The prognostic and immunological impacts of DCX expression:a pan-cancer analysis

Background:Doublecortin(DCX),a microtubule-associated protein,is best known for its critical role in neuronal migration during neural development,where it stabilizes microtubules and guides neurons to their proper positions.Recently,DCX has been implicated in various cancer processes,suggesting it may influence tumor progression and the tumor microenvironment.Emerging evidence indicates that DCX can modulate cell migration,invasion,and interaction with immune cells,making it a potential player in oncogenesis.However,the role of DCX across different cancer types and its potential as a prognostic biomarker remain underexplored,necessitating a comprehensive analysis.Methods:We utilized The Cancer Genome Atlas to extract data on DCX expression in tumor and adjacent normal tissues across diverse cancer types.Differential expression analysis was conducted using differential expression sequencing 2.Survival analysis was performed with Kaplan-Meier estimates and Cox proportional hazards models.Correlations between DCX expression and tumor mutational burden,microsatellite instability,and immune infiltration were examined using Spearman’s correlation.Results:DCX showed variable expression across cancer types,with significant overexpression in certain tumors such as liver and lung cancer and downexpression in others like breast cancer.High DCX expression was correlated with poor prognosis in adrenocortical carcinoma but with better outcomes in low-grade glioma.Additionally,DCX expression was significantly associated with various immune markers and chemokines,suggesting a role in modulating the immune microenvironment.Conclusion:Our findings highlight the complex role of DCX in cancer,underlining its potential as a prognostic marker and its involvement in immune-related pathways.Targeting DCX could represent a novel approach to modulating tumor behavior and enhancing immune response in cancer therapy.

The evolution of cancer genomic medicine in Japan and the role of the National Cancer Center Japan

The journey to implement cancer genomic medicine(CGM)in oncology practice began in the 1980s,which is considered the dawn of genetic and genomic cancer research.At the time,a variety of activating oncogenic alterations and their functional significance were unveiled in cancer cells,which led to the development of molecular targeted therapies in the 2000s and beyond.Although CGM is still a relatively new discipline and it is difficult to predict to what extent CGM will benefit the diverse pool of cancer patients,the National Cancer Center(NCC)of Japan has already contributed considerably to CGM advancement for the conquest of cancer.Looking back at these past achievements of the NCC,we predict that the future of CGM will involve the following:1)A biobank of paired cancerous and non-cancerous tissues and cells from various cancer types and stages will be developed.The quantity and quality of these samples will be compatible with omics analyses.All biobank samples will be linked to longitudinal clinical information.2)New technologies,such as whole-genome sequencing and artificial intelligence,will be introduced and new bioresources for functional and pharmacologic analyses(e.g.,a patient-derived xenograft library)will be systematically deployed.3)Fast and bidirectional translational research(bench-to-bedside and bedside-to-bench)performed by basic researchers and clinical investigators,preferably working alongside each other at the same institution,will be implemented;4)Close collaborations between academia,industry,regulatory bodies,and funding agencies will be established.5)There will be an investment in the other branch of CGM,personalized preventive medicine,based on the individual's genetic predisposition to cancer.

Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer

BACKGROUND Breast cancer(BC),a leading malignant disease,affects women all over the world.Cancer associated fibroblasts(CAFs)stimulate epithelial-mesenchymal transition,and induce chemoresistance and immunosuppression.AIM To establish a CAFs-associated prognostic signature to improve BC patient out-come estimation.METHODS We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas(TCGA)databases,and 3661 BC samples from the The Gene Expression Omnibus.CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm.CAF-associated gene identification was done by weighted gene co-expression network analysis.A CAF risk signature was established via univariate,least absolute shrinkage and selection operator regression,and mul-tivariate Cox regression analyses.The receiver operating characteristic(ROC)and Kaplan-Meier curves were employed to evaluate the predictability of the model.Subsequently,a nomogram was developed with the risk score and patient clinical signature.Using Spearman's correlations analysis,the relationship between CAF risk score and gene set enrichment scores were examined.Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction(qRT-PCR).RESULTS Employing an 8-gene(IL18,MYD88,GLIPR1,TNN,BHLHE41,DNAJB5,FKBP14,and XG)signature,we attemp-ted to estimate BC patient prognosis.Based on our analysis,high-risk patients exhibited worse outcomes than low-risk patients.Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis.ROC analysis exhibited satisfactory nomogram predictability.The area under the curve showed 0.805 at 3 years,and 0.801 at 5 years in the TCGA cohort.We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes.CAF risk score was significantly correlated with most hallmark gene sets.Finally,the prognostic signature were further validated by qRT-PCR.CONCLUSION We introduced a newly-discovered CAFs-associated gene signature,which can be employed to estimate BC patient outcomes conveniently and accurately.

Systematic analysis of DNA polymerases as therapeutic targets in pan-cancers

Introduction:DNA polymerases are crucial for maintaining genome stability and influencing tumorigenesis.However,the clinical implications of DNA polymerases in tumorigenesis and their potential as anti-cancer therapy targets are not well understood.Methods:We conducted a systematic analysis using TCGA Pan-Cancer Atlas data and Gene Set Cancer Analysis results to examine the expression profiles of 15 DNA polymerases(POLYs)and their clinical correlations.We also evaluated the prognostic value of POLYs by analyzing their expression levels in relation to overall survival time(OS)using Kaplan-Meier survival curves.Additionally,we investigated the correlations between POLY expression and immune cells,DNA damage repair(DDR)pathways,and ubiquitination.Drug sensitivity analysis was performed to assess the relationship between POLY expression and drug response.Results:Our analysis revealed that 14 out of 15 POLYs exhibited significantly distinct expression patterns between tumor and normal samples across most cancer types,except for DNA nucleotidylexotransferase(DNTT).Specifically,POLD1 and POLE showed elevated expression in almost all cancers,while POLQ exhibited high expression levels in all cancer types.Some POLYs showed heightened expression in specific cancer subtypes,while others exhibited low expression.Kaplan-Meier survival curves demonstrated significant prognostic value of POLYs in multiple cancers,including PAAD,KIRC,and ACC.Cox analysis further validated these findings.Alteration patterns of POLYs varied significantly among different cancer types and were associated with poorer survival outcomes.Significant correlations were observed between the expression of POLY members and immune cells,DDR pathways,and ubiquitination.Drug sensitivity analysis indicated an inverse relationship between POLY expression and drug response.Conclusion:Our comprehensive study highlights the significant role of POLYs in cancer development and identifies them as promising prognostic and immunological biomarkers for various cancer types.Additionally,targeting POLYs therapeutically holds promise for tumor immunotherapy.

Integrative cancer genomics： models, algorithms and analysis

In the past decade, the remarkable development of high-throughput sequencing technology accelerates the generation of large amount of multiple dimensional data such as genomic, epigenomic, transcriptomic and proteomic data. The comprehensive data make it possible to understand the underlying mechanisms of biology and disease such as cancer systematically. It also provides great challenges for computa- tional cancer genomics due to the complexity, scale and noise of data. In this article, we aim to review the recent develop- ments and progresses of computational models, algorithms and analysis of complex data in cancer genomics. These topics of this paper include the identification of driver mutations, the genetic heterogeneity analysis, genomic markers discovery of drug response, pan-cancer scale analysis and so on.

Databases and Web Tools for Cancer Genomics Study

Publicly-accessible resources have promoted the advance of scientific discovery. The era of genomics and big data has brought the need for collaboration and data sharing in order to make effective use of this new knowledge. Here, we describe the web resources for cancer genomics research and rate them on the basis of the diversity of cancer types, sample size, omics data comprehensiveness, and user experience. The resources reviewed include data repository and analysis tools; and we hope such introduction will promote the awareness and facilitate the usage of these resources in the cancer research community.

Applications of probability and statistics in cancer genomics

Background:The past decade has witnessed a rapid progress in our understanding of the genetics of cancer and its progression.Probabilistic and statistical modeling played a pivotal role in the discovery of general patterns from cancer genomics datasets and continue to be of central importance for personalized medicine.Results:In this review we introduce cancer genomics from a probabilistic and statistical perspective.We start from(1)functional classification of genes into oncogenes and tumor suppressor genes,then(2)demonstrate the importance of comprehensive analysis of different mutation types for individual cancer genomes,followed by(3)tumor purity analysis,which in turn leads to(4)the concept of ploidy and clonality,that is next connected to(5)tumor evolution under treatment pressure,which yields insights into cancer drug resistance.We also discuss future challenges including the non-coding genomic regions,integrative analysis of genomics and epigenomics,as well as early cancer detection.Conclusion:We believe probabilistic and statistical modeling will continue to play important roles for novel discoveries in the field o f cancer genomics and personalized medicine.

The cancer genomics and global cancer genome collaboration

All cancers arise as a result of abnormalities occurring in the DNA sequence of cancer cells, and we are now stepping into an era in which it is feasible to obtain the complete DNA sequence of large cohorts of cancer patients. The International Cancer Genome Consortium （ICGC） launched in 2007 is devoted to coordinate large-scale cancer genome studies in tumors from 50 different cancer types and/or subtypes and systematic studies of more than 25,000 cancer genomes. Several participant groups have summa- rized and published their data for various cancers. As the active members of ICGC, Chinese cancer genome investi- gators have contributed research for 13 tumor types and released some research articles about esophageal, liver, bladder, and kidney cancers. As genetic alterations in thousands of tumors have now been catalogued, the pan- cancer analysis has become ICGC at present. The ICGC the most significant role of research network will reveal the repertoire of oncogenic mutations, uncover traces of the mutagenic influences, define molecular subtypes for clinicalimplication, and enable the development of individual therapeutics for human cancers.

Identification of potential diagnostic and prognostic biomarkers for breast cancer based on gene expression omnibus

BACKGROUND Breast cancer is regarded as a highly malignant neoplasm in the female population,posing a significant risk to women’s overall well-being.The prevalence of breast cancer has been observed to rise in China,accompanied by an earlier age of onset when compared to Western countries.Breast cancer continues to be a prominent contributor to cancer-related mortality and morbidity among women,primarily due to its limited responsiveness to conventional treatment modalities.The diagnostic process is challenging due to the presence of non-specific clinical manifestations and the suboptimal precision of conventional diagnostic tests.There is a prevailing uncertainty regarding the most effective screening method and target populations,as well as the specificities and execution of screening programs.AIM To identify diagnostic and prognostic biomarkers for breast cancer.METHODS Overlapping differentially expressed genes were screened based on Gene Expression Omnibus(GSE36765,GSE10810,and GSE20086)and The Cancer Genome Atlas datasets.A protein-protein interaction network was applied to excavate the hub genes among these differentially expressed genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses,as well as gene set enrichment analyses,were conducted to examine the functions of these genes and their potential mechanisms in the development of breast cancer.For clarification of the diagnostic and prognostic roles of these genes,Kaplan–Mei-er and Cox proportional hazards analyses were conducted.RESULTS This study demonstrated that calreticulin,heat shock protein family B member 1,insulin-like growth Factor 1,interleukin-1 receptor 1,Krüppel-like factor 4,suppressor of cytokine signaling 3,and triosephosphate isomerase 1 are potential diagnostic biomarkers of breast cancer as well as potential treatment targets with clinical implications.CONCLUSION The screening of biomarkers is of guiding significance for the diagnosis and prognosis of the diseases.

Comprehensive analysis of distal-less homeobox family gene expression in colon cancer

BACKGROUND The distal-less homeobox(DLX)gene family plays an important role in the development of several tumors.However,the expression pattern,prognostic and diagnostic value,possible regulatory mechanisms,and the relationship between DLX family genes and immune infiltration in colon cancer have not been systematically reported.AIM We aimed to comprehensively analyze the biological role of the DLX gene family in the pathogenesis of colon cancer.METHODS Colon cancer tissue and normal colon tissue samples were collected from the Cancer Genome Atlas and Gene Expression Omnibus databases.Wilcoxon rank sum test and t-test were used to assess DLX gene family expression between colon cancer tissue and unpaired normal colon tissue.cBioPortal was used to analyze DLX gene family variants.R software was used to analyze DLX gene expression in colon cancer and the relationship between DLX gene family expression and clinical features and correlation heat map.The survival package and Cox regression module were used to assess the prognostic value of the DLX gene family.The pROC package was used to analyze the diagnostic value of the DLX gene family.R software was used to analyze the possible regulatory mechanisms of DLX gene family members and related genes.The GSVA package was used to analyze the relationship between the DLX gene family and immune infiltration.The ggplot2,the survminer package,and the clusterProfiler package were used for visualization.RESULTS DLX1/2/3/4/5 were significantly aberrantly expressed in colon cancer patients.The expression of DLX genes were associated with M stage,pathologic stage,primary therapy outcome,residual tumor,lymphatic invasion,T stage,N stage,age,perineural invasion,and history of colon polyps.DLX5 was independently correlated with the prognosis of colon cancer in multivariate analysis.DLX1/2/3/4/5/6 were involved in the development and progression of colon cancer by participating in immune infiltration and associated pathways,including the Hippo signaling pathway,the Wnt signaling pathway,several signaling pathways regulating the pluripotency of stem cells,and Staphylococcus aureus infection.CONCLUSION The results of this study suggest a possible role for the DLX gene family as potential diagnostic or prognostic biomarkers and therapeutic targets in colon cancer.

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

Epigenetics is the study of phenotypic variations that do not alter DNA sequences.Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers.One of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer.Therefore,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences.Currently,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying loci.At each DNA methylation site,a beta value(β)is used to reflect the methylation intensity.Therefore,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user bias.This study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation sites.The efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research Institute.The performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data sets.The NBDC-GA outperformed the IGA in terms of convergence performance.Furthermore,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,respectively.The proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities that were previously linked to cancer.

Construction of a prognostic signature of m7G-related lncRNAs in bladder cancer:a bioinformatics analysis

Background:N7-methylguanosine(m7G)-related plays an important role in the occurrence and development of tumors,and some recent studies have pointed out that long non-coding RNA is involved in the occurrence and development of various cancers.However,there is no literature on how m7G-related lncRNAs predict the prognosis of bladder cancer.The purpose of this study was to develop a predictive feature based on long non-coding RNA(lncRNAs)associated with m7G to predict the prognosis of patients with bladder cancer.Methods:We obtained the RNA transcriptome data and clinical data of bladder cancer patients through the cancer genome atlas database,and obtained the lncRNAs related to m7G by co-expression analysis and Cox regression analysis.Then the signature was evaluated by receiver operating characteristic curve and nomogram,and single sample gene set concentration analysis was used to study the correlation between the predictive model and tumor immune microenvironment in high-risk and low-risk groups.Results:We got a total of 5 m7G related lncRNA(MAFG-DT,AP003352.1,AC242842.1,AC024060.1,FAM111A-DT),which may be related to the prognosis of patients with bladder cancer.For predicting 1-,3-and 5-year survival rates,the area under the receiver operating characteristic curve was 0.757 and 0.722 and 0.739,respectively.Kaplan-Meier analysis showed that the prognosis of bladder cancer patients in high risk group was worse than that in low risk group.Immunoassay showed that the immune function of patients with bladder cancer in high risk group was more active.Conclusion:Prognostic markers based on m7G-related lncRNAs can be used to independently predict the prognosis of patients with bladder cancer and provide therapeutic targets for future clinical treatment.

Development and Validation of a Carbohydrate Metabolism-Related Model for Predicting Prognosis and Immune Landscape in Hepatocellular Carcinoma Patients

Objective The activities and products of carbohydrate metabolism are involved in key processes of cancer.However,its relationship with hepatocellular carcinoma(HCC)is unclear.Methods The cancer genome atlas(TCGA)-HCC and ICGC-LIRI-JP datasets were acquired via public databases.Differentially expressed genes(DEGs)between HCC and control samples in the TCGA-HCC dataset were identified and overlapped with 355 carbohydrate metabolism-related genes(CRGs)to obtain differentially expressed CRGs(DE-CRGs).Then,univariate Cox and least absolute shrinkage and selection operator(LASSO)analyses were applied to identify risk model genes,and HCC samples were divided into high/low-risk groups according to the median risk score.Next,gene set enrichment analysis(GSEA)was performed on the risk model genes.The sensitivity of the risk model to immunotherapy and chemotherapy was also explored.Results A total of 8 risk model genes,namely,G6PD,PFKFB4,ACAT1,ALDH2,ACYP1,OGDHL,ACADS,and TKTL1,were identified.Moreover,the risk score,cancer status,age,and pathologic T stage were strongly associated with the prognosis of HCC patients.Both the stromal score and immune score had significant negative/positive correlations with the risk score,reflecting the important role of the risk model in immunotherapy sensitivity.Furthermore,the stromal and immune scores had significant negative/positive correlations with risk scores,reflecting the important role of the risk model in immunotherapy sensitivity.Eventually,we found that high-/low-risk patients were more sensitive to 102 drugs,suggesting that the risk model exhibited sensitivity to chemotherapy drugs.The results of the experiments in HCC tissue samples validated the expression of the risk model genes.Conclusion Through bioinformatic analysis,we constructed a carbohydrate metabolism-related risk model for HCC,contributing to the prognosis prediction and treatment of HCC patients.

Prognostic value of PEA3 subfamily gene expression in cholangiocarcinoma

BACKGROUND Cholangiocarcinoma(CCA)is a lethal malignancy with limited treatment options and poor prognosis.The PEA3 subfamily of E26 transformation specific genes:ETV1,ETV4,and ETV5 are known to play significant roles in various cancers by influencing cell proliferation,invasion,and metastasis.AIM To analyze PEA3 subfamily gene expression levels in CCA and their correlation with clinical parameters to determine their prognostic value for CCA.METHODS The expression levels of PEA3 subfamily genes in pan-cancer and CCA data in the cancer genome atlas and genotype-tissue expression project databases were analyzed with R language software.Survival curve and receiver operating characteristic analyses were performed using the SurvMiner,Survival,and Procr language packages.The gene expression profiling interactive analysis 2.0 database was used to analyze the expression levels of PEA3 subfamily genes in different subtypes and stages of CCA.Web Gestalt was used to perform the gene ontology/Kyoto encyclopedia of genes and genomes(GO/KEGG)analysis,and STRING database analysis was used to determine the genes and proteins related to PEA3 subfamily genes.RESULTS ETV1,ETV4,and ETV5 expression levels were significantly increased in CCA.There were significant differences in ETV1,ETV4,and ETV5 expression levels among the different subtypes of CCA,and predictive analysis revealed that only high ETV1 and ETV4 expression levels were significantly associated with shorter overall survival in patients with CCA.GO/KEGG analysis revealed that PEA3 subfamily genes were closely related to transcriptional misregulation in cancer.In vitro and in vivo experiments revealed that PEA3 silencing inhibited the invasion and metastasis of CCA cells.CONCLUSION The expression level of ETV4 may be a predictive biomarker of survival in patients with CCA.

Cell-free DNA liquid biopsy for early detection of gastrointestinal cancers:A systematic review

BACKGROUND Gastrointestinal tumors are among the most common cancer types,and early detection is paramount to improve their management.Cell-free DNA(cfDNA)liquid biopsy raises significant hopes for non-invasive early detection.AIM To describe current applications of this technology for gastrointestinal cancer detection and screening.METHODS A systematic review of the literature was performed across the PubMed database.Articles reporting the use of cfDNA liquid biopsy in the screening or diagnosis of gastrointestinal cancers were included in the analysis.RESULTS A total of 263 articles were screened for eligibility,of which 13 articles were included.Studies investigated colorectal cancer(5 studies),pancreatic cancer(2 studies),hepatocellular carcinoma(3 studies),and multi-cancer detection(3 studies),including gastric,oesophageal,or bile duct cancer,representing a total of 4824 patients.Test sensitivities ranged from 71% to 100%,and specificities ranged from 67.4% to 100%.Pre-cancerous lesions detection was less performant with a sensitivity of 16.9% and a 100% specificity in one study.Another study using a large biobank demonstrated a 94.9% sensitivity in detecting cancer up to 4 years before clinical symptoms,with a 61% accuracy in tissue-of-origin identification.CONCLUSION cfDNA liquid biopsy seems capable of detecting gastrointestinal cancers at an early stage of development in a non-invasive and repeatable manner and screening simultaneously for multiple cancer types in a single blood sample.Further trials in clinically relevant settings are required to determine the exact place of this technology in gastrointestinal cancer screening and diagnosis strategies.

Genomic alterations and molecular subtypes of gastric cancers in Asians

Gastric cancer(GC) is a highly heterogenic disease,and it is the second leading cause of cancer death in the world.Common chemotherapies are not very effective for GC,which often presents as an advanced or metastatic disease at diagnosis.Treatment options are limited,and the prognosis for advanced GCs is poor.The landscape of genomic alterations in GCs has recently been characterized by several international cancer genome programs,including studies that focused exclusively on GCs in Asians.These studies identified major recurrent driver mutations and provided new insights into the mutational heterogeneity and genetic profiles of GCs.An analysis of gene expression data by the Asian Cancer Research Group(ACRG) further uncovered four distinct molecular subtypes with well-defined clinical features and their intersections with actionable genetic alterations to which targeted therapeutic agents are either already available or under clinical development.In this article,we review the ACRG GC project.We also discuss the implications of the genetic and molecular findings from various GC genomic studies with respect to developing more precise diagnoses and treatment approaches for GCs.

Emerging molecular classifications and therapeutic implications for gastric cancer

Gastric cancer(GC) is a highly aggressive and life-threatening malignancy.Even with radical surgical removal and front-line chemotherapy,more than half of GCs locally relapse and metastasize at a distant site.The dismal outcomes reflect the ineffectiveness of a one-size fits-all approach for a highly heterogeneous disease with diverse etiological causes and complex molecular underpinnings.The recent comprehensive genomic and molecular profiling has led to our deepened understanding of GC.The emerging molecular classification schemes based on the genetic,epigenetic,and molecular signatures are providing great promise for the development of more effective therapeutic strategies in a more personalized and precise manner.To this end,the Cancer Genome Atlas(TCGA) research network conducted a comprehensive molecular evaluation of primary GCs and proposed a new molecular classification dividing GCs into four subtypes:Epstein-Barr virus-associated tumors,microsatellite unstable tumors,genomically stable tumors,and tumors with chromosomal instability.This review primarily focuses on the TCGA molecular classification of GCs and discusses the implications on novel targeted therapy strategies.We believe that these fundamental findings will support the future application of targeted therapies and will guide our efforts to develop more efficacious drugs to treat human GCs.

Identification of key genes controlling cancer stem cell characteristics in gastric cancer

BACKGROUND Self-renewal of gastric cancer stem cells(GCSCs)is considered to be the underlying cause of the metastasis,drug resistance,and recurrence of gastric cancer(GC).AIM To characterize the expression of stem cell-related genes in GC.METHODS RNA sequencing results and clinical data for gastric adenoma and adenocarcinoma samples were obtained from The Cancer Genome Atlas database,and the results of the GC mRNA expression-based stemness index(mRNAsi)were analyzed.Weighted gene coexpression network analysis was then used to find modules of interest and their key genes.Survival analysis of key genes was performed using the online tool Kaplan-Meier Plotter,and the online database Oncomine was used to assess the expression of key genes in GC.RESULTS mRNAsi was significantly upregulated in GC tissues compared to normal gastric tissues(P<0.0001).A total of 16 modules were obtained from the gene coexpression network;the brown module was most positively correlated with mRNAsi.Sixteen key genes(BUB1,BUB1 B,NCAPH,KIF14,RACGAP1,RAD54 L,TPX2,KIF15,KIF18 B,CENPF,TTK,KIF4 A,SGOL2,PLK4,XRCC2,a n d C1 orf112)were identified in the brown module.The functional and pathway enrichment analyses showed that the key genes were significantly enriched in the spindle cellular component,the sister chromatid segregation biological process,the motor activity molecular function,and the cell cycle and homologous recombination pathways.Survival analysis and Oncomine analysis revealed that the prognosis of patients with GC and the expression of three genes(RAD54 L,TPX2,and XRCC2)were consistently related.CONCLUSION Sixteen key genes are primarily associated with stem cell self-renewal and cell proliferation characteristics.RAD54 L,TPX2,and XRCC2 are the most likely therapeutic targets for inhibiting the stemness characteristics of GC cells.

Identification of ITGA3 as an Oncogene in Human Tongue Cancer via Integrated Bioinformatics Analysis

Human tongue cancer （TC） is an aggressive malignancy with a very poor prognosis. There is an urgent need to elucidate the underlying molecular mechanisms involved in TC progression, mRNA expression profiles play a vital role in the exploration of cancer-related genes. Therefore, the purpose of our study was to identify the progression associated candidate genes of TC by bioinformatics analysis. Five microarray datasets of TC samples were downloaded from the Gene Expression Onmibus （GEO） database and the data of 133 TC patients were screened from The Cancer Genome Atlas （TCGA） head and neck squamous cell carcinoma （HNSC） database. The integrated analysis of five microarray datasets and the RNA sequencing data of TC samples in TCGA-HNSC was performed to obtain 1023 overlapping differentially expressed genes （DEGs） in TC and adjacent normal tissue （ANT） samples. Next, Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analysis was conducted to enrich the significant pathways of the 1023 DEGs and PI3K- Akt signaling pathway （P=0.011） was selected to be the candidate pathway. A total of 23 DEGs with ｜log2 fold change （FC）｜ ≥1.0 in phosphatidylinositol 3-kinase-serine/threonine kinase （PI3K-Akt） signaling pathway were subjected to survival analysis of 125 eligible TC samples in TCGA database, indicating increased integrin-α3 gene （ITGA3） expression was significantly associated with poorer prognosis. Taken together, our study suggested ITGA3 may facilitate the development of TC via activating PI3K-Akt signaling pathway.

Weighted Gene Co-expression Network Analysis of Gene Modules for the Prognosis of Esophageal Cancer

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas(TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival(PFS) or overall survival(OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that 'glycoprotein binding' was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor(PTAFR) and feline Gardner-Rasheed(FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.