G-Protein Signaling Modulator 2 as a Potential Biomarker in Colorectal Cancer: Integrative Analysis Using Genetic Profiling and Pan-Cancer Studies.

Colorectal cancer (CRC) imposes a significant healthcare burden globally, prompting the quest for innovative biomarkers to enhance diagnostic and therapeutic strategies. This study investigates the G-protein signaling modulator (GPSM) family across several cancers and presents a comprehensive pan-cancer analysis of the GPSM2 gene across several gastrointestinal (GI) cancers. Leveraging bioinformatics methodologies, we investigated GPSM2 expression patterns, protein interactions, functional enrichments, prognostic implications, genetic alterations, and immune infiltration associations. Furthermore, the expression of the GPSM2 gene was analyzed using real-time analysis. Our findings reveal a consistent upregulation of GPSM2 expression in all GI cancer datasets analyzed, suggesting its potential as a universal biomarker in GI cancers. Functional enrichment analysis underscores the involvement of GPSM2 in vital pathways, indicating its role in tumor progression. The prognostic assessment indicates that elevated GPSM2 expression correlates with adverse overall and disease-free survival outcomes across multiple GI cancer types. Genetic alteration analysis highlights the prevalence of mutations, particularly missense mutations, in GPSM2. Furthermore, significant correlations between GPSM2 expression and immune cell infiltration are observed, suggesting its involvement in tumor immune evasion mechanisms. Collectively, our study underscores the multifaceted role of GPSM2 in GI cancers, particularly in CRC, emphasizing its potential as a promising biomarker for prognosis and therapeutic targeting. Further functional investigations are warranted to elucidate its clinical utility and therapeutic implications in CRC management.

The prognostic, diagnostic, and therapeutic impact of Long noncoding RNAs in gastric cancer.

Gastric cancer (GC), ranking as the third deadliest cancer globally, faces challenges of late diagnosis and limited treatment efficacy. Long non-coding RNAs (lncRNAs) emerge as valuable treasured targets for cancer prognosis, diagnosis, and therapy, given their high specificity, convenient non-invasive detection in body fluids, and crucial roles in diverse physiological and pathological processes. Research indicates the significant involvement of lncRNAs in various aspects of GC pathogenesis, including initiation, metastasis, and recurrence, underscoring their potential as novel diagnostic and prognostic biomarkers, as well as therapeutic targets for GC. Despite existing challenges in the clinical application of lncRNAs in GC, the evolving landscape of lncRNA molecular biology holds promise for advancing the survival and treatment outcomes of gastric cancer patients. This review provides insights into recent studies on lncRNAs in gastric cancer, elucidating their molecular mechanisms and exploring the potential clinical applications in GC.

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach.

Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan-Meier analysis. The STRING database was used to construct a protein-protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants-the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1-as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes-ASPHD1 and ZBTB12-and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.

Targeting transforming growth factor beta (TGF-ß) using Pirfenidone, a potential repurposing therapeutic strategy in colorectal cancer.

The modulating factors within the tumor microenvironment, for example, transforming growth factor beta (TGF-ß), may limit the response to chemo and immunotherapy protocols in colorectal cancer (CRC). In the current study, the therapeutic potential of targeting the TGF-ß pathway using Pirfenidone (PFD), a TGF-ß inhibitor, either alone or in combination with five fluorouracil (5-FU) has been explored in preclinical models of CRC. The anti-proliferative and migratory effects of PFD were assessed by MTT and wound-healing assays respectively. Xenograft models were used to study the anti-tumor activity, histopathological, and side effects analysis. Targeting of TGF-ß resulted in suppression of cell proliferation and migration, associated with modulation of survivin and MMP9/E-cadherin. Moreover, the PFD inhibited TGF-ß induced tumor progression, fibrosis, and inflammatory response through perturbation of collagen and E-cadherin. Targeting the TGF-ß pathway using PFD may increase the anti-tumor effects of 5-FU and reduce tumor development, providing a new therapeutic approach to CRC treatment.

Therapeutic Potential of Targeting the Cytochrome P450 Enzymes Using Lopinavir/Ritonavir in Colorectal Cancer: A Study in Monolayers, Spheroids and In Vivo Models.

Cytochrome P450 (CYP450) enzyme has been shown to be expressed in colorectal cancer (CRC) and its dysregulation is linked to tumor progression and a poor prognosis. Here we investigated the therapeutic potential of targeting CYP450 using lopinavir/ritonavir in CRC. The integrative systems biology method and RNAseq were utilized to investigate the differential levels of genes associated with patients with colorectal cancer. The antiproliferative activity of lopinavir/ritonavir was evaluated in both monolayer and 3-dimensional (3D) models, followed by wound-healing assays. The effectiveness of targeting CYP450 was examined in a mouse model, followed by histopathological analysis, biochemical tests (MDA, SOD, thiol, and CAT), and RT-PCR. The data of dysregulation expressed genes (DEG) revealed 1268 upregulated and 1074 down-regulated genes in CRC. Among the top-score genes and dysregulated pathways, CYPs were detected and associated with poor prognosis of patients with CRC. Inhibition of CYP450 reduced cell proliferation via modulating survivin, Chop, CYP13a, and induction of cell death, as detected by AnnexinV/PI staining. This agent suppressed the migratory behaviors of cells by induction of E-cadherin. Moreover, lopinavir/ritonavir suppressed tumor growth and fibrosis, which correlated with a reduction in SOD/thiol levels and increased MDA levels. Our findings illustrated the therapeutic potential of targeting the CYP450 using lopinavir/ritonavir in colorectal cancer, supporting future investigations on this novel therapeutic approach for the treatment of CRC.

Prognostic Significance of Integrin Subunit Alpha 2 (ITGA2) and Role of Mechanical Cues in Resistance to Gemcitabine in Pancreatic Ductal Adenocarcinoma (PDAC).

INTRODUCTION: PDAC is an extremely aggressive tumor with a poor prognosis and remarkable therapeutic resistance. The dense extracellular matrix (ECM) which characterizes PDAC progression is considered a fundamental determinant of chemoresistance, with major contributions from mechanical factors. This study combined biomechanical and pharmacological approaches to evaluate the role of the cell-adhesion molecule ITGA2, a key regulator of ECM, in PDAC resistance to gemcitabine. METHODS: The prognostic value of ITGA2 was analysed in publicly available databases and tissue-microarrays of two cohorts of radically resected and metastatic patients treated with gemcitabine. PANC-1 and its gemcitabine-resistant clone (PANC-1R) were analysed by RNA-sequencing and label-free proteomics. The role of ITGA2 in migration, proliferation, and apoptosis was investigated using hydrogel-coated wells, siRNA-mediated knockdown and overexpression, while collagen-embedded spheroids assessed invasion and ECM remodeling. RESULTS: High ITGA2 expression correlated with shorter progression-free and overall survival, supporting its impact on prognosis and the lack of efficacy of gemcitabine treatment. These findings were corroborated by transcriptomic and proteomic analyses showing that ITGA2 was upregulated in the PANC-1R clone. The aggressive behavior of these cells was significantly reduced by ITGA2 silencing both in vitro and in vivo, while PANC-1 cells growing under conditions resembling PDAC stiffness acquired resistance to gemcitabine, associated to increased ITGA2 expression. Collagen-embedded spheroids of PANC-1R showed a significant matrix remodeling and spreading potential via increased expression of CXCR4 and MMP2. Additionally, overexpression of ITGA2 in MiaPaCa-2 cells triggered gemcitabine resistance and increased proliferation, both in vitro and in vivo, associated to upregulation of phospho-AKT. CONCLUSIONS: ITGA2 emerged as a new prognostic factor, highlighting the relevance of stroma mechanical properties as potential therapeutic targets to counteract gemcitabine resistance in PDAC.

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.

Association of a Genetic Variant in the Cyclin-Dependent Kinase Inhibitor 2B with Risk of Pancreatic Cancer.

Background: Pancreatic cancer (PC) is among the most aggressive tumors with a poor prognosis, indicating the need for the identification of a novel prognostic biomarker for risk stratifications. Recent genome-wide association studies have demonstrated common genetic variants in a region on chromosome 9p21 associated with an increased risk of different malignancies. Methods: In the present study, we explore the possible relationship between genetic variant, rs10811661, and gene expression of CDKN2B in 75 pancreatic cancer patients, and 188 healthy individuals. DNAs were extracted and genotyping and gene expression were performed by TaqMan real-time PCR and RT-PCR, respectively. Logistic regression was used to assess the association between risk and genotypes, while the significant prognostic variables in the univariate analysis were included in multivariate analyses. Results: The patients with PDAC had a higher frequency of a TT genotype for rs10811661 than the control group. Also, PDAC patients with dominant genetic model, (TT + TC), was associated with increased risk of developing PDAC (OR= 14.71, 95% CI [1.96-110.35], p= 0.009). Moreover, patients with CC genotype had a higher expression of CDKN2B, in comparison with TT genotype. Conclusion: Our findings demonstrated that CDKN2A/B was associated with the risk of developing PDAC, supporting further investigations in the larger and multicenter setting to validate the potential value of this gene as an emerging marker for PDAC.