Kim L. O'Neill. Bioinformatic analysis of the tumor biomarker thymidine kinase 1: Elucidating its cancer gene network and membrane expression across all cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2134.

Regulation of Hippo, TGFβ/SMAD, Wnt/β-Catenin, JAK/STAT, and NOTCH by Long Non-Coding RNAs in Pancreatic Cancer.

Rapidly evolving and ever-increasing knowledge of the molecular pathophysiology of pancreatic cancer has leveraged our understanding altogether to a next level. Compared to the exciting ground-breaking discoveries related to underlying mechanisms of pancreatic cancer onset and progression, however, there had been relatively few advances in the therapeutic options available for the treatment. Since the discovery of the DNA structure as a helix which replicates semi-conservatively to pass the genetic material to the progeny, there has been conceptual refinement and continuous addition of missing pieces to complete the landscape of central dogma. Starting from transcription to translation, modern era has witnessed non-coding RNA discovery and central role of these versatile regulators in onset and progression of pancreatic cancer. Long non-coding RNAs (lncRNAs) have been shown to act as competitive endogenous RNAs through sequestration and competitive binding to myriad of microRNAs in different cancers. In this article, we set spotlight on emerging evidence of regulation of different signaling pathways (Hippo, TGFβ/SMAD, Wnt/β-Catenin, JAK/STAT and NOTCH) by lncRNAs. Conceptual refinements have enabled us to understand how lncRNAs play central role in post-translational modifications of various proteins and how lncRNAs work with epigenetic-associated machinery to transcriptionally regulate gene network in pancreatic cancer.

Analysis of Nuclear Encoded Mitochondrial Gene Networks in Cervical Cancer.

Background:Cervical cancer (CC) is one of the most common female cancers in many developing and underdeveloped countries. High incidence, late presentation, and mortality suggested the need for molecular markers. Mitochondrial defects due to abnormal expression of nuclear-encoded mitochondrial genes (NEMG) have been reported during cancer progression. Nevertheless, the application of NEMG for the prognosis of CC is still elusive. Herein, we aimed to investigate the associations between NEMG and CC prognosis. Materials and Methods:The differentially expressed genes (DEG) in the TCGA-CESC dataset and NEMGs were retrieved from TACCO and Mitocarta2.0 databases, respectively. The impact of methylation on NEMG expression were predicted using DNMIVD and UALCAN tools. HCMDB tool was used to predict genes having metastatic potential. The prognostic models were constructed using DNMIVD, TACCO, GEPIA2, and SurvExpress. The functional enrichment analysis (FEA) was performed using clusterProfiler. The protein-protein interaction network (PPIN) was constructed to identify the hub genes (HG) using String and CytoHubba tools. Independent validation of the HG was performed using Oncomine and Human Protein Atlas databases. The druggable genes were predicted using DGIdb.Results:Among the 52 differentially expressed NEMG, 15 were regulated by DNA methylation. The expression level of 16, 10, and 7 has the potential for CC staging, prediction of metastasis, and prognosis. Moreover, 1 driver gene and 16 druggable genes were also identified. The FEA identified the enrichment of cancer-related pathways, including AMPK and carbon metabolism in cancer. The combined expression of 10 HG has been shown to affect patient survival. Conclusion:Our findings suggest that the abnormal expression of NEMGs may play a critical role in CC development and progression. The genes identified in our study may serve as a prognostic indicator and therapeutic target in CC.

Effective utilisation of influence maximization technique for the identification of significant nodes in breast cancer gene networks

BackgroundIdentifying the most important genes in a cancer gene network is a crucial step in understanding the disease's functional characteristics and finding an effective drug. MethodIn this study, a popular influence maximization technique was applied on a large breast cancer gene network to identify the most influential genes computationally. The novel approach involved incorporating gene expression data and protein to protein interaction network to create a customized pruned and weighted gene network. This was then readily provided to the influence maximization procedure. The weighted gene network was also processed through a widely accepted framework that identified essential proteins to benchmark the proposed method. ResultsThe proposed method's results had matched with the majority of the output from the benchmarked framework. The key takeaway from the experiment was that the influential genes identified by the proposed method, which did not match favorably with the widely accepted framework, were found to be very important by previous in-vivo studies on breast cancer. Interpretation & conclusionThe new findings generated from the proposed method give us a favorable reason to infer that influence maximization added a more diversified approach to define and identify important genes and could be incorporated with other popular computational techniques for more relevant results.

FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.

Identification of driver genes, whose mutations cause the development of tumors, is crucial for the improvement of cancer research and precision medicine. To overcome the problem that the traditional frequency-based methods cannot detect lowly recurrently mutated driver genes, researchers have focused on the functional impact of gene mutations and proposed the function-based methods. However, most of the function-based methods estimate the distribution of the null model through the non-parametric method, which is sensitive to sample size. Besides, such methods could probably lead to underselection or overselection results. In this study, we proposed a method to identify driver genes by using functional impact prediction neural network (FI-net). An artificial neural network as a parametric model was constructed to estimate the functional impact scores for genes, in which multi-omics features were used as the multivariate inputs. Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. According to the comprehensive evaluation criterion, FI-net was powerful among various datasets and outperformed the other methods in terms of the overlap fraction with Cancer Gene Census and Network of Cancer Genes database, and the consensus in predictions among methods. Furthermore, the results illustrated that FI-net can identify known and potential novel driver genes.