A STUDY OF MULTI-GENE EXPRESSION IN THE HIGHLY METASTASIZING HUMAN OVARIAN CANCER CELL LINE HO-8910PM AND ITS MOTHER CELL LINE HO-8910

Objective: To investigate multi gene expression in the highly metastasizing human ovarian cancer cell line HO 8910PM and its mother cell line HO 8910. Method: The expression of 9 kinds of gene products in HO 8910PM and its mother cell line HO 8910 was detected by S P immunohistochemical method. Result: Eight kinds oncogene products showed various degrees of positive expression in both HO 8910PM and HO 8910 cell lines except gene bax. The expression of P53, Cyclin D 1, CD 44 ν6 and EGFR in HO 8910PM was stronger than that in HO 8910. However, the expression of P16, nm23 in HO 8910PM was weaker than that in HO 8910. There was no significant difference on the expression of C erb B 2 and bcl 2 between the two cell lines. Conclusion: Stronger invasive and metastatic patential is found in HO 8910PM than that in HO 8910. Carcinogenesis is a result of multi oncogene and multiple step process cooperation.

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.

A Comprehensive Study of the Association between LEPR Gene rs1137101 Variant and Risk of Digestive System Cancers

Objective The leptin receptor,encoded by the LEPR gene,is involved in tumorigenesis.A potential functional variant of LEPR,rs1137101(Gln223Arg),has been extensively investigated for its contribution to the risk of digestive system(DS)cancers,but results remain conflicting rather than conclusive.Here,we performed a case–control study and subsequent meta-analysis to examine the association between rs1137101 and DS cancer risk.Methods A total of 1,727 patients with cancer(gastric/liver/colorectal:460/480/787)and 800 healthy controls were recruited.Genotyping of rs1137101 was conducted using a polymerase chain reactionrestriction fragment length polymorphism(PCR-RFLP)assay and confirmed using Sanger sequencing.Twenty-four eligible studies were included in the meta-analysis.Results After Bonferroni correction,the case–control study revealed that rs1137101 was significantly associated with the risk of liver cancer in the Hubei Chinese population.The meta-analysis suggested that rs1137101 is significantly associated with the risk of overall DS,gastric,and liver cancer in the Chinese population.Conclusion The LEPR rs1137101 variant may be a genetic biomarker for susceptibility to DS cancers(especially liver and gastric cancer)in the Chinese population.

Comprehensive analysis of the potential pathogenesis of COVID-19 infection and liver cancer

BACKGROUND A growing number of clinical examples suggest that coronavirus disease 2019(COVID-19)appears to have an impact on the treatment of patients with liver cancer compared to the normal population,and the prevalence of COVID-19 is significantly higher in patients with liver cancer.However,this mechanism of action has not been clarified.Gene sets for COVID-19(GSE180226)and liver cancer(GSE87630)were obtained from the Gene Expression Omnibus database.After identifying the common differentially expressed genes(DEGs)of COVID-19 and liver cancer,functional enrichment analysis,protein-protein interaction network construction and scree-ning and analysis of hub genes were performed.Subsequently,the validation of the differential expression of hub genes in the disease was performed and the regulatory network of transcription factors and hub genes was constructed.RESULTS Of 518 common DEGs were obtained by screening for functional analysis.Fifteen hub genes including aurora kinase B,cyclin B2,cell division cycle 20,cell division cycle associated 8,nucleolar and spindle associated protein 1,etc.,were further identified from DEGs using the“cytoHubba”plugin.Functional enrichment analysis of hub genes showed that these hub genes are associated with P53 signalling pathway regulation,cell cycle and other functions,and they may serve as potential molecular markers for COVID-19 and liver cancer.Finally,we selected 10 of the hub genes for in vitro expression validation in liver cancer cells.CONCLUSION Our study reveals a common pathogenesis of liver cancer and COVID-19.These common pathways and key genes may provide new ideas for further mechanistic studies.

Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication

BACKGROUND Liver cancer is one of the deadliest malignant tumors worldwide.Immunotherapy has provided hope to patients with advanced liver cancer,but only a small fraction of patients benefit from this treatment due to individual differences.Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies,thereby improving patient survival rates.Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer,the impact of cell-cell interactions in the tumor microenvir-onment has not been adequately considered.AIM To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy.METHODS Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways.Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells.The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features,and a least absolute shrinkage and selection operator(LASSO)regression model was constructed to screen for diagnostic-related features.Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model.Finally,3 genes(stathmin 1,cofilin 1,and C-C chemokine ligand 5)significantly associated with survival were identified and used to construct an immune-related gene signature.RESULTS The immune-related gene signature composed of stathmin 1,cofilin 1,and C-C chemokine ligand 5 was identified through cell-cell communication.The effectiveness of the identified gene signature was validated based on experi-mental results of predictive immunotherapy response,tumor mutation burden analysis,immune cell infiltration analysis,survival analysis,and expression analysis.CONCLUSION The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment,providing insights for personalized treatment strategies.

Immune-related gene characteristics:A new chapter in precision treatment of gastric cancer

Gastric cancer ranks as the sixth most prevalent cancer worldwide.In recent research within the realm of gastric cancer treatment,the identification and application of immune-related genetic features have emerged as groundbreaking advancements.The study by Ma et al,which developed a prognostic model based on 10 genes,categorizes patients into high and low-risk groups to predict their responsiveness to immune checkpoint inhibitor therapy.This research underscores the potential of immune-related genes as biomarkers for personalized treatment,offering insights into tumor mutation burden and immune phenotype scores.We advocate for further validation,understanding of biological mechanisms,and integration of diverse datasets to enhance the model's predictive accuracy and clinical application,marking a significant step towards personalized and precise treatment for gastric cancer.

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer

BACKGROUND Treatment options for patients with gastric cancer(GC)continue to improve,but the overall prognosis is poor.The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present,and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups.AIM To determined the effects of differentially expressed immune-related genes(DEIRGs)on the development,prognosis,tumor microenvironment(TME),and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations.METHODS Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas(TCGA),and immune-related genes(IRGs)were searched from ImmPort.DEIRGs were extracted from the intersection of the differentially-expressed genes(DEGs)and IRGs lists.The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes(KEGGs)and Gene Ontology(GO)databases.To identify genes associated with prognosis,a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression.The tumor risk score was divided into high-and lowrisk groups.The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model.The infiltration of immune cells was obtained using‘CIBERSORT,’and the infiltration of immune subgroups in high-and low-risk groups was analyzed.The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed.RESULTS We collected 412 GC and 36 adjacent tissue samples,and identified 3627 DEGs and 1311 IRGs.A total of 482 DEIRGs were obtained.GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes,receptor ligand activity,and signaling receptor activators.KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors,neuroactive ligand receptor interactions,and viral protein interactions.We ultimately obtained an immune-related signature based on 10 genes,including 9 risk genes(LCN1,LEAP2,TMSB15A mRNA,DEFB126,PI15,IGHD3-16,IGLV3-22,CGB5,and GLP2R)and 1 protective gene(LGR6).Kaplan-Meier survival analysis,receiver operating characteristic curve analysis,and risk curves confirmed that the risk model had good predictive ability.Multivariate COX analysis showed that age,stage,and risk score were independent prognostic factors for patients with GC.Meanwhile,patients in the low-risk group had higher tumor mutation burden and immunophenotype,which can be used to predict the immune checkpoint inhibitor response.Both cytotoxic T lymphocyte antigen4+and programmed death 1+patients with lower risk scores were more sensitive to immunotherapy.CONCLUSION In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME.By providing risk factor analysis and prognostic information,our risk model can provide new directions for immunotherapy in GC patients.

Comprehensive analysis of clinical and biological value of ING family genes in liver cancer

BACKGROUND Liver cancer(LIHC)is a malignant tumor that occurs in the liver and has a high mortality in cancer.The ING family genes were identified as tumor suppressor genes.Dysregulated expression of these genes can lead to cell cycle arrest,senescence and/or apoptosis.ING family genes are promising targets for anticancer therapy.However,their role in LIHC is still not well understood.AIM To have a better understanding of the important roles of ING family members in LIHC.METHODS A series of bioinformatics approaches(including gene expression analysis,genetic alteration analysis,survival analysis,immune infiltration analysis,prediction of upstream microRNAs(miRNAs)and long noncoding RNAs(lncRNAs)of ING1,and ING1-related gene functional enrichment analysis)was applied to study the expression profile,clinical relationship,prognostic significance and immune infiltration of ING in LIHC.The relationship between ING family genes expression and tumor associated immune checkpoints was investigated in LIHC.The molecular mechanism of ING1 mediated hepatocarcinogenesis was preliminarily discussed.RESULTS mRNA/protein expression of different ING family genes in LIHC was analyzed in different databases,showing that ING family genes were highly expressed in LIHC.In 47 samples from 366 LIHC patients,the ING family genes were altered at a rate of 13%.By comprehensively analyzing the expression,clinical pathological parameters and prognostic value of ING family genes,ING1/5 was identified.ING1/5 was related to poor prognosis of LIHC,suggesting that they may play key roles in LIHC tumorigenesis and progression.One of the target miRNAs of ING1 was identified as hsa-miR-214-3p.Two upstream lncRNAs of hsa-miR-214-3p,U91328.1,and HCG17,were identified.At the same time,we found that the expression of ING family genes was correlated with immune cell infiltration and immune checkpoint genes.CONCLUSION This study lays a foundation for further research on the potential mechanism and clinical value of ING family genes in the treatment and prognosis of LIHC.

Comprehensive analysis of gene mutations and mismatch repair in Chinese colorectal cancer patients

BACKGROUND RAS,BRAF,and mismatch repair(MMR)/microsatellite instability(MSI)are crucial biomarkers recommended by clinical practice guidelines for colorectal cancer(CRC).However,their characteristics and influencing factors in Chinese patients have not been thoroughly described.AIM To analyze the clinicopathological features of KRAS,NRAS,BRAF,and PIK3CA mutations and the DNA MMR status in CRC.METHODS We enrolled 2271 Chinese CRC patients at the China-Japan Friendship Hospital.MMR proteins were tested using immunohistochemical analysis,and the KRAS/NRAS/BRAF/PIK3CA mutations were determined using quantitative polymerase chain reaction.Microsatellite status was determined using an MSI detection kit.Statistical analyses were conducted using SPSS software and logistic regression.RESULTS The KRAS,NRAS,BRAF,and PIK3CA mutations were detected in 44.6%,3.4%,3.7%,and 3.9% of CRC patients,respectively.KRAS mutations were more likely to occur in patients with moderate-to-high differentiation.BRAF mutations were more likely to occur in patients with right-sided CRC,poorly differentiated,or no perineural invasion.Deficient MMR(dMMR)was detected in 7.9% of all patients and 16.8% of those with mucinous adenocarcinomas.KRAS,NRAS,BRAF,and PIK3CA mutations were detected in 29.6%,1.1%,8.1%,and 22.3% of patients with dMMR,respectively.The dMMR was more likely to occur in patients with a family history of CRC,aged<50 years,right-sided CRC,poorly differentiated histology,no perineural invasion,and with carcinoma in situ,stage I,or stage II tumors.CONCLUSION This study analyzed the molecular profiles of KRAS,NRAS,BRAF,PIK3CA,and MMR/MSI in CRC,identifying key influencing factors,with implications for clinical management of CRC.

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner.Machine Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique.Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification.Normally,microarrays include several genes and may cause confusion or false prediction.Therefore,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected genes.Which can allow the classifiers to yield the best performance for lung cancer classification.In addition,we proposed a modified version of AOA which can work effectively on the high dimensional dataset.In the modified AOA,the features are ranked by their weights and are used to initialize the AOA population.The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies.Finally,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation.The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction accuracy.For instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

Transglutaminase 2 serves as a pathogenic hub gene of KRAS mutant colon cancer based on integrated analysis

BACKGROUND Colon cancer is acknowledged as one of the most common malignancies worldwide,ranking third in United States regarding incidence and mortality.Notably,approximately 40%of colon cancer cases harbor oncogenic KRAS mutations,resulting in the continuous activation of epidermal growth factor receptor signaling.AIM To investigate the key pathogenic genes in KRAS mutant colon cancer holds considerable importance.METHODS Weighted gene co-expression network analysis,in combination with additional bioinformatics analysis,were conducted to screen the key factors driving the progression of KRAS mutant colon cancer.Meanwhile,various in vitro experiments were also conducted to explore the biological function of transglutaminase 2(TGM2).RESULTS Integrated analysis demonstrated that TGM2 acted as an independent prognostic factor for progression-free survival.Immunohistochemical analysis on tissue microarrays revealed that TGM2 was associated with an elevated probability of perineural invasion in patients with KRAS mutant colon cancer.Additionally,biological roles of the key gene TGM2 was also assessed,suggesting that the downregulation of TGM2 attenuated the proliferation,invasion,and migration of the KRAS mutant colon cancer cell line.CONCLUSION This study underscores the potential significance of TGM2 in the progression of KRAS mutant colon cancer.This insight not only offers a theoretical foundation for therapeutic approaches but also highlights the need for additional clinical trials and fundamental research to support our preliminary findings.

Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

BACKGROUND Gastric cancer(GC)is a common malignancy of the digestive system.According to global 2018 cancer data,GC has the fifth-highest incidence and the thirdhighest fatality rate among malignant tumors.More than 60%of GC are linked to infection with Helicobacter pylori(H.pylori),a gram-negative,active,microaerophilic,and helical bacterium.This parasite induces GC by producing toxic factors,such as cytotoxin-related gene A,vacuolar cytotoxin A,and outer membrane proteins.Ferroptosis,or iron-dependent programmed cell death,has been linked to GC,although there has been little research on the link between H.pylori infection-related GC and ferroptosis.AIM To identify coregulated differentially expressed genes among ferroptosis-related genes(FRGs)in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.METHODS Gene expression profiles of GC patients and those with H.pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus(GEO)databases.The FRGs were acquired from the FerrDb database.A ferroptosis-related gene prognostic index(FRGPI)was created using least absolute shrinkage and selection operator–Cox regression.The predictive ability of the FRGPI was validated in the GEO cohort.Finally,we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.RESULTS Four hub genes were identified(NOX4,MTCH1,GABARAPL2,and SLC2A3)and shown to accurately predict GC and H.pylori-associated GC.The FRGPI based on the hub genes could independently predict GC patient survival;GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group.The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression.Moreover,the gene expression levels of common immune checkpoint proteins dramatically increased in the highrisk subgroup of the FRGPI cohort.The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane.The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.CONCLUSION In this study,we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.

Identification of Prognosis-Related Genes and Key Target Genes for Pancreatic Cancer: A Bioinformatics Analysis

Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raising more concern for scholars to find effective methods to prevent and treat in order to improve the pancreatic cancer outcome. Using bioinformatic analysis, this study aims to pinpoint key genes that could impact PaCa patients’ prognosis and could be used as therapeutic targets. Methods: The TCGA and GEO datasets were integratively analyzed to identify prognosis-related differentially expressed genes. Next, the STRING database was used to develop PPI networks, and the MCODE and CytoNCA Cytoscape in Cytoscape were used to screen for critical genes. Through CytoNCA, three kinds of topology analysis were considered (degree, betweenness, and eigenvector). Essential genes were confirmed as potential target treatment through Go function and pathways enrichment analysis, a developed predictive risk model based on multivariate analysis, and the establishment of nomograms using the clinical information. Results: Overall, the GSE183795 and TCGA datasets associated 1311 and 2244 genes with pancreatic cancer prognosis, respectively. We identified 132 genes that were present in both datasets. The PPI network analysis using, the centrality analysis approach with the CytoNCA plug-in, showed that CDK2, PLK1, CCNB1, and TOP2A ranked in the top 5% across all three metrics. The independent analysis of a risk model revealed that the four key genes had a Hazard Ratio (HR) > 1. The monogram showed the predictive risk model and individual patient survival predictions were accurate. The results indicate that the effect of the selected vital genes was significant and that they could be used as biomarkers to predict a patient’s outcome and as possible target therapy in patients with pancreatic cancer. GO function and pathway analysis demonstrated that crucial genes might affect the P53 signaling pathway and FoxO signaling pathway, through which Meiotic nuclear division and cell cycle may have a significant function in essential genes affecting the outcome of patients who have pancreatic cancer. Conclusions: This study suggests that CDK2, CCNB1, PLK1 and TOP2A are four key genes that have a significant influence on PaCa migration and proliferation. CDK2, CCNB1, PLK1, and TOP2A can be used as potential PaCa prognostic biomarkers and therapeutic targets. However, experimental validation is necessary to confirm these predictions. Our study comes into contributions to the development of personalized target therapy for pancreatic cancer patients.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Establishment of a prognosis predictive model for liver cancer based on expression of genes involved in the ubiquitin-proteasome pathway

BACKGROUND The ubiquitin-proteasome pathway(UPP)has been proven to play important roles in cancer.AIM To investigate the prognostic significance of genes involved in the UPP and develop a predictive model for liver cancer based on the expression of these genes.METHODS In this study,UPP-related E1,E2,E3,deubiquitylating enzyme,and proteasome gene sets were obtained from the Kyoto Encyclopedia of Genes and Genomes(KEGG)database,aiming to screen the prognostic genes using univariate and multivariate regression analysis and develop a prognosis predictive model based RESULTS Five genes(including autophagy related 10,proteasome 20S subunit alpha 8,proteasome 20S subunit beta 2,ubiquitin specific peptidase 17 like family member 2,and ubiquitin specific peptidase 8)were proven significantly correlated with prognosis and used to develop a prognosis predictive model for liver cancer.Among training,validation,and Gene Expression Omnibus sets,the overall survival differed significantly between the high-risk and low-risk groups.The expression of the five genes was significantly associated with immunocyte infiltration,tumor stage,and postoperative recurrence.A total of 111 differentially expressed genes(DEGs)were identified between the high-risk and low-risk groups and they were enriched in 20 and 5 gene ontology and KEGG pathways.Cell division cycle 20,Kelch repeat and BTB domain containing 11,and DDB1 and CUL4 associated factor 4 like 2 were the DEGs in the E3 gene set that correlated with survival.CONCLUSION We have constructed a prognosis predictive model in patients with liver cancer,which contains five genes that associate with immunocyte infiltration,tumor stage,and postoperative recurrence.

Understanding the role of transmembrane 9 superfamily member 1 in bladder cancer pathogenesis

In this editorial we comment on the article by Wei et al,published in the recent issue of the World Journal of Clinical Oncology.The authors investigated the role of Transmembrane 9 superfamily member 1(TM9SF1)protein in bladder cancer(BC)carcinogenesis.Lentiviral vectors were used to achieve silencing or overexpression of TM9SF1 gene in three BC cell lines.These cell lines were then subject to cell counting kit 8,wound-healing assay,transwell assay,and flow cytometry.Proliferation,migration,and invasion of BC cells were increased in cell lines subjected to TM9SF1 overexpression.TM9SF1 silencing inhibited proliferation,migration and invasion of BC cells.The authors conclude that TM9SF1 may be an oncogene in bladder cancer pathogenesis.

Analysis of Genetic Alterations in TP53 Gene in Breast Cancer - A Secondary Publication

Tumor protein p53 (TP53) mediates DNA repair and cell proliferation in growing cells. The TP53 gene is a tumor suppressor that regulates the expression of target genes in response to multiple cellular stress factors. Key target genes are involved in crucial cellular events such as DNA repair, cell cycle regulation, apoptosis, metabolism, and senescence. TP53 genetic variants and the activity of the wild-type p53 protein (WT-p53) have been linked to a wide range of tumorigenesis. Various genetic and epigenetic alterations, including germline and somatic mutations, loss of heterozygosity, and DNA methylation, can alter TP53 activity, potentially resulting in cancer initiation and progression. This study was designed to screen three reported mutations in the DNA-binding domain of the p53 protein in breast cancer, to evaluate the relative susceptibility and risk associated with breast cancer in the local population. Genomic DNA was isolated from 30 breast tumor tissues along with controls. Tetra and Tri ARMS PCR were performed to detect mutations in the TP53 coding region. For SNPs c.637C>T and c.733C>T, all analyzed cases were homozygous for the wild-type allele ‘C,’ while for SNP c.745A>G, all cases were homozygous for the wild-type allele ‘A.’ These results indicate no relevance of these three SNPs to cancer progression in our study cohort. Additionally, the findings from whole exon sequencing will help to predict more precise outcomes and assess the importance of TP53 gene mutations in breast cancer patients.

OVER-EXPRESSION OF THE RAB5 GENE IN HUMAN LUNG ADENOCARCINOMA CELLS WITH HIGH METASTATIC POTENTIAL

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Stimuli-Responsive Gene Delivery Nanocarriers for Cancer Therapy

Gene therapy provides a promising approach in treating cancers with high efficacy and selectivity and few adverse effects.Currently,the development of functional vectors with safety and effectiveness is the intense focus for improving the delivery of nucleic acid drugs for gene therapy.For this purpose,stimuli-responsive nanocarriers displayed strong potential in improving the overall efficiencies of gene therapy and reducing adverse effects via effective protection,prolonged blood circulation,specific tumor accumulation,and controlled release profile of nucleic acid drugs.Besides,synergistic therapy could be achieved when combined with other therapeutic regimens.This review summarizes recent advances in various stimuliresponsive nanocarriers for gene delivery.Particularly,the nanocarriers responding to endogenous stimuli including pH,reactive oxygen species,glutathione,and enzyme,etc.,and exogenous stimuli including light,thermo,ultrasound,magnetic field,etc.,are introduced.Finally,the future challenges and prospects of stimuli-responsive gene delivery nanocarriers toward potential clinical translation are well discussed.The major objective of this review is to present the biomedical potential of stimuli-responsive gene delivery nanocarriers for cancer therapy and provide guidance for developing novel nanoplatforms that are clinically applicable.

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.