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

Role of microRNA-363 during tumor progression and invasion

Recent progresses in diagnostic and therapeutic methods have significantly improved prognosis in cancer patients. However, cancer is still considered as one of the main causes of human deaths in the world. Late diagnosis in advanced tumor stages can reduce the effectiveness of treatment methods and increase mortality rate of cancer patients. Therefore, investigating the molecular mechanisms of tumor progression can help to introduce the early diagnostic markers in these patients. MicroRNA (miRNAs) has an important role in regulation of pathophysiological cellular processes. Due to their high stability in body fluids, they are always used as the non-invasive markers in cancer patients. Since, miR-363 deregulation has been reported in a wide range of cancers, we discussed the role of miR-363 during tumor progression and metastasis. It has been reported that miR-363 has mainly a tumor suppressor function through the regulation of transcription factors, apoptosis, cell cycle, and structural proteins. MiR-363 also affected the tumor progression via regulation of various signaling pathways such as WNT, MAPK, TGF-β, NOTCH, and PI3K/AKT. Therefore, miR-363 can be introduced as a probable therapeutic target as well as a non-invasive diagnostic marker in cancer patients.

Identification of crucial ubiquitin-associated genes for predicting the effects of immunotherapy and therapeutic agents in colorectal cancer

BACKGROUND

A growing body of research indicates that colorectal cancer (CRC) is significantly influenced by the ubiquitin-proteasome system. Nevertheless, reliable immune landscapes and ubiquitin-associated prognostic markers are still scarce.

METHODS

We systematically analyzed the RNA-seq data of 2,830 ubiquitin-related genes from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A CRC prognostic risk model was developed based on ubiquitin-associated gene signatures. In-depth multi-dimensional analyses were performed on ubiquitin-related subgroups with high and low risk. Drug response sensitivity for high-risk CRC patients was also predicted.

RESULTS

A total of 131 ubiquitin-related differentially expressed genes were retrieved, of which 9 prognostic genes for CRC were ultimately identified and further validated by our clinical CRC tumor and adjacent normal samples. The expression pattern of these 9 ubiquitin-associated genes was found to be strongly related to overall survival, immune cell fractions, and immune-related genes of CRC patients. CRC patients stratified by the ubiquitin prognostic model exhibited distinct clinicopathological characteristics and immune landscapes. A comprehensive framework for personalized medicine prediction identified regorafenib and sorafenib as the most promising therapeutic agents for high ubiquitin-related risk CRC patients, which was confirmed in cell viability assays.

CONCLUSIONS

Ubiquitin characteristics can reflect CRC prognosis and help develop innovative biomarkers for precision treatment.

Spatial Transcriptomic Profiling of Tetraspanins in Stage 4 Colon Cancer from Primary Tumor and Liver Metastasis

Stage 4 colon cancer (CC) presents a significant global health challenge due to its poor prognosis and limited treatment options. Tetraspanins, the transmembrane proteins involved in crucial cancer processes, have recently gained attention as diagnostic markers and therapeutic targets. However, their spatial expression and potential roles in stage 4 CC tissues remain unknown. Using the GeoMx digital spatial profiler, we profiled all 33 human tetraspanin genes in 48 areas within stage 4 CC tissues, segmented into immune, fibroblast, and tumor compartments. Our results unveiled diverse gene expression patterns across different primary tumor sub-regions. CD53 exhibited distinct overexpression in the immune compartment, hinting at a potential role in immune modulation. TSPAN9 was specifically overexpressed in the fibroblast compartment, suggesting involvement in tumor invasion and metastasis. CD9, CD151, TSPAN1, TSPAN3, TSPAN8, and TSPAN13 displayed specific overexpression in the tumor compartment, indicating potential roles in tumor growth. Furthermore, our differential analysis revealed significant spatial changes in tetraspanin expression between patient-matched stage 4 primary CC and metastatic liver tissues. These findings provide spatially resolved insights into the expression and potential roles of tetraspanins in stage 4 CC progression, proposing their utility as diagnostic markers and therapeutic targets. Understanding this landscape is beneficial for tailoring therapeutic strategies to specific sub-tumor regions in the context of stage 4 CC and liver metastasis.

A Comprehensive Analysis of the Prognostic, Immunological and Diagnostic Role of CCNF in Pan-cancer

Background: Cyclin F (CCNF) represents a pivotal constituent within the family of cell cycle proteins, which also belongs to the F-box protein family and acts as a critical regulatory factor in cell cycle transition. Its heightened expression has been consistently identified across various cancer types, including breast, pancreatic, and colorectal cancer. Nonetheless, a comprehensive exploration of CCNF's involvement in pan-cancer remains lacking. Methods: This study collected transcriptomic data and clinical information from several databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and BioGPS detabase. Employing bioinformatics methods, we investigated the potential oncogenic role of CCNF, utilizing various databases such as cBioPortal, Human Protein Atlas (HPA), TIMER2, UALCAN, GEPIA, GSCALite, and CTD detabase. These analyses focused on exploring CCNF expression, prognosis, gene mutations, immune cell infiltration, DNA methylation levels, and targeted chemical drugs across different tumor types. Additionally, we obtained CCNF-related genes from GeneMANIA and GEPIA databases and conducted GO and KEGG enrichment analyses to gain deeper insights into the biological processes associated with CCNF. Furthermore, we validated the differential expression of CCNF in normal human breast cancer and breast cancer cell lines using experimental verification. Results: CCNF exhibited upregulation in the majority of cancer types, demonstrating early diagnostic potential in 15 cancers and prognostic implications for adverse outcomes across numerous malignancies. Furthermore, CCNF was found to be linked with markers of the tumor immune microenvironment in various cancers. Additionally, CCNF expression influenced genetic alterations in pan-cancer. Enrichment analysis revealed that CCNF primarily participates in crucial biological pathways such as the cell cycle, p53 signaling pathway, and cellular senescence pathways. RT-qpcr and WB assays further confirmed that CCNF expression was higher in human cancer cell lines than in normal cell lines. Conclusion: The underlying role and mechanism of CCNF in pan-cancer were elucidated through comprehensive bioinformatics analysis and experimental validation. CCNF holds promise as an invaluable early detection indicator and tumor biomarker, offering potential targets for tumor treatment and prevention.

Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis

Purpose

Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. Although interventions such as anti-VEGF and laser have high success rates in treating severe ROP, current treatment and preventative strategies still have their limitations. Thus, we aim to identify drugs and chemicals for ROP with comprehensive safety profiles and tolerability using a computational bioinformatics approach.

Methods

We generated a list of genes associated with ROP to date by querying PubMed Gene which draws from animal models, human studies, and genomic studies in the NCBI database. Gene enrichment analysis was performed on the ROP gene list with the ToppGene program which draws from multiple drug-gene interaction databases to predict compounds with significant associations to the ROP gene list. Compounds with significant toxicities or without known clinical indications were filtered out from the final drug list.

Results

The NCBI query identified 47 ROP genes with pharmacologic annotations present in ToppGene. Enrichment analysis revealed multiple drugs and chemical compounds related to the ROP gene list. The top ten most significant compounds associated with ROP include ascorbic acid, simvastatin, acetylcysteine, niacin, castor oil, penicillamine, curcumin, losartan, capsaicin, and metformin. Antioxidants, NSAIDs, antihypertensives, and anti-diabetics are the most common top drug classes derived from this analysis, and many of these compounds have potential to be readily repurposed for ROP as new prevention and treatment strategies.

Conclusion

This bioinformatics analysis creates an unbiased approach for drug discovery by identifying compounds associated to the known genes and pathways of ROP. While predictions from bioinformatic studies require preclinical/clinical studies to validate their results, this technique could certainly guide future investigations for pathologies like ROP.

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

BACKGROUND

Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.

METHODS

To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.

RESULTS

We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.

CONCLUSION

The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

Bioinformatics Analysis of RNA-seq Data Reveals Genes Related to Cancer Stem Cells in Colorectal Cancerogenesis

Cancer stem cells (CSC) play one of the crucial roles in the pathogenesis of various cancers, including colorectal cancer (CRC). Although great efforts have been made regarding our understanding of the cancerogenesis of CRC, CSC involvement in CRC development is still poorly understood. Using bioinformatics and RNA-seq data of normal mucosa, colorectal adenoma, and carcinoma (n = 106) from GEO and TCGA, we identified candidate CSC genes and analyzed pathway enrichment analysis (PEI) and protein–protein interaction analysis (PPI). Identified CSC-related genes were validated using qPCR and tissue samples from 47 patients with adenoma, adenoma with early carcinoma, and carcinoma without and with lymph node metastasis and were compared to normal mucosa. Six CSC-related genes were identified: ANLN, CDK1, ECT2, PDGFD, TNC, and TNXB. ANLN, CDK1, ECT2, and TNC were differentially expressed between adenoma and adenoma with early carcinoma. TNC was differentially expressed in CRC without lymph node metastases whereas ANLN, CDK1, and PDGFD were differentially expressed in CRC with lymph node metastases compared to normal mucosa. ANLN and PDGFD were differentially expressed between carcinoma without and with lymph node metastasis. Our study identified and validated CSC-related genes that might be involved in early stages of CRC development (ANLN, CDK1, ECT2, TNC) and in development of metastasis (ANLN, PDGFD).

Using Computational Drug-Gene Analysis to Identify Novel Therapeutic Candidates for Retinal Neuroprotection

Retinal cell death is responsible for irreversible vision loss in many retinal disorders. No commercially approved treatments are currently available to attenuate retinal cell loss and preserve vision. We seek to identify chemicals/drugs with thoroughly-studied biological functions that possess neuroprotective effects in the retina using a computational bioinformatics approach. We queried the National Center for Biotechnology Information (NCBI) to identify genes associated with retinal neuroprotection. Enrichment analysis was performed using ToppGene to identify compounds related to the identified genes. This analysis constructs a Pharmacome from multiple drug-gene interaction databases to predict compounds with statistically significant associations to genes involved in retinal neuroprotection. Compounds with known deleterious effects (e.g., asbestos, ethanol) or with no clinical indications (e.g., paraquat, ozone) were manually filtered. We identified numerous drug/chemical classes associated to multiple genes implicated in retinal neuroprotection using a systematic computational approach. Anti-diabetics, lipid-lowering medicines, and antioxidants are among the treatments anticipated by this analysis, and many of these drugs could be readily repurposed for retinal neuroprotection. Our technique serves as an unbiased tool that can be utilized in the future to lead focused preclinical and clinical investigations for complex processes such as neuroprotection, as well as a wide range of other ocular pathologies.

The Roles of EXO1 and RPA1 Polymorphisms in Prognosis of Lung Cancer Patients Treated with Platinum-Based Chemotherapy

Background. Lung cancer is one of the major causes of cancer-related mortality worldwide. DNA repair and damage response contribute to genomic instability that accompanies tumor progression. In this study, we focus on evaluating association between DNA repair polymorphisms of EXO1, RPA1, and prognosis in lung cancer patients whom received platinum-based chemotherapy. Methods. 593 lung cancer patients were recruited in this study. We performed genotyping of 19 single nucleotide polymorphisms (SNPs) by Sequenom MassARRAY. Cox regression analysis was used to assess overall survival (OS) and progression-free survival (PFS) among SNP genotypes. Results. Significant differences in PFS and OS were observed in RPA1 rs5030740, EXO1 rs1776148, and rs1047840. Results showed that patients with CC genotype in rs5030740 (recessive model: $P=0.034$ ) had a better PFS. Patients with AA or/and AG genotypes in rs1776148 (additive model: $P=0.004$ ; dominant model: $P=0.048$ ) and AA genotype in rs1047840 (recessive model: $P=0.023$ ) had longer OS. We also demonstrated differences in subgroup analysis between rs5030740, rs1776148, rs1047840, and prognosis. Conclusions. Our results indicated that EXO1 rs1776148, rs1047840, and RPA1 rs5030740 were significantly associated with prognosis of lung cancer. Rs1776148, rs1047840, and rs5030740 may act as prognosis markers in lung cancer patients with platinum-based chemotherapy.

Upregulation of EMID1 Accelerates to a Favorable Prognosis and Immune Infiltration in Lung Adenocarcinoma

Lung cancer is a difficult-to-treat cancer. Lung adenocarcinoma (LUAD) is the main subtype of lung cancer. Although there are many ways to treat lung cancer, the survival rate of patients is low. Therefore, novel molecules need to be identified to diagnose and treat LUAD. This study utilized The Cancer Genome Atlas (TCGA) LUAD data to analyze and validate the value of EMID1 as a LUAD diagnostic surface marker and overall survival prognostic marker. Differential expression analysis formally confirmed that decreased EMID1 expression was significantly associated with advanced stage and metastasis of lung cancer. Kaplan-Meier survival analysis showed that the patients with low EMID expression are dismal. The relationship between clinicopathological features and EMID1 was scored using Wilcoxon signed-rank test and R (v.3.5.1) logistic regression and suggested that patients with low EMID1 expression had a worse prognosis than patients with high EMID1 expression. (Gene Ontology) GO, Kyoto Encyclopedia of Genes and Genomes(KEGG), and gene set enrichment analysis (GSEA) were performed to investigate the potential mechanism of EMID1 expression on the prognosis of LUAD and suggested that Notch signaling pathway may be an important biological pathway for EMID1 to play a role in LUAD. Further, combined with univariate and multivariate Cox regression analysis, it was speculated that high and low levels of EMID1 expression and the logistic regression analysis of related clinical variables had significant clinical significance to verify the underlying mechanism of LUAD focus and prognosis. EMID1 plays an important role in the immune milieu of LUAD. Meanwhile, the correlation between tumor-infiltrating immune cells and genes was assessed using CIBERSORT, and it was found that the level of B cell infiltration was positively correlated with the expression of EMID1, all of which were validated in the GEO and GEPIA databases. In all, this study helps to understand the immune microenvironment of LUAD and improve the survival of patients with LUAD. Thus, EMID1 may be a novel immune-related prognostic marker of LUAD.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Age-Related Macular Degeneration

Purpose

Age-related macular degeneration (AMD) is the most common cause of aging-related blindness in the developing world. Although medications can slow progressive wet AMD, currently, no drugs to treat dry-AMD are available. We use a systems or in silico biology analysis to identify chemicals and drugs approved by the Food and Drug Administration for other indications that can be used to treat and prevent AMD.

Methods

We queried National Center for Biotechnology Information to identify genes associated with AMD, wet AMD, dry AMD, intermediate AMD, and geographic atrophy to date. We combined genes from various AMD subtypes to reflect distinct stages of disease. Enrichment analysis using the ToppGene platform predicted molecules that can influence AMD genes. Compounds without clinical indications or with deleterious effects were manually filtered.

Results

We identified several drug/chemical classes that can affect multiple genes involved in AMD. The drugs predicted from this analysis include antidiabetics, lipid-lowering agents, and antioxidants, which could theoretically be repurposed for AMD. Metformin was identified as the drug with the strongest association with wet AMD genes and is among the top candidates in all dry AMD subtypes. Curcumin, statins, and antioxidants are also among the top drugs correlating with AMD-risk genes.

Conclusions

We use a systematic computational process to discover potential therapeutic targets for AMD. Our systematic and unbiased approach can be used to guide targeted preclinical/clinical studies for AMD and other ocular diseases.

Translational Relevance

Advanced bioinformatics models identify novel chemicals and approved drug candidates that can be efficacious for different subtypes of AMD.

The Invasion and Metastasis of Colon Adenocarcinoma (COAD) Induced by SALL4

Objective

The development and progression of many cancers may be related to SALL4, the role and molecular mechanism of which are unclear in colon adenocarcinoma (COAD).

Methods

The SALL4 expression in adjacent normal mucosa tissues and carcinoma tissues of patients with COAD was detected through bioinformatic analysis based on TCGA database and immunohistochemistry. Single-cell analysis showed that the expression of SALL4 in normal tissue was noticeably low. GSEA analysis suggested that the SALL4 upregulated the GO and pathway of growth and cancer development and downregulated metabolization pathway. The relationship between lymph node metastasis, histological grading, clinical staging, and the expression of SALL4 in carcinoma tissues was analyzed. The upregulated or downregulated SALL4 expression of COAD cell lines was established. The influence of SALL4 on COAD cells invasion and proliferation was detected using plate cloning assay and Transwell. The expressions of EMT-related proteins E-cadherin, N-cadherin, vimentin, and Twist were detected by Western blot. The EMT phenotype was analyzed by immunofluorescence.

Results

The study confirmed that the expression of SALL4 was upregulated in COAD and positively correlated with the degree of tumor differentiation, tumor staging, and metastasis. The overexpression of SALL4 was related to a poor prognosis, promoted the invasion and proliferation of colorectal cancer cells, and accelerated the occurrence of EMT, which was characterized by upregulation of Twist, vimentin, and N-cadherin expressions and downregulation of E-cadherin. The immunofluorescence staining confirmed the EMT phenotype. On the contrary, knocking out SALL4 gene reversed EMT, weakened cell proliferation and invasion, inhibited upregulation of Twist, vimentin, and N-cadherin expressions and downregulation of E-cadherin.

Conclusion

To sum up, TNM grading, histological grading, and lymphatic metastasis were significantly correlated with SALL4 in tumor tissues. SALL4 played a vital role in tumor proliferation, invasion, and tumor EMT and may be a novel target for COAD.

MicroRNA-363-3p, negatively regulated by long non-coding RNA small nucleolar RNA host gene 5, inhibits tumor progression by targeting Aurora kinase A in colorectal cancer

MicroRNA-363-3p (miR-363-3p), reportedly, exhibits a tumor-suppressive role in human malignancies. Herein, our research was designed to further explain the functions and molecular mechanisms of miR-363-3p in the progression of colorectal cancer (CRC). With in vitro models, this study found that miR-363-3p was markedly under-expressed in CRC tissues and cells, and its overexpression suppressed the viability, migration, and invasion of CRC cells, and promoted cell apoptosis, whereas inhibiting miR-363-3p expression exhibited an opposite role. Additionally, aurora kinase A (AURKA), capable of counteracting the impacts of miR-363-3p on malignant biological behaviors of CRC cells, was identified as a direct target of miR-363-3p. Besides, miR-363-3p was sponged by long non-coding RNA small nucleolar RNA host gene 5 (SNHG5), which suppressed miR-363-3p expression. This research shows that SNHG5/miR-363-3p/AURKA axis partakes in CRC progression.

Application of Prognostic Models Based on Psoas Muscle Index, Stage, Pathological Grade, and Preoperative Carcinoembryonic Antigen Level in Stage II-III Colorectal Cancer Patients Undergoing Adjuvant Chemotherapy

Objective

To investigate the effect of sarcopenia on the prognosis of stage II-III colorectal cancer patients undergoing adjuvant chemotherapy.

Methods

A total of 196 stage II-III colorectal cancer patients who received 8 cycles of postoperative chemotherapy were retrospectively analyzed. An abdominal CT acquired at 3-4 weeks after surgery was used to calculate the psoas muscle index. Subsequently, once gender-specific receiver operating characteristic curves were plotted and cut-off values of psoas muscle index were defined, the clinicopathological characteristics and the prognosis of patients with high and low values were compared. Lastly, prognostic models were established based on the independent prognostic factors of relapse-free survival and overall survival identified by COX analysis.

Results

Based on the psoas muscle index, the prevalence of sarcopenia was 37.5% among 196 patients. This prevalence has significant correlation with patients' age and gender. However, it was not related to the AJCC stage, T stage, lymph node metastasis, pathological grade, grade III-IV myelosuppression, or preoperative carcinoembryonic antigen level. In addition, both the relapse-free and the overall survival of patients with low and high psoas muscle indexes were significantly different. COX analysis indicated that the psoas muscle index was an independent prognostic factor. Both the overall survival prognostic model based on patients' psoas muscle index, stage, pathological grade, and preoperative carcinoembryonic antigen level and the relapse-free survival prognostic model based on patients' psoas muscle index, pathological grade, and preoperative carcinoembryonic antigen level could accurately predict the prognosis of patients.

Conclusion

For stage II-III colorectal cancer patients, the presence of sarcopenia before adjuvant chemotherapy would adversely affect their recurrence-free and overall survival. Prognostic models based on psoas muscle index, stage, pathological grade, and preoperative carcinoembryonic antigen level could accurately predict the prognosis in these patients.

Colon Cancer Progression Is Reflected to Monotonic Differentiation in Gene Expression and Pathway Deregulation Facilitating Stage-specific Drug Repurposing

BACKGROUND/AIM

Colon cancer is one of the most common cancer types and the second leading cause of death due to cancer. Many efforts have been performed towards the investigation of molecular alterations during colon cancer progression. However, the identification of stage-specific molecular markers remains a challenge. The aim of this study was to develop a novel computational methodology for the analysis of alterations in differential gene expression and pathway deregulation across colon cancer stages in order to reveal stage-specific biomarkers and reinforce drug repurposing investigation.

MATERIALS AND METHODS

Transcriptomic datasets of colon cancer were used to identify (a) differentially expressed genes with monotonicity in their fold changes (MEGs) and (b) perturbed pathways with ascending monotonic enrichment (MEPs) related to the number of the participating differentially expressed genes (DEGs), across the four colon cancer stages. Through an in silico drug repurposing pipeline we identified drugs that regulate the expression of MEGs and also target the resulting MEPs.

RESULTS

Our methodology highlighted 15 MEGs and 32 candidate repurposed drugs that affect their expression. We also found 51 MEPs divided into two groups according to their rate of DEG content alteration across colon cancer stages. Focusing on the target MEPs of the highlighted repurposed drugs, we found that one of them, the neuroactive ligand-receptor interaction, was targeted by the majority of the candidate drugs. Moreover, we observed that two of the drugs (PIK-75 and troglitazone) target the majority of the resulting MEPs.

CONCLUSION

These findings highlight significant genes and pathways that can be used as stage-specific biomarkers and facilitate the discovery of new potential repurposed drugs for colon cancer. We expect that the computational methodology presented can be applied in a similar way to the analysis of any progressive disease.

miRNAs in the Expression Regulation of Dopamine-Related Genes and Proteins in Endometrial Cancer

Disruption of the dopaminergic system leads to many diseases, including cancer. Dopamine and its receptors are involved in the regulation of proliferation, cell death, invasion, and migration. Better understanding of the mechanisms involved in these processes could reveal new molecular markers and therapeutic targets. The aim of this study was to determine the expression profile of dopamine-related genes and proteins in endometrial cancer and to assess whether miRNAs are involved in its regulation. Sixty women were recruited for the study: 30 with endometrial cancer and 30 without cancer. The expression profiles of dopamine-related genes were determined in endometrial tissue samples using microarrays and qRT-PCR. Then, protein concentration was determined with the ELISA test. In the last step, miRNA detection was performed using microarrays. The matching of miRNAs to the studied genes was carried out using the TargetScan tool. The analysis showed DRD2 and DRD3 overexpression, with a reduction in DRD5 expression, which could be due to miR-15a-5p, miR-141-3p, miR-4640-5p, and miR-221-5p activity. High levels of OPRK1 and CXCL12, related to the activity of miR-124-3p.1 and miR-135b-5p, have also been reported. Low COMT expression was probably not associated with miRNA regulation in endometrial cancer.

Identification and Characterization of Novel circRNAs Involved in Muscle Growth of Blunt Snout Bream (Megalobrama amblycephala)

Circular RNAs (circRNAs), a novel class of endogenous RNAs, have been recognized to play important roles in the growth of animals. However, the regulatory mechanism of circRNAs on fish muscle growth is still unclear. In this study, we performed whole transcriptome analysis of skeletal muscles from two populations with different growth rates (fast-growing and slow-growing) of blunt snout bream (Megalobrama amblycephala), an important fish species for aquaculture. The selected circRNAs were validated by qPCR and Sanger sequencing. Pairs of circRNA–miRNA–mRNA networks were constructed with the predicted differentially expressed (DE) pairs, which revealed regulatory roles in muscle myogenesis and hypertrophy. As a result, a total of 445 circRNAs were identified, including 42 DE circRNAs between fast-growing (FG) and slow-growing (SG) groups. Many of these DE circRNAs were related with aminoglycan biosynthetic and metabolic processes, cytokinetic processes, and the adherens junction pathway. The functional prediction results showed that novel_circ_0001608 and novel_circ_0002886, competing to bind with dre-miR-153b-5p and dre-miR-124-6-5p, might act as competing endogenous RNAs (ceRNAs) to control MamblycephalaGene14755 (pik3r1) and MamblycephalaGene10444 (apip) level, respectively, thus playing an important regulatory role in muscle growth. Overall, these results will not only help us to further understand the novel RNA transcripts in M. amblycephala, but also provide new clues to investigate the potential mechanism of circRNAs regulating fish growth and muscle development.

Increased expression of Cyclin F in liver cancer predicts poor prognosis: A study based on TCGA database

BACKGROUND

Cyclin F (CCNF) dysfunction has been implicated in various forms of cancer, offering a new avenue for understanding the pathogenic mechanisms underlying hepatocellular carcinoma (HCC). We aimed to evaluate the role of CCNF in HCC using publicly available data from The Cancer Genome Atlas (TCGA).

METHOD

We used TCGA data and Gene Expression Omnibus (GEO) data to analyze the differential expression of CCNF between tumor and adjacent tissues and the relationship between CCNF and clinical characteristics. We compared prognosis of patients with HCC with high and low CCNF expression and constructed receiver operating characteristic (ROC) curves. In addition, we also explored the types of gene mutations in relevant groups and conducted Gene Set Enrichment Analysis (GSEA).

RESULTS

The expression of CCNF in liver cancer tissues was significantly increased compared with that in adjacent tissues, and patients with high CCNF expression had a worse prognosis than those with low CCNF expression. Patients with high CCNF expression also had more somatic mutations. High expression of CCNF hampers the prognosis independently. The GSEA showed that the "http://www.gsea-msigdb.org/gsea/msigdb/cards/BIOCARTA_WNT_PATHWAY" Wnt pathway, "http://www.gsea-msigdb.org/gsea/msigdb/cards/BIOCARTA_P53_PATHWAY" P53 pathway, "http://www.gsea-msigdb.org/gsea/msigdb/cards/HALLMARK_PI3K_AKT_MTOR_SIGNALING" PI3K/Akt/mTOR pathway, "http://www.gsea-msigdb.org/gsea/msigdb/cards/HALLMARK_NOTCH_SIGNALING" Notch pathway were enriched in patients with the high CCNF expression phenotype.

CONCLUSION

High CCNF expression can be seen as an independent risk factor for poor survival in HCC. Its expression may serve as a target for the diagnosis and treatment of liver cancer.

Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas

Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.

Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach

Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC. We sought to discover the hub genes and identify their roles in several key pathways, including oxidative stress and apoptosis in the different stages of CRC. To identify the hub genes that may be involved in the different stages of CRC, gene expression datasets were obtained from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) common among the different datasets for each group were obtained using the robust rank aggregation method. Then, gene enrichment analysis was carried out with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, the protein-protein interaction networks were constructed using the Cytoscape software. We identified 40 hub genes and performed enrichment analysis for each group. We also used the Oncomine database to identify the DEGs related to stress-survival and apoptosis pathways involved in different stages of CRC. In conclusion, the hub genes were found to be enriched in several key pathways, including the cell cycle and p53 signaling pathway. Some of the hub genes were also reported in the stress-survival and apoptosis pathways. The hub DEGs revealed from our study may be used as biomarkers and may explain CRC development and progression mechanisms.

Comprehensive Analysis of Cyclin Family Gene Expression in Colon Cancer

Colon cancer is a common malignancy of the digestive tract with high morbidity and mortality. There is an urgent need to identify effective biomarkers for the early diagnosis of colon cancer and to prolong patient survival. Cyclins are a family of proteins that directly participate in the cell cycle and are associated with many types of tumors, but the role and regulatory mechanism of most cyclin family members in colon cancer remain unclear. Here, we provide a systematic and comprehensive study of cyclin family gene expression and their potential roles in colon cancer. Pan-cancer analysis revealed that cyclin genes were most differentially expressed in colon adenocarcinoma. Among the four datasets of colon cancer from The Cancer Genome Atlas and the Gene Expression Omnibus, six cyclin genes (CCNA2, CCNB1, CCND1, CCNE1, CCNF, and CCNJL) were differentially expressed between normal and tumor tissues. Four of them (CCNA2, CCNB1, CCNE1, and CCNF) were notably elevated in the early TNM stages and significantly correlated with overall survival. Meanwhile, the expression of CCNA2 and CCNB1 was positively correlated with tumor-killing immune cells, such as CD8+ T cells.The copy numbers of CCNA2, CCNB1, CCND1, CCNE1, and CCNF was positively related to gene expression. The methylation levels of CCNB1 were lower in tumor tissues than in normal tissues and were negatively correlated with gene expression. The receiver operating characteristic curves indicated that the gene expression of 24 cyclins had higher predictive accuracy than the TNM stage. Pathway analysis showed that cyclin genes were tightly associated with apoptosis, the cell cycle, hormone ER, the RAS/MAPK pathway, mismatch repair, mTORC1 signaling, KRAS signaling, Akt, and TGFB in colon cancer. Weighted gene co-expression network analysis suggested that cyclin genes were closely linked to CDK1, BIRC5, PLK1, and BCL2L12. At the protein level, Cyclin A2 and Cyclin B1 were also expressed higher in colon adenocarcinoma tissues. In addition, cyclin genes were highly related to the drug sensitivity of some FDA-approved drugs, such as MEK and EGFR inhibitors, which might provide guidance for clinical treatment. In conclusion, cyclin genes are promising biomarkers for the diagnosis and prognosis of colon cancer.

Identification of gene signatures and potential therapeutic targets for acquired chemotherapy resistance in gastric cancer patients

BACKGROUND

Gastric cancer (GC) is the most common type of gastrointestinal cancer, and has been studied extensively. However, resistance to chemotherapeutic agents has become a major problem, leading to treatment failure. This study aimed to investigate the molecular mechanisms mediating acquired resistance to cisplatin and fluorouracil (CF) combination-based chemotherapy in GC patients.

METHODS

The microarray datasets (GSE14209, GSE30070) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) using the limma package in R/Bioconductor. Possible targets of the DEMs were predicted using miRWalk, and the putative miRNA-mRNA regulatory network was constructed using Cytoscape software. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) analyses were then conducted and visualized using the Search Tool for Retrieval of Interacting Genes (STRING) and Cytoscape. The prognostic value of hub genes was revealed by Kaplan-Meier Plotter. The causal relationships and interactions between proteins were displayed using DisNor. Finally, similarity analysis was conducted using the Connectivity Map (CMap) profiles to predict a group of small molecules in GC treatment.

RESULTS

A total of 394 DEGs and 31 DEMs were identified after analysis of pre- and post-treatment samples of clinical responders to CF therapy. TM9SF4, hsa-miR-185-5p, and hsa-miR-145-5p were found to be critical in the miRNA-mRNA regulatory network. The DEGs were found to be mainly enriched in the processes of ribonucleoprotein complex assembly, catalytic activity acting on RNA, mitochondrial matrix, and thermogenesis. The DEMs were predominantly found to be involved in single-stranded RNA binding and endoplasmic reticulum lumen. HDAC5, DDX17, ILF3, and SDHC were identified as hub genes in the PPI network. Of these, HDAC5, DDX17, and ILF3 were found to be closely related to the overall survival of GC patients. DisNor identified the first neighbors of the key genes. Furthermore, CMap profiles predicted a group of small molecules, including several histone deacetylase inhibitors (HDACIs), menadione, and mibefradil, which could serve as promising therapeutic agents to reverse acquired resistance to CF therapy.

CONCLUSIONS

Our findings reveal new targets and alternative therapies to overcome the acquired resistance of GC patients to CF treatment.

Expression of Genomic Instability-Related Molecules: Cyclin F, RRM2 and SPDL1 and Their Prognostic Significance in Pancreatic Adenocarcinoma

In the present study, we aimed to assess the selected components of cell cycle machinery, checkpoint, DNA repair, and synthesis, namely RRM2, cyclin F, and SPDL1 in pancreatic adenocarcinomas (PAC) by in-house immunohistochemistry (IHC) and bioinformatic analysis of public datasets, in terms of expression, correlation with clinicopathological parameters, and patient survival. Sixty eight patients with pancreatic ductal adenocarcinoma (PDAC) were included in our cohort study, and IHC was performed on tissue macroarrays. RNA-Seq-based transcriptome data for 177 PACs were retrieved from the Cancer Genome Atlas (TCGA). We found cyclin F, RRM2, and SPDL1 to be overexpressed at both protein and mRNA levels in tumor tissues compared to respective controls. Based on TCGA dataset, we have demonstrated that CCNF, RRM2, and SPDL1 are potent independent prognostic markers for poor overall survival, both by themselves and even more in combination with each other. Furthermore, high CCNF mRNA expression was associated with features of cancer progression. By contrast, overexpression of cyclin F or SPDL1 proteins denoted a good prognosis in PDAC patients; however, in the case of the former protein, the results did not reach statistical significance. Specifically, high levels of SPDL1 protein emerged as the most powerful independent prognostic factor associated with a better outcome. If validated, the CCNF/RRM2/SPDL1 three-gene panel developed in this study, as well as SPDL1 protein, may provide significant clinical implications for the prognosis prediction of PAC patients.

Exonuclease 1 (EXO1) is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Lung Adenocarcinoma

Background

Exonuclease 1 (EXO1) has been identified to be highly expressed in different human malignancies, but its expression and prognostic role in lung adenocarcinoma (LUAD) remain unknown.

Materials and Methods

Two independent cohorts extracted from public databases and one cohort from our center were analyzed in this study. Expression levels of EXO1 in LUAD tissues and paired para-cancer tissues were detected. The prognostic value of EXO1 in LUAD patients was evaluated in the three cohorts. Enrichment analyses were performed to explore the possible underlying biological pathways. Moreover, we also explored the correlations between EXO1 and tumor-infiltrating immune cells and evaluated the impact of EXO1 knock-down on the migration of lung cancer cells.

Results

In this study, we found that EXO1 was highly expressed in LUAD tissues compared with para-cancerous tissues in public databases (p < 0.01), which was consistent with our data (p < 0.01). Survival analysis indicated that high expression of EXO1 was associated with poor prognosis in LUAD (p < 0.01). Enrichment analyses indicated that biological pathways like cell cycle regulation, DNA damage and repair, immune response, neuroactive ligand-receptor interaction, may be associated with EXO1 aberrant expression. Moreover, high expression of EXO1 was correlated with decreased infiltrating B cells (p < 0.01) and CD4+ T cells (p < 0.01) levels, and low infiltrating levels of B cells (p < 0.01) and dendritic cells (DCs) (p < 0.05) indicated poor overall survival (OS) in LUAD. Additionally, in vitro experiments suggested that knockdown of EXO1 may inhibit the migratory ability of lung cancer cells.

Conclusion

In conclusion, EXO1 is a potential prognostic biomarker in LUAD, and correlates with infiltrating levels of immune cells in the tumor microenvironment. Further prospective validation of EXO1 in lung cancer is warranted.

Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis

Colorectal cancer (CRC) is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients. In this study, mRNA microarray datasets GSE113513, GSE21510, GSE44076, and GSE32323 were obtained from the Gene Expression Omnibus (GEO) and analyzed with bioinformatics to identify hub genes in CRC development. Differentially expressed genes (DEGs) were analyzed using the GEO2R tool. Gene ontology (GO) and KEGG analyses were performed through the DAVID database. STRING database and Cytoscape software were used to construct a protein-protein interaction (PPI) network and identify key modules and hub genes. Survival analyses of the DEGs were performed on GEPIA database. The Connectivity Map database was used to screen potential drugs. A total of 865 DEGs were identified, including 374 upregulated and 491 downregulated genes. These DEGs were mainly associated with metabolic pathways, pathways in cancer, cell cycle and so on. The PPI network was identified with 863 nodes and 5817 edges. Survival analysis revealed that HMMR, PAICS, ETFDH, and SCG2 were significantly associated with overall survival of CRC patients. And blebbistatin and sulconazole were identified as candidate drugs. In conclusion, our study found four hub genes involved in CRC, which may provide novel potential biomarkers for CRC prognosis, and two potential candidate drugs for CRC.

Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

EXO1 Plays a Carcinogenic Role in Hepatocellular Carcinoma and is related to the regulation of FOXP3

Exonuclease 1 (EXO1), a member of the RAD2 nuclease family, was first described as possessing 5' to 3' nuclease activity and 5' structure-specific endonuclease activity. Here, we show that EXO1 is significantly upregulated in HCC tumor tissues and that high EXO1 expression is significantly correlated with liver cirrhosis. We further demonstrate that EXO1 knockdown decreases proliferation and colony forming abilities of HCC cells in vitro and tumorigenicity in vivo, as well as decreases migration and invasive capabilities of HCC cells. Alternatively, EXO1 overexpression significantly increases the proliferation, colony forming ability, and migration and invasive capabilities of HCC cells in vitro. Additionally, we truncated a region upstream of the transcription start site (TSS) of EXO1 and used the region with the strongest transcriptional activity to predict that the transcription factor FOXP3 can bind to the EXO1 promoter. Bioinformatics analysis found that FOXP3 was positively correlated with EXO1 and luciferase reporter assays and RT-PCR confirmed that FOXP3 could enhance the transcriptional activity of EXO1. CCK-8 assays showed that depletion of FOXP3 further reduces cell proliferation ability after knocking down of EXO1 in vitro. Taken together, our findings indicate that EXO1 acts as an oncogene in HCC and its expression level is related to FOXP3 activity.

Identification and Verification of Core Genes in Colorectal Cancer

Colorectal cancer, a malignant neoplasm that occurs in the colorectal mucosa, is one of the most common types of gastrointestinal cancer. Colorectal cancer has been studied extensively, but the molecular mechanisms of this malignancy have not been characterized. This study identified and verified core genes associated with colorectal cancer using integrated bioinformatics analysis. Three gene expression profiles (GSE15781, GSE110223, and GSE110224) were downloaded from the Gene Expression Omnibus (GEO) databases. A total of 87 common differentially expressed genes (DEGs) among GSE15781, GSE110223, and GSE110224 were identified, including 19 upregulated genes and 68 downregulated genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for common DEGs using clusterProfiler. These common DEGs were significantly involved in cancer-associated functions and signaling pathways. Then, we constructed protein-protein interaction networks of these common DEGs using Cytoscape software, which resulted in the identification of the following 10 core genes: SST, PYY, CXCL1, CXCL8, CXCL3, ZG16, AQP8, CLCA4, MS4A12, and GUCA2A. Analysis using qRT-PCR has shown that SST, CXCL8, and MS4A12 were significant differentially expressed between colorectal cancer tissues and normal colorectal tissues (P < 0.05). Gene Expression Profiling Interactive Analysis (GEPIA) overall survival (OS) has shown that low expressions of AQP8, ZG16, CXCL3, and CXCL8 may predict poor survival outcome in colorectal cancer. In conclusion, the core genes identified in this study contributed to the understanding of the molecular mechanisms involved in colorectal cancer development and may be targets for early diagnosis, prevention, and treatment of colorectal cancer.

Identification and Verification of the Main Differentially Expressed Proteins in Gastric Cancer via iTRAQ Combined with Liquid Chromatography-Mass Spectrometry

Background

To find the potential intersections between the differentially expressed proteins and abnormally expressed genes in gastric cancer (GC) patients.

Methods

Gastric cancer tissue and adjacent normal mucosa tissue were used for iTRAQ analysis. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) analysis were used to evaluate gene function. Western blotting and immunohistochemistry (IHC) were applied to verify the protein expression.

Results

A total of 2770 proteins were identified, of which 147 proteins were upregulated and 159 proteins were downregulated. GO analysis revealed that the differentially expressed genes were mainly enriched for the terms “cellular process,” “binding,” and “cell.” The results of the KEGG analysis showed that the most abundantly enriched proteins were involved in the “focal adhesion” pathway. The results of the PPI analysis showed that VCAM1 was located at the center of the PPI network. Western blotting and IHC analysis demonstrated that VCAM1, FLNA, VASP, CAV1, PICK1, and COL4A2 were differentially expressed in GC and adjacent normal tissues, which was consistent with the results of the iTRAQ analysis.

Conclusion

In conclusion, 6 highly differentially expressed proteins were identified as novel differentially expressed proteins in human GC. This exploratory research may provide useful information for the treatment of gastric cancer in the clinic.

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.