Identification of Critical Genes and Five Prognostic Biomarkers Associated with Colorectal Cancer

3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci, we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18, and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 - an inflammation-responsive gene in nerve nociceptors - was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18 and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 - an inflammation-responsive gene in nerve nociceptors - was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

Ferroptosis-related gene signature and clinical prognostic factors as prognostic marker for colon adenocarcinoma

Aim

To build a ferroptosis-related prognostic model for patients with colon adenocarcinoma (COAD).

Methods

COAD expression profiles from The Cancer Genome Atlas were used as the training set and GSE39582 from Gene Expression Omnibus as the validation set. Differentially expressed ferroptosis-related genes between patients with COAD and normal controls were screened, followed by tumor subtype exploration based on ferroptosis-related gene expression levels. A ferroptosis score (FS) model was constructed using least absolute shrinkage and selection operator penalized Cox analysis. Based on FS, patients were subgrouped into high- and low-risk subgroups and overall survival was predicted. The potential prognostic value of the FS model and the clinical characteristics were investigated using receiver operating characteristic curves.

Results

Twenty-four differentially expressed ferroptosis-related genes were identified, four of which (CYBB, PRNP, ACSL4, and ACSL6) were included in the prognostic signature. Moreover, age, pathological T stage, and tumor recurrence were independent prognostic factors for COAD. The FS model combined with three independent prognostic factors showed the best prognostic value (The Cancer Genome Atlas: area under the curve = 0.897; GSE39582: area under the curve = 0.858).

Conclusion

The novel prognostic model for patients with COAD constructed by pairing the FS model with three important independent prognostic factors showed promising clinical predictive value.

Identification and analysis of the molecular targets of statins in colorectal cancer

Colorectal cancer (CRC) is the third most common cancer in the world. According to several types of research, statins may impact the development and treatment of CRC. This work aimed to use bioinformatics to discover the relationship between statin targets and differentially expressed genes (DEGs) in CRC patients and determine the possible molecular effect of statins on CRC suppression. We used CRC datasets from the GEO database to select CRC-related DEGs. DGIdb and STITCH databases were used to identify gene targets of subtypes of statin. Further, we identified the statin target of CRC DEGs hub genes by using a Venn diagram of CRC DEGs and statin targets. Funrich and enrichr databases were carried out for the KEGG pathway and gene ontology (GO) enrichment analysis, respectively. GSE74604 and GSE10950 were used to identify CRC DEGs. After analyzing datasets,1370 genes were identified as CRC DEGs, and 345 targets were found for statins. We found that 35 genes are CRC DEGs statin targets. We found that statin targets in CRC were enriched in the receptor and metallopeptidase activity for molecular function, cytoplasm and plasma membrane for cellular component, signal transduction, and cell communication for biological process genes were substantially enriched based on FunRich enrichment. Analysis of the KEGG pathways revealed that the overexpressed DEGs were enriched in the IL-17, PPAR, and Toll-like receptor signaling pathways. Finally, CCNB1, DNMT1, AURKB, RAC1, PPARGC1A, CDKN1A, CAV1, IL1B, and HSPD1 were identified as hub CRC DEGs statin targets. The genetic and molecular aspects of our findings reveal that statins might have a therapeutic effect on CRC.

Identification of autophagy and angiogenesis modulators in colorectal cancer based on bioinformatics analysis

Colorectal cancer (CRC) is the third most common cancer and the fourth leading cause of cancer-related death worldwide. The purpose of this study was to discover novel molecular pathways and potential prognosis biomarkers. To achieve this, we acquired five microarray datasets from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes between CRC and adjacent normal tissue samples and further validated them using The Cancer Genome Atlas (TCGA) database. Using various analytical approaches, including the construction of a competing endogenous RNA (ceRNA) network, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway analyses, as well as survival analysis, we identified key genes and pathways associated with the diagnosis and prognosis of CRC. We obtained a total of 185 differentially expressed genes, comprising 17 lncRNAs, 30 miRNAs, and 138 mRNAs. The ceRNA network consisted of 17 lncRNAs, 25 miRNAs, and 7 mRNAs. Among the 7 mRNAs involved in the ceRNA network, SLC7A5 and KRT80 were found to be upregulated, while ADIPOQ, CCBE1, KCNB1, CADM2, and CHRDL1 were downregulated in CRC. Further analysis revealed that ADIPOQ and SLC7A5 are involved in the AMPK and mTOR signaling pathway, respectively. In addition, survival analysis demonstrated a statistically significant relationship between ADIPOQ, SLC7A5, and overall survival rates in CRC patients. In conclusion, our findings suggest that downregulation of ADIPOQ and upregulation of SLC7A5 in tumor cells lead to increased mTORC1 activity, reduced autophagy, enhanced angiogenesis, and ultimately contribute to cancer progression and decreased survival in CRC patients.

Novel methylation-related long non-coding RNA clinical outcome prediction method: the clinical phenotype and immune infiltration research in low-grade gliomas

Background

Recent studies have suggested that long non-coding RNAs (lncRNAs) may play crucial role in low-grade glioma; however, the underlying mechanisms linking them to epigenetic methylation remain unclear.

Methods

We downloaded expression level data for regulators associated with N1 methyladenosine (m1A), 5-methyladenine (m5C), and N6 methyladenosine (m6A) (M1A/M5C/M6A) methylation from the Cancer Genome Atlas-low-grade glioma (TCGA-LGG) database. We identified the expression patterns of lncRNAs, and selected methylation-related lncRNAs using Pearson correlation coefficient>0.4. Non-negative matrix dimensionality reduction was then used to determine the expression patterns of the methylation-associated lncRNAs. We constructed a weighted gene co-expression network analysis (WGCNA) network to explore the co-expression networks between the two expression patterns. Functional enrichment of the co-expression network was performed to identify biological differences between the expression patterns of different lncRNAs. We also constructed prognostic networks based on the methylation presence in lncRNAs in low-grade gliomas.

Results

We identified 44 regulators by literature review. Using a correlation coefficient greater than 0.4, we identified 2330 lncRNAs, among which 108 lncRNAs with independent prognostic values were further screened using univariate Cox regression at P< 0.05. Functional enrichment of the co-expression networks revealed that regulation of trans-synaptic signaling, modulation of chemical synaptic transmission, calmodulin binding, and SNARE binding were mostly enriched in the blue module. The calcium and CA2 signaling pathways were associated with different methylation-related long non-coding chains. Using the Least Absolute Shrinkage Selector Operator (LASSO) regression analysis, we analyzed a prognostic model containing four lncRNAs. The model's risk score was 1.12 *AC012063 + 0.74 * AC022382 + 0.32 * AL049712 + 0.16 * GSEC. Gene set variation analysis (GSVA) revealed significant differences in mismatch repair, cell cycle, WNT signaling pathway, NOTCH signaling pathway, Complement and Cascades, and cancer pathways at different GSEC expression levels. Thus, these results suggest that GSEC may be involved in the proliferation and invasion of low-grade glioma, making it a prognostic risk factor for low-grade glioma.

Conclusion

Our analysis identified methylation-related lncRNAs in low-grade gliomas, providing a foundation for further research on lncRNA methylation. We found that GSEC could serve as a candidate methylation marker and a prognostic risk factor for overall survival in low-grade glioma patients. These findings shed light on the underlying mechanisms of low-grade glioma development and may facilitate the development of new treatment strategies.

Comprehensive analysis of the differences between left- and right-side colorectal cancer and respective prognostic prediction

Background

Previous studies have reported that the tumor heterogeneity and complex oncogenic mechanisms of proximal and distal colon cancer (CRC) are divergent. Therefore, we aim to analyze the differences between left-sided CRC (L_cancer) and right-sided CRC (R_cancer), as well as constructing respective nomograms.

Methods

We enrolled 335 colon cancer patients (146 L_cancer patients and 189 R_cancer patients) from The Cancer Genome Atlas (TCGA) data sets, and 102 pairs of color cancer tissue and adjacent normal tissue (51 L_cancer patients and 51 R_cancer patients) from our hospital. Firstly, we analyzed the differences between the L_cancer patients and R_cancer patients, and then established the L_cancer and R_cancer prognostic models using LASSO Cox.

Results

R_cancer patients had lower survival than L_cancer patients. R_cancer patients had higher ESTIMATE and immune scores and lower tumor purity. These patterns of expression of immune checkpoint-related genes and TMB level were higher in R_cancer than in L_cancer patients. Finally, we using Lasso Cox regression analyses established a prognostic model for L_cancer patients and a prognostic model for R_cancer patients. The AUC values of the risk score for OS in L_cancer were 0.862 in the training set and 0.914 in the testing set, while those in R_cancer were 0.835 in the training set and 0.857 in the testing set. The AUC values in fivefold cross-validation were between 0.727 and 0.978, proving that the two prognostic models have great stability. The nomogram of L_cancer included prognostic genes, age, pathological M, pathological stage, and gender, the AUC values of which were 0.800 in the training set and 0.905 in the testing set. Meanwhile, the nomogram of R_cancer comprised prognostic genes, pathological N, pathological T, and age, the AUC values of which were 0.836 in the training set and 0.850 in the testing set. In the R_cancer patients, high-risk patients had a lower proportion of ‘B cells memory’, ‘Dendritic cells resting’, immune score, ESTIMATE score, immune checkpoint-related genes, and HLA-family genes, and a higher proportion of ‘T cells follicular helper’, ‘Dendritic cells activated’, and ‘Mast cells activated’.

Conclusions

We found significant differences between L_cancer and R_cancer patients and established a clinical predictive nomogram for L_cancer patients and a nomogram for R_cancer patients. Additionally, R_cancer patients in low-risk groups may be more beneficial from immunotherapy.

Identification and validation of a novel signature as a diagnostic and prognostic biomarker in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most common malignant neoplasms worldwide. Although marker genes associated with CRC have been identified previously, only a few have fulfilled the therapeutic demand. Therefore, based on differentially expressed genes (DEGs), this study aimed to establish a promising and valuable signature model to diagnose CRC and predict patient's prognosis.

METHODS

The key genes were screened from DEGs to establish a multiscale embedded gene co-expression network, protein-protein interaction network, and survival analysis. A support vector machine (SVM) diagnostic model was constructed by a supervised classification algorithm. Univariate Cox analysis was performed to construct two prognostic signatures for overall survival and disease-free survival by Kaplan-Meier analysis, respectively. Independent clinical prognostic indicators were identified, followed by univariable and multivariable Cox analysis. GSEA was used to evaluate the gene enrichment analysis and CIBERSORT was used to estimate the immune cell infiltration. Finally, key genes were validated by qPCR and IHC.

RESULTS

In this study, four key genes (DKC1, FLNA, CSE1L and NSUN5) were screened. The SVM diagnostic model, consisting of 4-gene signature, showed a good performance for the diagnostic (AUC = 0.9956). Meanwhile, the four-gene signature was also used to construct a risk score prognostic model for disease-free survival (DFS) and overall survival (OS), and the results indicated that the prognostic model performed best in predicting the DFS and OS of CRC patients. The risk score was validated as an independent prognostic factor to exhibit the accurate survival prediction for OS according to the independent prognostic value. Furthermore, immune cell infiltration analysis demonstrated that the high-risk group had a higher proportion of macrophages M0, and T cells CD4 memory resting was significantly higher in the low-risk group than in the high-risk group. In addition, functional analysis indicated that WNT and other four cancer-related signaling pathways were the most significantly enriched pathways in the high-risk group. Finally, qRT-PCR and IHC results demonstrated that the high expression of DKC1, CSE1L and NSUN5, and the low expression of FLNA were risk factors of CRC patients with a poor prognosis.

CONCLUSION

In this study, diagnosis and prognosis models were constructed based on the screened genes of DKC1, FLNA, CSE1L and NSUN5. The four-gene signature exhibited an excellent ability in CRC diagnosis and prognostic prediction. Our study supported and highlighted that the four-gene signature is conducive to better prognostic risk stratification and potential therapeutic targets for CRC patients.

m7G-Related DNA Damage Repair Genes are Potential Biomarkers for Predicting Prognosis and Immunotherapy Effectiveness in Colon Cancer Patients

Objective: m⁷G is a post-transcriptional modification modality, however, limited research has been conducted on its role in colon cancer. DNA damage repair (DDR) is an important factor that contributes to colon cancer development, growth and chemoresistance. This study aimed to explore whether m⁷G-related DNA damage repair genes may be used as biomarkers to predict the prognosis of colon cancer patients. Methods: We use non-negative matrix factorization (NMF) to type CRC patients into. Risk models were constructed using different expression genes in two clusters. We assessed the reliability of risk models with DCA curves, and a Nomogram. Meanwhile, The receiver operating characteristic and C-index curves were used to compare the predictive significance of the constructed risk models with other studies. In additional, we examined the significance of risk models on patients' immunity microenvironment and response to immune therapy. Finally, we used a series of cellular experiments to validate the effect of model genes on the malignant progression of CRC cells. Results: Twenty-eight m⁷G genes were obtained from the GSEA database. Multivariate Cox and LASSO Cox regression analysis was performed and eleven m⁷G-related DDR genes were identified for constructing the risk model. Survival and stage of CRC patients were worser in the high-risk group than in the low-risk group for both the training and test sets. Additionally, the different immune microenvironment status of patients in the high- and low-risk groups, suggesting that patients in the low-risk group may be more sensitive to immunotherapy, particularly immune checkpoint inhibitors. Finally, we found that depletion of ATP2A1, one of the risk genes in our model, influence the biologic behaviour of CRC cells significantly. Conclusion: The m⁷G-related DDR genes can be used as important markers for predicting patient prognosis and immunotherapy response. Our data suggest that ATP2A1 may promote the proliferation of colon cancer cells. These findings may provide new therapeutic targets for the treatment of colon cancer.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.

Methods

Two groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.

Results

We identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.

Conclusion

The findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

Contribution of Matrix Metalloproteinase-1 Genotypes to Colorectal Cancer in Taiwan

BACKGROUND/AIM

Matrix metalloproteinase-1 is responsible for extracellular matrix regulation, and its genetic role in colorectal cancer (CRC) is unclear. The aim of the study was to investigate the contribution of Matrix metalloproteinase-1 genotypes to CRC risk in Taiwan.

MATERIALS AND METHODS

A total of 362 cases and 362 controls were included and their MMP-1 -1607 (rs1799705) genotypes were examined. The environmental factors and clinical-pathological records were also analyzed.

RESULTS

The genotypic frequency of MMP-1 rs1799750 were different between the CRC and control groups (p for trend=0.0083). 1G/2G and 1G/1G were associated with lower risk (p=0.0438 and 0.0030, adjusted OR=0.73 and 0.54, 95%CI=0.54-0.90 and 0.37-0.83). Among non-smokers, those with 1G/2G and 1G/1G genotypes were at 0.70- and 0.48-fold odds of having CRC. Among non-alcohol drinkers, people with 1G/2G and 1G/1G genotypes were at 0.71- and 0.54-fold odds. The 1G/1G genotype were statistically lower among CRC patients with lymph node metastasis (7.2%) than those without (19.0%).

CONCLUSION

The genotypes at MMP-1 rs1799705 play a role in determining susceptibility to CRC risk in Taiwan.

Circulating lncRNA DANCR as a potential auxillary biomarker for the diagnosis and prognostic prediction of colorectal cancer

Studies have shown that long non-coding RNAs (lncRNAs) play vital roles in the development of cancer, including colorectal cancer (CRC). Our purpose is to validate the diagnostic value of serum differentiation antagonizing non-protein coding RNA (DANCR) in CRC by focusing on its expression and clinical application. lncRNA expression profiles of CRC patients were obtained and analyzed by repurposing the publically available microarray data. Tissue or serum specimens were obtained from 40 patients with primary CRC, 10 patients with recurrent CRC, 40 patients with colorectal polyps, and 40 healthy controls. It was found that DANCR level in the CRC tissue and serum was significantly increased, and serum DANCR expression was decreased in post-operative patients as compared with that in pre-treatment patients and recurrent patients. In addition, serum DANCR expression was significantly correlated with different TNM stages. Correlation analysis of DANCR and other diagnostic indicators showed that the serum DANCR expression level was significantly correlated with CA199 but not with CEA in CRC patients. As for diagnostic efficiency by ROC analysis, the area under the curve (AUC) of serum DANCR was higher than that of CEA and CA199 in CRC group vs. colorectal polyp group. Simultaneous detection of DANCR, CEA and CA199 yielded the highest sensitivity and AUC as compared with either of them alone. Taken together, serum DANCR was up-regulated in CRC patients and high expression of DANCR may prove to be a potential biomarker for the diagnosis of CRC.

Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer

Background

Colorectal cancer (CRC) is the second most common cause of cancer death in the United States and the third most common cancer globally. The incidence of CRC tends to be younger, and we urgently need a reliable prognostic assessment strategy.

Methods

Protein expression profile and clinical information of 390 CRC patients/samples were downloaded from the TCPA and TCGA database, respectively. The Kaplan-Meier, Cox regression, and Pearson correlation analysis were applied in this study.

Results

Based on the TCPA and TCGA database, we screened 6 hub proteins and first constructed protein risk signature, all of which were significantly associated with CRC patients' overall survival (OS). The risk score was an independent prognostic factor and significantly related with the size of the tumor in situ (T). 6 hub proteins were differentially expressed in cancer and normal tissues and in different CRC stages, which were validated at the ONCOMINE database. Next, 40 coexpressed proteins of 6 hub proteins were extracted from the TCPA database. In the protein-protein interaction (PPI) network, HER1, HER2, and CTNNB1 were at the center. Function enrichment analysis illustrated that 46 proteins were mainly involved in the EGFR (HER1) tyrosine kinase inhibitor resistance pathway.

Conclusion

Studies indicated that 6 hub proteins might be considered as new targets for CRC therapies, and the protein risk signature can be used to predict the OS of CRC patients.

Identification of Potential Key Genes for Pathogenesis and Prognosis in Prostate Cancer by Integrated Analysis of Gene Expression Profiles and the Cancer Genome Atlas

Background: Prostate cancer (PCa)is a malignancy of the urinary system with a high incidence, which is the second most common male cancer in the world. There are still huge challenges in the treatment of prostate cancer. It is urgent to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. Methods: Multiple gene differential expression profile datasets of PCa tissues and normal prostate tissues were integrated analysis by R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the overlapping Differentially Expressed Genes (DEG) were performed. The STRING online database was used in conjunction with Cytospace software for protein-protein interaction (PPI) network analysis to define hub genes. The relative mRNA expression of hub genes was detected in Gene Expression Profiling Interactive Analysis (GEPIA) database. A prognostic gene signature was identified by Univariate and multivariate Cox regression analysis. Results: Three hundred twelve up-regulated genes and 85 down-regulated genes were identified from three gene expression profiles (GSE69223, GSE3325, GSE55945) and The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset. Seven hub genes (FGF2, FLNA, FLNC, VCL, CAV1, ACTC1, and MYLK) further were detected, which related to the pathogenesis of PCa. Seven prognostic genes (BCO1, BAIAP2L2, C7, AP000844.2, ASB9, MKI67P1, and TMEM272) were screened to construct a prognostic gene signature, which shows good predictive power for survival by the ROC curve analysis. Conclusions: We identified a robust set of new potential key genes in PCa, which would provide reliable biomarkers for early diagnosis and prognosis and would promote molecular targeting therapy for PCa.

Metabolic reprogramming-associated genes predict overall survival for rectal cancer

Metabolic reprogramming has become a hot topic recently in the regulation of tumour biology. Although hundreds of altered metabolic genes have been reported to be associated with tumour development and progression, the important prognostic role of these metabolic genes remains unknown. We downloaded messenger RNA expression profiles and clinicopathological data from The Cancer Genome Atlas and the Gene Expression Omnibus database to uncover the prognostic role of these metabolic genes. Univariate Cox regression analysis and lasso Cox regression model were utilized in this study to screen prognostic associated metabolic genes. Patients with high‐risk demonstrated significantly poorer survival outcomes than patients with low‐risk in the TCGA database. Also, patients with high‐risk still showed significantly poorer survival outcomes than patients with low‐risk in the GEO database. What is more, gene set enrichment analyses were performed in this study to uncover significantly enriched GO terms and pathways in order to help identify potential underlying mechanisms. Our study identified some survival‐related metabolic genes for rectal cancer prognosis prediction. These genes might play essential roles in the regulation of metabolic microenvironment and in providing significant potential biomarkers in metabolic treatment.

Identification of Hub Genes Related to Carcinogenesis and Prognosis in Colorectal Cancer Based on Integrated Bioinformatics

The high mortality of colorectal cancer (CRC) patients and the limitations of conventional tumor-node-metastasis (TNM) stage emphasized the necessity of exploring hub genes closely related to carcinogenesis and prognosis in CRC. The study is aimed at identifying hub genes associated with carcinogenesis and prognosis for CRC. We identified and validated 212 differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets and the Cancer Genome Atlas (TCGA) database. We investigated functional enrichment analysis for DEGs. The protein-protein interaction (PPI) network was constructed, and hub modules and genes in CRC carcinogenesis were extracted. A prognostic signature was developed and validated based on Cox proportional hazards regression analysis. The DEGs mainly regulated biological processes covering response to stimulus, metabolic process, and affected molecular functions containing protein binding and catalytic activity. The DEGs played important roles in CRC-related pathways involving in preneoplastic lesions, carcinogenesis, metastasis, and poor prognosis. Hub genes closely related to CRC carcinogenesis were extracted including six genes in model 1 (CXCL1, CXCL3, CXCL8, CXCL11, NMU, and PPBP) and two genes and Metallothioneins (MTs) in model 2 (SLC26A3 and SLC30A10). Among them, CXCL8 was also related to prognosis. An eight-gene signature was proposed comprising AMH, WBSCR28, SFTA2, MYH2, POU4F1, SIX4, PGPEP1L, and PAX5. The study identified hub genes in CRC carcinogenesis and proposed an eight-gene signature with good reproducibility and robustness at the molecular level for CRC, which might provide directive significance for treatment selection and survival prediction.

In silico identification of microRNAs as candidate colorectal cancer biomarkers

The involvement of microRNA in cancers plays a significant role in their pathogenesis. Specific expressions of these non-coding RNAs also serve as biomarkers for early colorectal cancer diagnosis, but their laboratory/molecular identification is challenging and expensive. The aim of this study was to identify potential microRNAs for colorectal cancer diagnosis using in silico approach. Sequence similarity search was employed to obtain the candidate microRNA from the datasets, and three target prediction software were employed to determine their target genes. To determine the involvement of these microRNAs in colorectal cancer, the microRNA gene list obtained was used alongside with colorectal cancer expressed genes from gbCRC and CoReCG databases for gene intersection analysis. The involvement of these genes in the cancer subtype was further strengthened with the DAVID database. KEGG and Gene Ontology were used for the pathway and functional analysis, while STRING was employed for the interactions of protein network and further visualized by Cytoscape. The cBioPortal database was used to prioritize the target genes; prognostic and expression analysis were finally performed on the candidate microRNAs and the prioritized targets. This study, therefore, identified five candidate microRNAs, two hub genes (CTNNB1 and epidermal growth factor receptor), and seven significant target genes associated with colorectal cancer. The molecular validation studies are ongoing to ascertain the biological fitness of these findings.

Systemic Inflammation and the Increased Risk of Inflamm-Aging and Age-Associated Diseases in People Living With HIV on Long Term Suppressive Antiretroviral Therapy

The ART program in low- and middle-income countries (LMIC) like India, follows a public health approach with a standardized regimen for all people living with HIV (PLHIV). Based on the evidence from high-income countries (HIC), the risk of an enhanced, and accentuated onset of premature-aging or age-related diseases has been observed in PLHIV. However, very limited data is available on residual inflammation and immune activation in the populations who are on first-generation anti-HIV drugs like zidovudine and lamivudine that have more toxic side effects. Therefore, the aim of the present study was to evaluate the levels of systemic inflammation and understand the risk of age-associated diseases in PLHIV on long-term suppressive ART using a large number of biomarkers of inflammation and immune activation. Blood samples were obtained from therapy naïve PLHIV (Pre-ART, n = 43), PLHIV on ART for >5 years (ART, n = 53), and HIV-negative healthy controls (HIVNC, n = 41). Samples were analyzed for 92 markers of inflammation, sCD14, sCD163, and telomere length. Several statistical tests were performed to compare the groups under study. Multivariate linear regression was used to investigate the associations. Despite a median duration of 8 years of successful ART, sCD14 (p < 0.001) and sCD163 (p = 0.04) levels continued to be significantly elevated in ART group as compared to HIVNC. Eleven inflammatory markers, including 4E-BP1, ADA, CCL23, CD5, CD8A, CST5, MMP1, NT3, SLAMF1, TRAIL, and TRANCE, were found to be significantly different (p < 0.05) between the groups. Many of these markers are associated with age-related co-morbidities including cardiovascular disease, neurocognitive decline and some of these markers are being reported for the first time in the context of HIV-induced inflammation. Linear regression analysis showed a significant negative association between HIV-1-positivity and telomere length (p < 0.0001). In ART-group CXCL1 (p = 0.048) and TGF-α (p = 0.026) showed a significant association with the increased telomere length and IL-10RA was significantly associated with decreased telomere length (p = 0.042). This observation warrants further mechanistic studies to generate evidence to highlight the need for enhanced treatment monitoring and special interventions in HIV-infected individuals.

Emerging roles for multifunctional ion channel auxiliary subunits in cancer

Several superfamilies of plasma membrane channels which regulate transmembrane ion flux have also been shown to regulate a multitude of cellular processes, including proliferation and migration. Ion channels are typically multimeric complexes consisting of conducting subunits and auxiliary, non-conducting subunits. Auxiliary subunits modulate the function of conducting subunits and have putative non-conducting roles, further expanding the repertoire of cellular processes governed by ion channel complexes to processes such as transcellular adhesion and gene transcription. Given this expansive influence of ion channels on cellular behaviour it is perhaps no surprise that aberrant ion channel expression is a common occurrence in cancer. This review will focus on the conducting and non-conducting roles of the auxiliary subunits of various Ca2+, K+, Na+ and Cl- channels and the burgeoning evidence linking such auxiliary subunits to cancer. Several subunits are upregulated (e.g. Cavβ, Cavγ) and downregulated (e.g. Kvβ) in cancer, while other subunits have been functionally implicated as oncogenes (e.g. Navβ1, Cavα2δ1) and tumour suppressor genes (e.g. CLCA2, KCNE2, BKγ1) based on in vivo studies. The strengthening link between ion channel auxiliary subunits and cancer has exposed these subunits as potential biomarkers and therapeutic targets. However further mechanistic understanding is required into how these subunits contribute to tumour progression before their therapeutic potential can be fully realised.

Identification of novel biomarkers and small molecule drugs in human colorectal cancer by microarray and bioinformatics analysis

Background

Colorectal cancer (CRC) is one of the most common malignant tumors. In the present study, the expression profile of human multistage colorectal mucosa tissues, including healthy, adenoma, and adenocarcinoma samples was downloaded to identify critical genes and potential drugs in CRC.

Methods

Expression profiles, GSE33113 and GSE44076, were integrated using bioinformatics methods. Differentially expressed genes (DEGs) were analyzed by R language. Functional enrichment analyses of the DEGs were performed using the Database for Annotation, visualization, and integrated discovery (DAVID) database. Then, the search tool for the retrieval of interacting genes (STRING) database and Cytoscape were used to construct a protein–protein interaction (PPI) network and identify hub genes. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis (GEPIA). Connectivity Map (CMap) was used to query potential drugs for CRC.

Results

A total of 428 upregulated genes and 751 downregulated genes in CRC were identified. The functional changes of these DEGs were mainly associated with cell cycle, oocyte meiosis, DNA replication, p53 signaling pathway, and progesterone‐mediated oocyte maturation. A PPI network was identified by STRING with 482 nodes and 2,368 edges. Survival analysis revealed that high mRNA expression of AURKA, CCNB1, CCNF, and EXO1 was significantly associated with longer overall survival. Moreover, CMap predicted a panel of small molecules as possible adjuvant drugs to treat CRC.

Conclusion

Our study found key dysregulated genes involved in CRC and potential drugs to combat it, which may provide novel insights and potential biomarkers for prognosis, as well as providing new CRC treatments.

Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis

BACKGROUND

The current study aimed to identify potential diagnostic and prognostic gene biomarkers for colorectal cancer (CRC) based on the Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) dataset.

METHODS

Microarray data of gene expression profiles of CRC from GEO and RNA-sequencing dataset of CRC from TCGA were downloaded. After screening overlapping differentially expressed genes (DEGs) by R software, functional enrichment analyses of the DEGs were performed using the DAVID database. Then, the STRING database and Cytoscape were used to construct a protein-protein interaction (PPI) network and identify hub genes. The receiver operating characteristic (ROC) curves were conducted to assess the diagnostic values of the hub genes. Cox proportional hazards regression was performed to screen the potential prognostic genes. Kaplan-Meier curve and the time-dependent ROC curve were used to assess the prognostic values of the potential prognostic genes for CRC patients.

RESULTS

Integrated analysis of GEO and TCGA databases revealed 207 common DEGs in CRC. A PPI network consisted of 70 nodes and 170 edges were constructed and top 10 hub genes were identified. The area under curve (AUC) of the ROC curves of the hub genes were 0.900, 0.927, 0.869, 0.863, 0.980, 0.682, 0.903, 0.790, 0.995, and 0.989 for CCL19, CXCL1, CXCL5, CXCL11, CXCL12, GNG4, INSL5, NMU, PYY, and SST, respectively. A prognostic gene signature consisted of 9 genes including SLC4A4, NFE2L3, GLDN, PCOLCE2, TIMP1, CCL28, SCGB2A1, AXIN2, and MMP1 was constructed with a good performance in predicting overall survivals of CRC patients. The AUC of the time-dependent ROC curve was 0.741 for 5-year survival.

CONCLUSION

The results in this study might provide some directive significance for further exploring the potential biomarkers for diagnosis and prognosis prediction of CRC patients.