Holistic exploration of CHGA and hsa-miR-137 in colorectal cancer via multi-omic data Integration.

Colorectal cancer (CRC) ranks among the most widespread malignancies globally, with early detection significantly influencing prognosis. Employing a systems biology approach, we aimed to unravel the intricate mRNA-miRNA network linked to CRC pathogenesis, potentially yielding diagnostic biomarkers. Through an integrative analysis of microarray, Bulk RNA-seq, and single-cell RNA-seq data, we explored CRC-related transcriptomes comprehensively. Differential gene expression analysis uncovered crucial genes, while Weighted Gene Co-expression Network Analysis (WGCNA) identified key modules closely linked to CRC. Remarkably, CRC manifested its strongest correlation with the turquoise module, signifying its pivotal role. From the cohort of genes showing high Gene Significance (GS) and Module Membership (MM), and Differential Expression Genes (DEGs), we highlighted the downregulated Chromogranin A (CHGA) as a notable hub gene in CRC. This finding was corroborated by the Human Protein Atlas database, which illustrated decreased CHGA expression in CRC tissues. Additionally, CHGA displayed elevated expression in primary versus metastatic cell lines, as evidenced by the CCLE database. Subsequent RT-qPCR validation substantiated the marked downregulation of CHGA in CRC tissues, reinforcing the significance of our differential expression analysis. Analyzing the Space-Time Gut Cell Atlas dataset underscored specific CHGA expression in epithelial cell subclusters, a trend persisting across developmental stages. Furthermore, our scrutiny of colon and small intestine Enteroendocrine cells uncovered distinct CHGA expression patterns, accentuating its role in CRC pathogenesis. Utilizing the WGCNA algorithm and TargetScan database, we validated the downregulation of hsa-miR-137 in CRC, and integrated assessment highlighted its interplay with CHGA. Our findings advocate hsa-miR-137 and CHGA as promising CRC biomarkers, offering valuable insights into diagnosis and prognosis. Despite proteomic analysis yielding no direct correlation, our multifaceted approach contributes comprehensive understanding of CRC's intricate regulatory mechanisms. In conclusion, this study advances hsa-miR-137 and CHGA as promising CRC biomarkers through an integrated analysis of diverse datasets and network interactions.

Experimental validation of in silico analysis estimated the reverse effect of upregulated hsa-miR-106a-5p and hsa-miR-223-3p on SLC4A4 gene expression in Iranian patients with colorectal adenocarcinoma by RT-qPCR.

BACKGROUND AND METHODS: Colorectal cancer (CRC) is considered one of the most common malignancies worldwide. The diagnosis and prognosis of the patients are very poor. In this study, we used in-silico analysis and experimental techniques to investigate novel co-expression genes and their associated miRNA networks in CRC. For this purpose, we conducted a comprehensive transcriptome analysis using online bulk and single-cell RNA-seq datasets. We then validated the results on tissue samples from cancerous and adjacent normal tissues from CRC patients by RT-qPCR. RESULTS: Using a weighted gene co-expression network algorithm, we identified SLC4A4 as a significantly downregulated hub gene in the CRC. The single-cell analysis indicated that the expression level of SLC4A4 in Paneth cells is higher than in other cell populations. Further computational analysis suggested hsa-miR-223-3p and hsa-miR-106a-5p as two specific hub-miRNAs for the SLC4A4 gene. RT-qPCR analysis showed a 2.60-fold downregulation of SLC4A4. Moreover, hsa-miR-223-3p and hsa-miR-106a-5p showed an increased expression level of 5.58-fold and 9.66-fold in CRC samples, respectively. Based on the marginal model analysis, by increasing the expression of hsa-miR-106a-5p, the average expression of the SLC4A4 gene significantly decreased by 103 units. Furthermore, ROC curves analysis indicated statistically significant for diagnostic ability of SLC4A4 (AUC: 0.94, Sensitivity: 95.5%, Specificity: 95.5%) and hsa-miR-106a-5p (AUC: 0.72, Sensitivity: 72.7%, Specificity: 100%). CONCLUSION: This study provides a framework of co-expression gene modules and miRNAs of CRC, which identifies some important biomarkers for CRC pathogenicity and diagnosis. Further experimental evidence will be required to support this study and validate the precise molecular pathways.

CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity.

Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA-miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine-learning algorithm was used to compute the gene importance in AA. Finally, drug-target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on GZMA, OXCT2, HOXC13, KRT40, COMP, CHAC1, and KRT83 hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that CHAC1 could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA-approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co-expression network. Using transcriptome analysis, suggested CHAC1 as a potential diagnostic predictor to diagnose AA.

Treatment with Hydroxyurea Leads to Fetal Hemoglobin Reactivation through CA1 and LIN28B Genes: An In Vitro Study.

Hydroxyurea (HU) is an effective drug to increase fetal γ-globin gene (Hb F) expression, replacing the missing adult ß-globin gene. The mechanism of Hb F induction by HU and improvement in clinical symptoms are still poorly understood. The current study aimed to improve the molecular understanding of drug-induced alterations and reveals genes related to HU treatment responsiveness in ß-thalassemia (ß-thal). We analyzed the GSE109186 dataset using system biology and weighted gene coexpression network analysis (WGCNA) to identify and quantify gene expression changes reflected in the HU-treated human erythroblastic leukemia cells. The K562 cell line was treated in 50, 100, and 150 µM concentrations of HU for 24, 48, and 72 hours with three replications. The alteration of CA1, LIN28B and Hb F gene expression in HU-treated cells was evaluated using the real-time polymerase chain (real-time PCR) technique. The results showed that LIN28B has an increase of 4.27-fold on the first day of HU-treatment in 50 µM (p < 0.01). The CA1 expression showed a decrease at all times and doses of treatment, and the most decrease happened in 48 hours and 50 µM (p < 0.04). Hb F also showed the highest increase in 100 µM after 24 hours of treatment (5.18-fold). In summary, the data suggest that alteration of LIN28B and CA1 gene expression is associated with γ-globin increasing in HU-treated cells.

Investigating the effect of quadruple therapy with Saccharomyces boulardii or Lactobacillus reuteri strain (DSMZ 17648) supplements on eradication of Helicobacter pylori and treatments adverse effects: a double-blind placebo-controlled randomized clinical trial.

BACKGROUND: The goal of this study was to investigate the effects of treatment with Saccharomyces boulardii and Lactobacillus reuteri on the eradication of Helicobacter pylori and Adverse effects (AEs) of the treatment. RESULTS: This study was a double-blind, randomized, placebo-controlled trial. And, eradication of H. pylori was reported comparing quadruple therapy include of PPI (proton pomp inhibitor), bismuth subcitrate, clarithromycin, and amoxicillin versus quadruple therapy supplemented with S. boulardii and L. reuteri DSMZ 17648. For this aim, a total of 156 patients were included in the current study; and patients positive for H. pylori infection (n = 156) were randomly assigned to 3 groups: 52 patients (Group P) received conventional quadruple therapy plus L. reuteri, 52 patients (Group S) received conventional quadruple therapy plus S. boulardii daily, for 2 weeks, and 52 patients were in the control group (Group C). At the end of the treatment period, all the subjects continued to take proton pump inhibitor (PPI) alone for 14 days, and then, no medication was given for 2 weeks again. During follow-up, gastrointestinal symptoms were assessed using an evaluation scale (Glasgow dyspepsia questionnaire [GDQ]), and AEs were assessed at 7, 14, 21, and 28 days. As a result, all patients completed the treatment protocol in all groups by the end of the study. Additionally, eradication therapy was effective for 94.2% of subjects in Group S, 92.3% of subjects in Group P, and 86.5% of subjects in the control group, with no differences between treatment arms. In Group S, the chance of developing symptoms of nausea (OR = 2.74), diarrhea (OR = 3.01), headache (OR = 10.51), abdominal pain (OR = 3.21), and anxiety (OR = 3.58) was significantly lower than in the control group (p < 0.05). CONCLUSION: S. boulardii could significantly reduce some AEs of H. pylori eradication therapy, but effectiveness of Lactobacillus reuteri on these cases was not significant. It is recommended to conduct the future research with larger sample size in order to investigate the effect. TRIAL REGISTRATION: IRCT20200106046021N1, this trial was registered on Jan 14, 2020.

Prediction and validation of GUCA2B as the hub-gene in colorectal cancer based on co-expression network analysis: In-silico and in-vivo study.

BACKGROUND: Several serious attempts to treat colorectal cancer have been made in recent decades. However, no effective treatment has yet been discovered due to the complexities of its etiology. METHODS: we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub-genes, and mRNA-miRNA regulatory networks associated with CRC. Next, enrichment analysis of modules has been performed using Cluepedia. Next, quantitative real-time PCR (RT-qPCR) was used to validate the expression of selected hub-genes in CRC tissues. RESULTS: Based on the WGCNA results, the brown module had a significant positive correlation (r = 0.98, p-value=9e-07) with CRC. Using the survival and DEGs analyses, 22 genes were identified as hub-genes. Next, three candidate hub-genes were selected for RT-qPCR validation, and 22 pairs of cancerous and non-cancerous tissues were collected from CRC patients referred to the Gastroenterology and Liver Clinic. The RT-qPCR results revealed that the expression of GUCA2B was significantly reduced in CRC tissues, which is consistent with the results of differential expression analysis. Finally, top miRNAs correlated with GUCA2B were identified, and ROC analyses revealed that GUCA2B has a high diagnostic performance for CRC. CONCLUSIONS: The current study discovered key modules and GUCA2B as a hub-gene associated with CRC, providing references to understand the pathogenesis and be considered a novel candidate to CRC target therapy.

ACAN, MDFI, and CHST1 as Candidate Genes in Gastric Cancer: A Comprehensive Insilco Analysis.

BACKGROUND: Gastric cancer (GC) is a complex disorder with an inadequate response to treatment. Although many efforts have been made to clarify the development of GC, the exact etiology and molecular mechanisms of this malignancy remain unclear. This study was designed to identify and characterize essential associated genes with GC to construct a prognostic model. METHODS: In this Insilco study, the gene expression microarray dataset GSE122401 was downloaded from the Gene Expression Omnibus (GEO). The raw data were processed and quantile-normalized with the edgeR package of R ver.3.5.3. The module-trait relationship and hub-genes associated with GC were analyzed with Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Cluepedia and Enrichr Database. Finally, hub-genes were screened and validated by GEPIA online database. RESULTS: According to the WGCNA results, the blue module was found to be strongly correlated with the GC (r=0.91, p-value=1e-57). DEGs analysis was performed by edgeR package of R and indicated a total of 47 genes as hub-genes. Verifying the hub-genes expression using GEPIA online database showed a significantly increased level of ACAN gene expression in primary cancer cell line compared to metastatic cell line. On the other hand, the expression of MDFI and CHST1 genes in primary cell lines were lower compared to metastatic cancer cell lines. CONCLUSIONS: This study provides a framework of the co-expression gene modules ACAN, MDFI, and CHST1 as hub-genes. These hub-genes might offer candidate biomarkers to targeted therapy against GC. Further experiment validation and animal models are needed to reveal the exact mechanism of the above-mentioned genes in the pathogenesis and prognoses of GC.

Exploiting systems biology to investigate the gene modules and drugs in ovarian cancer: A hypothesis based on the weighted gene co-expression network analysis.

BACKGROUND: Ovarian cancer (OC) is one of the worrisome gynecological cancers worldwide. Given its considerable mortality rate, it is necessary to investigate its oncogenesis. METHODS: In this study, we used systems biology approaches to describe the key gene modules, hub genes, and regulatory drugs associated with serous OC as the novel biomarkers using weighted gene co-expression network analysis (WGCNA). FINDINGS: Our findings have demonstrated that the blue module genes (r = 0.8, p-value = 1e-16) are involved in OC progression. Based on gene enrichment analysis, the genes in this module are frequently involved in biological processes such as the Cyclic adenosine monophosphate (cAMP) signaling pathway and the cellular response to transforming growth factor-beta stimulation. The co-expression network has been built using the correlated module's top hub genes, which are ADORA1, ANO9, CD24P4, CLDN3, CLDN7, ELF3, KLHL14, PRSS8, RASAL1, RIPK4, SERINC2, and WNT7A. Finally, a drug-target network has been built to show the interaction of the FDA-approved drugs with hub genes. CONCLUSIONS: Our results have discovered that ADORA1, ANO9, SERINC2, and KLHL14 are hub genes associated with serous OC. These genes can be considered as novel candidate target genes for treating OC.

Identification of early diagnostic biomarkers via WGCNA in gastric cancer.

BACKGROUND: Gastric cancer (GC) is the world's second-leading cause of cancer-related mortality, continuing to make it a serious healthcare concern. Even though the prevalence of GC reduces, the prognosis for GC patients remains poor in terms of a lack of reliable biomarkers to diagnose early GC and predict chemosensitivity and recurrence. METHODS AND MATERIAL: We integrated the gene expression patterns of gastric cancers from four RNAseq datasets (GSE113255, GSE142000, GSE118897, and GSE130823) from Gene Expression Omnibus (GEO) database to recognize differentially expressed genes (DEGs) between normal and GC samples. A gene co-expression network was built using weighted co-expression network analysis (WGCNA). Furthermore, RT-qPCR was performed to validate the in silico results. RESULTS: The red modules in GSE113255, Turquoise in GSE142000, Brown in GSE118897, and the green-yellow module in GSE130823 datasets were found to be highly correlated with the anatomical site of GC. ITGAX, CCL14, ADHFE1, and HOXB13) as the hub gene are differentially expressed in tumor and non-tumor gastric tissues in this study. RT-qPCR demonstrated a high level of the expression of this gene. CONCLUSION: The expression levels of ITGAX, CCL14, ADHFE1, and HOXB13 in GC tumor tissues are considerably greater than in adjacent normal tissues. Systems biology approaches identified that these genes could be possible GC marker genes, providing ideas for other experimental studies in the future.

A mini review of bisphenol A (BPA) effects on cancer-related cellular signaling pathways.

Bisphenol A (BPA) is a plasticizer used widely in many industrial products and is now well established as an endocrine-disrupting chemical (EDC). BPA readily leaches out from these products into the environment and into foodstuffs (from packaging materials) and human exposure can be considerable. Many studies have shown that BPA exposure is associated with a range of chronic human health conditions, including diabetes, cardiovascular disorders, polycystic ovarian disease, hepatotoxicity, and various types of cancer. BPA exerts its effects through deregulating cell signaling pathways associated with cell growth, proliferation, migration, invasion, and apoptosis. Previous studies on the molecular mechanisms of BPA have illustrated a variety of pathways impaired at very low exposure concentrations and that stimulate cellular responses relating to tumorigenesis both in cancer onset and progression. In this mini review, the recent advancements made through in vitro analyses are reported on for the effect of BPA on various cellular signaling pathways focusing on the signaling pathways that play a major role in carcinogenesis.