Genes In Colon Cancer Research Articles

Unveiling an anoikis-related risk model and the role of RAD9A in colon cancer

ObjectiveColorectal cancer (CRC), specifically colon adenocarcinoma, is the third most prevalent and the second most lethal form of cancer. Anoikis is found to be specialized form of programmed cell death (PCD), which plays a pivotal role in tumor progression. This study aimed to investigate the role of the anoikis related genes (ARGs) in colon cancer. MethodsConsensus unsupervised clustering, differential expression analysis, tumor mutational burden analysis, and analysis of immune cell infiltration were utilized in the study. For the analysis of RNA sequences and clinical data of COAD patients, data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were obtained. A prognostic scoring system for overall survival (OS) prediction was developed using Cox regression and LASSO regression analysis. Furthermore, loss-of-function assay was utilized to explore the role of RAD9A played in the progression of colon cancer. ResultsThe prognostic value of a risk score composed of NTRK2, EPHA2, RAD9A, CDC25C, and SNAI1 genes was significant. Furthermore, these findings suggested potential mechanisms that may influence prognosis, supporting the development of individualized treatment plans and management of patient outcomes. Further experiments confirmed that RAD9A could promote proliferation and metastasis of colon cancer cells. These effects may be achieved by affecting the phosphorylation of AKT. ConclusionDifferences in survival time and the tumor immune microenvironment (TIME) were observed between two gene clusters associated with ARGs. In addition, a prognostic risk model was established and confirmed as an independent risk factor. Furthermore, our data indicated that RAD9A promoted tumorigenicityby activating AKT in colon cancer.

Identifying polyamine related biomarkers in diagnosis and treatment of ulcerative colitis by integrating bulk and single-cell sequencing data

Ulcerative colitis (UC) is a chronic inflammatory disorder of the colon, and its pathogenesis remains unclear. Polyamine metabolic enzymes play a crucial role in UC. In this study, we aimed to identify pivotal polyamine-related genes (PRGs) and explore the underlying mechanism between PRGs and the disease status and therapeutic response of UC. We analyzed mRNA-sequencing data and clinical information of UC patients from the GEO database and identified NNMT, PTGS2, TRIM22, TGM2, and PPARG as key PRGs associated with active UC using differential expression analysis and weighted gene co-expression network analysis (WCGNA). Receiver operator characteristic curve (ROC) analysis confirmed the accuracy of these key genes in UC and colitis-associated colon cancer (CAC) diagnosis, and we validated their relationship with therapeutic response in external verification sets. Additionally, single-cell analysis revealed that the key PRGs were specific to certain immune cell types, emphasizing the vital role of intestinal tissue stem cells in active UC. The results were validated in vitro and in vivo experiments, including the colitis mice model and CAC mice model. In conclusion, these key PRGs effectively predict the progression of UC patients and could serve as new pharmacological biomarkers for the therapeutic response of UC.

Predictive role of oxidative stress-related genes in colon cancer: a retrospective cohort study based on The Cancer Genome Atlas

PurposeThis study aimed to elucidate the predictive role of an oxidative stress-related genes (OSRGs) model in colon cancer.Materials and methodsFirst, OSRGs that were differentially expressed between tumor and normal tissues were identified using The Cancer Genome Atlas (TCGA)—(Colorectal Adenocarcinoma) COAD dataset. Then, Lasso COX regression was performed to develop an optimal prognostic model patients were stratified into high- and low-risk groups based on the expression patterns of these genes. The model's validity was confirmed through Kaplan–Meier survival curves and receiver operating characteristic curve (ROC) analysis. Additionally, enrichment analyses were performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to uncover underlying mechanisms.ResultsA totally of 115 differentially expressed OSRGs were identified within the TCGA cohort, with 17 significantly linked to overall survival. These 17 genes were used to formulate a prognostic model that differentiated patients into distinct risk groups, with the high-risk group demonstrating a notably inferior overall survival rate. The risk score, when integrated with clinical and pathological data, emerged as an independent prognostic indicator of colon cancer. Further analyses revealed that the disparity in prognostic outcomes between risk groups could be attributed to the reactive oxygen species pathway and the p53 signaling pathway.ConclusionA new prediction model was established based on OSRGs. CYP19A1, NOL3 and UCN were found to be highly expressed in tumor tissues and substantial clinical predictive significance. These findings offer new insights into the role of oxidative stress in colon cancer.

Identification of Key Prognostic Alternative Splicing Events of Costimulatory Molecule-Related Genes in Colon Cancer.

This study aimed to explore the key alternative splicing events in costimulatory molecule-related genes in colon cancer and to determine their correlation with prognosis. Gene expression RNA-sequencing data, clinical data, and SpliceSeq data of colon cancer were obtained from The Cancer Genome Atlas. Differentially expressed alternative splicing events in genes were identified, Followed by correlation analysis of genes corresponding to differentially expressed alternative splicing events with costimulatory molecule-related genes. Survival analysis was conducted using differentially expressed alternative splicing events in these genes and a prognostic model was constructed. Functional enrichment, proteinprotein interaction network, and splicing factor analyses were performed. In total, 6504 differentially expressed alternative splicing events in 3949 genes were identified between tumor and normal tissues. Correlation analysis revealed 3499 differentially expressed alternative splicing events in 2168 costimulatory molecule-related genes. Moreover, 328 differentially expressed alternative splicing events in 288 costimulatory molecule-related genes were associated with overall survival. The prognostic models constructed using these showed considerable power in predicting survival. The ubiquitin A-52 residue ribosomal protein fusion product 1 and ribosomal protein S9 were the hub nodes in the protein-protein interaction network. Furthermore, one splicing factor, splicing factor proline and glutamine-rich, was significantly associated with patient prognosis. Four splicing factor-alternative splicing pairs were obtained from four alternative splicing events in three genes: TBC1 domain family member 8 B, complement factor H, and mitochondrial fission 1. The identified differentially expressed alternative splicing events of costimulatory molecule-related genes may be used to predict patient prognosis and immunotherapy responses in colon cancer.

Identification and validation of the prognostic signature of a novel demethylation-related gene associated with the clinical features of colon cancer

BackgroundThe aim of this study was to construct a prognostic model of colon cancer based on demethylation-related genes. An in-depth understanding of the relationship between the set of demethylated genes and colon cancer not only assists in revealing the pathogenesis of colon cancer but also provides strong support for future therapeutic strategies and individualized medicine. MethodsData were obtained from the TCGA database and the GEO-GSE39582 cohort. A risk score model for demethylation-related genes was developed using univariate Cox regression analysis and LASSO regression analysis. The accuracy and reliability of the model were confirmed using K–M survival analysis and ROC curve analysis. Additionally, a nomogram was created by integrating the risk score and clinicopathological variables. Finally, the biological function of the RCOR2 gene was verified by performing qPCR, MTT, colony formation, Transwell, and subcutaneous tumor formation assays in nude mice. ResultsWe constructed a risk score model containing 30 demethylation-related genes for predicting the survival risk of patients with colon cancer. COAD patients were categorized into high-risk and low-risk groups, and Kaplan–Meier (KM) curve analysis revealed that the high-risk group was associated with a worse prognosis. Univariate and multivariate Cox regression analyses validated the risk score as an independent prognostic factor for COAD. We also analyzed the differences in the sensitivity to nine chemotherapeutic agents and small molecule targeted drugs between the high-risk and low-risk groups. Moreover, we performed experiments in COAD cell lines and nude mice to verify that RCOR2 was differentially expressed between tumor tissues and normal tissues and that high RCOR2 expression promoted a malignant phenotype of colon cancer. ConclusionThis study demonstrated the potential roles of demethylation-related genes in colon cancer by conducting a comprehensive analysis and constructing a risk score. These findings also highlight the ability of these genes to indicate patient prognosis and tumor immune microenvironment. Furthermore, this study provides a reliable predictive tool that can assist in guiding the treatment and management of colon cancer patients.

Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer.

In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.

Complement factor I knockdown inhibits colon cancer development by affecting Wnt/β-catenin/c-Myc signaling pathway and glycolysis.

Colon cancer (CC) occurrence and progression are considerably influenced by the tumor microenvironment. However, the exact underlying regulatory mechanisms remain unclear. To investigate immune infiltration-related differentially expressed genes (DEGs) in CC and specifically explored the role and potential molecular mechanisms of complement factor I (CFI). Immune infiltration-associated DEGs were screened for CC using bioinformatics. Quantitative reverse transcription polymerase chain reaction was used to examine hub DEGs expression in the CC cell lines. Stable CFI-knockdown HT29 and HCT116 cell lines were constructed, and the diverse roles of CFI in vitro were assessed using CCK-8, 5-ethynyl-2'-deoxyuridine, wound healing, and transwell assays. Hematoxylin and eosin staining and immunohistochemistry staining were employed to evaluate the influence of CFI on the tumorigenesis of CC xenograft models constructed using BALB/c male nude mice. Key proteins associated with glycolysis and the Wnt pathway were measured using western blotting. Six key immune infiltration-related DEGs were screened, among which the expression of CFI, complement factor B, lymphoid enhancer binding factor 1, and SRY-related high-mobility-group box 4 was upregulated, whereas that of fatty acid-binding protein 1, and bone morphogenic protein-2 was downregulated. Furthermore, CFI could be used as a diagnostic biomarker for CC. Functionally, CFI silencing inhibited CC cell proliferation, migration, invasion, and tumor growth. Mechanistically, CFI knockdown downregulated the expression of key glycolysis-related proteins (glucose transporter type 1, hexokinase 2, lactate dehydrogenase A, and pyruvate kinase M2) and the Wnt pathway-related proteins (β-catenin and c-Myc). Further investigation indicated that CFI knockdown inhibited glycolysis in CC by blocking the Wnt/β-catenin/c-Myc pathway. The findings of the present study demonstrate that CFI plays a crucial role in CC development by influencing glycolysis and the Wnt/β-catenin/c-Myc pathway, indicating that it could serve as a promising target for therapeutic intervention in CC.

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

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

Four centrosome-related genes to predict the prognosis and drug sensitivity of patients with colon cancer.

As the primary microtubule organizing center in animal cells, centrosome abnormalities are involved in human colon cancer. To explore the role of centrosome-related genes (CRGs) in colon cancer. CRGs were collected from public databases. Consensus clustering analysis was performed to separate the Cancer Genome Atlas cohort. Univariate Cox and least absolute shrinkage selection operator regression analyses were performed to identify candidate prognostic CRGs and construct a centrosome-related signature (CRS) to score colon cancer patients. A nomogram was developed to evaluate the CRS risk in colon cancer patients. An integrated bioinformatics analysis was conducted to explore the correlation between the CRS and tumor immune microenvironment and response to immunotherapy, chemotherapy, and targeted therapy. Single-cell transcriptome analysis was conducted to examine the immune cell landscape of core prognostic genes. A total of 726 CRGs were collected from public databases. A CRS was constructed, which consisted of the following four genes: TSC1, AXIN2, COPS7A, and MTUS1. Colon cancer patients with a high-risk signature had poor survival. Patients with a high-risk signature exhibited decreased levels of plasma cells and activated memory CD4+ T cells. Regarding treatment response, patients with a high-risk signature were resistant to immunotherapy, chemotherapy, and targeted therapy. COPS7A expression was relatively high in endothelial cells and fibroblasts. MTUS1 expression was high in endothelial cells, fibroblasts, and malignant cells. We constructed a centrosome-related prognostic signature that can accurately predict the prognosis of colon cancer patients, contributing to the development of individualized treatment for colon cancer.

NTRK3 exhibits a pro-oncogenic function in upper tract urothelial carcinomas.

Neurotrophic receptor tyrosine kinase 3 (NTRK3) has pleiotropic functions: it acts not only as an oncogene in breast and gastric cancers but also as a dependence receptor in tumor suppressor genes in colon cancer and neuroblastomas. However, the role of NTRK3 in upper tract urothelial carcinoma (UTUC) is not well documented. This study investigated the association between NTRK3 expression and outcomes in UTUC patients and validated the results in tests on UTUC cell lines. A total of 118 UTUC cancer tissue samples were examined to evaluate the expression of NTRK3. Survival curves were generated using Kaplan-Meier estimates, and Cox regression models were used for investigating survival outcomes. Higher NTRK3 expression was correlated with worse progression-free survival, cancer-specific survival, and overall survival. Moreover, the results of an Ingenuity Pathway Analysis suggested that NTRK3 may interact with the PI3K-AKT-mTOR signaling pathway to promote cancer. NTRK3 downregulation in BFTC909 cells through shRNA reduced cellular migration, invasion, and activity in the AKT-mTOR pathway. Furthermore, the overexpression of NTRK3 in UM-UC-14 cells promoted AKT-mTOR pathway activity, cellular migration, and cell invasion. From these observations, we concluded that NTRK3 may contribute to aggressive behaviors in UTUC by facilitating cell migration and invasion through its interaction with the AKT-mTOR pathway and the expression of NTRK3 is a potential predictor of clinical outcomes in cases of UTUC.

Abstract 7413: Establishing a database for the discovery and validation of survival-associated genes in colon cancer

Abstract Introduction: Unveiling the prognostic significance of genes as potential biomarkers in colon cancer (CRC) is crucial for the discovery of new therapeutic targets and the effective stratification of patients into clinically relevant groups. The primary objective of this study was to establish a transcriptomic cohort comprising clinical data from colon cancer specimens and to leverage this dataset to pinpoint genes with robust prognostic implications. Methods: Our approach involved a comprehensive search of the Gene Expression Omnibus (GEO) to identify datasets containing both clinical information and raw gene expression measurements for individual colon cancer specimens. Subsequently, we curated and integrated these datasets into a unified database, and linked into our Kaplan-Meier plotter. This integration enabled the identification of genes whose expression changes were associated with altered survival outcomes. The identification of the most influential genes in relation to overall survival in stage 2 and stage 3/4 tumors was accomplished through both uni- and multivariate Cox regression analyses. To mitigate the impact of multiple hypothesis testing, a false discovery rate was computed for each marker. Results: The database encompasses a total of 2,137 tumor samples derived from 17 distinct independent cohorts. Within this dataset, 1,343 samples are equipped with recorded relapse-free survival data, while 1,061 samples provide information on overall survival events along with the corresponding duration. In addition to survival-related metrics, the database encompasses a spectrum of supplementary clinical parameters such as gender, T (tumor size), N (lymph node involvement), M (metastasis), grade, stage, location, and microsatellite instability. The most significant genes associated with relapse-fee survival with FDR<1% in stage 2 CRC include CSF1R (HR=2.86), FLNA (HR=2.88), TPBG (HR=2.65), B2M (HR=2.62) and LOXL2 (HR=2.63). When ordered by the Cox regression p-value in stage 3 and 4 tumors, the five strongest genes were BHLHE40 (HR=2.6), COL4A2 (HR=2.02), TSC22D3 (HR=1.95), NPR3 (HR=1.91), and A2M (HR=1.86). Conclusions: In this study, we successfully developed a robust integrated database dedicated to colon cancer. By leveraging this resource, we pinpointed the most impactful prognostic biomarkers and potential therapy targets specifically within stage 2 and stage 3/4 patients. Beyond its current applications, this database holds promise for future endeavors, serving as a valuable tool for identifying and prioritizing additional promising biomarkers and therapeutic targets in other sub-cohorts of colon cancer. The entire database can be utilized at www.kmplot.com. Citation Format: Balazs Gyorffy. Establishing a database for the discovery and validation of survival-associated genes in colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7413.

Prognosis and immunotherapy response prediction based on M2 macrophage-related genes in colon cancer

BackgroundM2 macrophage were revealed to play a crucial role in immune evasion and immunotherapies. This study aims to explore the potential significance of M2 macrophage-related genes in colon adenocarcinoma (COAD) by analysizing the transcriptome data in a comprehensive way.MethodsWe collected RNA-sequencing (RNA-seq) data of COAD from The Cancer Genome Atlas (TCGA) and Gene Expression Ominibus (GEO) databases. We calculated the immune infiltration scores of every sample using CIBERSORT algorithm. Through weighted gene co-expression network analysis (WGCNA), we picked out M2 macrophage-related genes. With these genes we screened out prognosis related genes which were utilized to construct a signature to assess the prognosis of patients. To extend the potential application of the signature, we also calculated the correlations with immune infiltration. Finally, we applied techniques such as quantitative reverse transcription polymerase chain reaction (qRT–PCR) and immunoblotting (Western Blotting) to validate the RNF32 gene in cellular in vitro assays.ResultsSeven M2 macrophage-related genes signature was constructed, which was an excellent prognostic predictor in two independent groups. The high-risk group showed lower immune infiltration and poorer response to immunotherapies than those of the low-risk group. The cell vitro experiments showed that the expression level of RNF32 was upregulated in colon cancer cell lines compared with normal cell lines. Moreover, we found that RNF32 may promote the proliferation, migration and invasion of cancer cells in vitro by inhibiting apoptosis.ConclusionA novel M2 macrophage-related gene signature affects the prognosis and immune characteristics of colon cancer.

Exploring the Molecular Targets and Therapeutic Potential of Coptisine in Colon Cancer: A Network Pharmacology Approach.

Colon cancer is a frequent malignancy, and surgery is still the primary therapy for people with colon cancer. Other treatments, including radiation, chemotherapy, and biologic therapy, may be utilized as a supplement. Chemotherapy, a prominent treatment for colon cancer, has failed to provide positive outcomes. This necessitates the development of more effective and less harmful treatment drugs. Coptisine was discovered to inhibit the development of colon cancer cell line HCT-116 in vivo, decrease the growth of HCT-116 cells, and cause apoptosis in vitro in colon cancer. Coptisine (COP) has shown antitumor activity in colon cancer, but its molecular mechanism and its molecular targets have not been fully understood. In this study, the biological behavior was verified in vitro. The targets of Huanglian alkaloids on colon cancer were predicted, and the protein-protein interaction (PPI) network was constructed. The core targets of safranine for colon cancer were extracted and analyzed by GO and KEGG enrichment to identify the possible molecular mechanisms of safranine treatment. Western blot was used to detect the changes of related pathway proteins in colon cancer cells. The differential expression of hub genes in colon cancer was analyzed using the GEPIA2 website. The binding ability of safranine to the target was verified by molecular docking. Finally, the targets were preliminarily verified by q-PCR analysis. Coptisine can inhibit the survival, migration, and proliferation of colon cancer cells DLD1 and HCT-116. Based on network pharmacology, ninety-one targets for colon cancer were screened. ESR1, ALB, AR, CDK2, PARP1, HSP90AB1, IGF1R, CCNE1, and CDC42 were found in the top 10. Enrichment analysis showed that these targets were mainly related to pathways in cancer, FC γ R-mediated phagocytosis, prostate cancer, progesterone-mediated oocyte maturation, the oestrogen signal pathway, proteoglycan in cancer and the PI3K-Akt signal pathway. WB results showed that after the treatment of colon cancer DLD1 cells with coptisine, the expression of P-AKT and AKT decreased, that of its downstream protein Bcl-2 decreased, and that of BAX increased. Differential expression analysis of hub genes showed that CCNE1, CDK2, HSP90AB1, and CHEK2 were upregulated in colon cancer samples, and molecular docking showed that these targets had a good ability to bind to coptisine. After the treatment of colon cancer DLD1 cells with coptisine, q-PCR results showed that CCNE1 and HSP90AB1 were significantly downregulated, while CDK2 and CHEK2 had no significant changes. Coptisine may be a candidate drug for the treatment of colon cancer, and its therapeutic effect may be related to the cancer pathway and PI3K-Akt signalling pathway. CCNE1 and HSP90AB1 may be potential targets of coptisine in the treatment of colon cancer.

Characterization of RNA Processing Genes in Colon Cancer for Predicting Clinical Outcomes.

Colon cancer is associated with multiple levels of molecular heterogeneity. RNA processing converts primary transcriptional RNA to mature RNA, which drives tumourigenesis and its maintenance. The characterisation of RNA processing genes in colon cancer urgently needs to be elucidated. In this study, we obtained 1033 relevant samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to explore the heterogeneity of RNA processing phenotypes in colon cancer. Firstly, Unsupervised hierarchical cluster analysis detected 4 subtypes with specific clinical outcomes and biological features via analysis of 485 RNA processing genes. Next, we adopted the least absolute shrinkage and selection operator (LASSO) as well as Cox regression model with penalty to characterise RNA processing-related prognostic features. An RNA processing-related prognostic risk model based on 10 genes including FXR1, MFAP1, RBM17, SAGE1, SNRPA1, SRRM4, ADAD1, DDX52, ERI1, and EXOSC7 was identified finally. A composite prognostic nomogram was constructed by combining this feature with the remaining clinical variables including TNM, age, sex, and stage. Genetic variation, pathway activation, and immune heterogeneity with risk signatures were also analysed via bioinformatics methods. The outcomes indicated that the high-risk subgroup was associated with higher genomic instability, increased proliferative and cycle characteristics, decreased tumour killer CD8+ T cells and poorer clinical prognosis than the low-risk group. This prognostic classifier based on RNA-edited genes facilitates stratification of colon cancer into specific subgroups according to TNM and clinical outcomes, genetic variation, pathway activation, and immune heterogeneity. It can be used for diagnosis, classification and targeted treatment strategies comparable to current standards in precision medicine. It provides a rationale for elucidation of the role of RNA editing genes and their clinical significance in colon cancer as prognostic markers.

Transient receptor potential channels as predictive marker and potential indicator of chemoresistance in colon cancer.

Transient receptor potential (TRP) channels are strongly associated with colon cancer development and progression. This study leveraged a multivariate Cox regression model on publicly available datasets to construct a TRP channels-associated gene signature, with further validation of signature in real world samples from our hospital treated patient samples. Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curves were employed to evaluate this gene signature's predictive accuracy and robustness in both training and testing cohorts, respectively. Additionally, the study utilized the CIBERSORT algorithm and single-sample gene set enrichment analysis to explore the signature's immune infiltration landscape and underlying functional implications. The support vector machine algorithm was applied to evaluate the signature's potential in predicting chemotherapy outcomes. The findings unveiled a novel three TRP channels-related gene signature (MCOLN1, TRPM5, and TRPV4) in colon adenocarcinoma (COAD). The ROC and K-M survival curves in the training dataset (AUC = 0.761; p = 1.58e-05) and testing dataset (AUC = 0.699; p = 0.004) showed the signature's robust predictive capability for the overall survival of COAD patients. Analysis of the immune infiltration landscape associated with the signature revealed higher immune infiltration, especially an increased presence of M2 macrophages, in high-risk group patients compared to their low-risk counterparts. High-risk score patients also exhibited potential responsiveness to immune checkpoint inhibitor therapy, evident through increased CD86 and PD-1 expression profiles. Moreover, the TRPM5 gene within the signature was highly expressed in the chemoresistance group (p = 0.00095) and associated with poor prognosis (p = 0.036) in COAD patients, highlighting its role as a hub gene of chemoresistance. Ultimately, this signature emerged as an independent prognosis factor for COAD patients (p = 6.48e-06) and expression of model gene are validated by public data and real-world patients. Overall, this bioinformatics study provides valuable insights into the prognostic implications and potential chemotherapy resistance mechanisms associated with TRPs-related genes in colon cancer.

Bioinformatic Analyses and Integrated Machine Learning to Predict prognosis and therapeutic response Based on E3 Ligase-Related Genes in colon cancer.

Purpose: Colorectal cancer is the third most common cause of cancer death worldwide. We probed the correlations between E3 ubiquitin ligase (E3)-related genes (ERGs) and colon cancer prognosis and immune responses. Methods: Gene expression profiles and clinical data of patients with colon cancer were acquired from the TCGA, GTEx, GSE17537 and GSE29621 databases. ERGs were identified by coexpression analysis. WGCNA and differential expression analysis were subsequently conducted. Consensus clustering identified two molecular clusters. Differential analysis of the two clusters and Cox regression were then conducted. A prognostic model was constructed based on 10 machine learning algorithms and 92 algorithm combinations. The CIBERSORT, ssGSEA and TIMER algorithms were used to estimate immune infiltration. The OncoPredict algorithm and The Cancer Immunome Atlas (TCIA) predicted susceptibility to chemotherapeutic and targeted drugs and immunotherapy sensitivity. CCK-8, scratch-wound and RT‒PCR assays were subsequently conducted. Results: Two ERG-associated clusters were identified. The prognosis and immune function of patients in cluster A were superior to those of patients in cluster B. We constructed a prognostic model with perfect predictive capability and validated it in internal and external colon cancer datasets. We discovered significant discrepancies in immune infiltration and immune checkpoints between different risk groups. The group with high-risk had a reduced half-maximal inhibitory concentration (IC50) for some routine antitumor drugs and reduced susceptibility to immunotherapy. In vitro experiments demonstrated that the ectopic expression of PRELP inhibited the migration and proliferation of CRC cells. Conclusions: In summary, we identified novel molecular subtypes and developed a prognostic model, which will help a lot in the advancement of better forecasting and therapeutic approaches.

DNA Methylation Status of the RETN and ADIPOQ Genes in Sporadic Colon Cancer

DNA Methylation Status of the RETN and ADIPOQ Genes in Sporadic Colon Cancer

Apigenin exerts anti-cancer effects in colon cancer by targeting HSP90AA1

Apigenin, a flavonoid, has shown early promise in colon cancer (CC); thus, exploring potential mechanisms of Apigenin is obligatory. In this study, shared targets of Apigenin and CC were identified through online tools, which were then subjected to functional enrichment analyses, Gene Ontology and KEGG. Further, the protein-protein interaction network of the shared targets was developed (via STRING). The top targets of Apigenin in CC were identified by molecular docking; further investigated for differential gene and protein expression in CC and their influence on CC patient survival (using TCGA data). Out of 13 hub genes, the top 3 targets (HSP90AA1, MMP9, PTGS2) were selected based on docking score. Their expression was significantly elevated and related to poor overall survival in CC (except PTGS2). Molecular dynamics simulation further validated protein-ligand interactions and divulged HSP90AA1 as the best target of Apigenin in CC. Finally, the anti-cancer effects of Apigenin and its major metabolite, luteolin, were investigated in CC, which is involved in the cytotoxicity of CC cells (COLO-205) by reducing HSP90AA1 expression revealed by real-time PCR. Thus, HSP90AA1 was identified as one of the prime targets of Apigenin in CC, and Apigenin could be effective against CC. Communicated by Ramaswamy H. Sarma

Investigating the Diagnostic and Therapeutic Potential of SREBF2-Related Lipid Metabolism Genes in Colon Cancer.

Colon cancer is one of the leading causes of death worldwide, and screening of effective molecular markers for the diagnosis is prioritised for prevention and treatment. This study aimed to investigate the diagnostic and predictive potential of genes related to the lipid metabolism pathway, regulated by a protein called sterol-regulatory element-binding transcription Factor 2 (SREBF2), for colon cancer and patient outcomes. We used machine-learning algorithms to identify key genes associated with SREBF2 in colon cancer based on a public database. A nomogram was created to assess the diagnostic value of these genes and validated in the Cancer Genome Atlas. We also analysed the relationship between these genes and the immune microenvironment of colon tumours, as well as the correlation between gene expression and clinicopathological characteristics and prognosis in the China Medical University (CMU) clinical cohort. Three genes, 7-dehydrocholesterol reductase (DHCR7), hydroxysteroid 11-beta dehydrogenase 2 (HSD11B2), and Ral guanine nucleotide dissociation stimulator-like 1 (RGL1), were identified as hub genes related to SREBF2 and colon cancer. Using the TCGA dataset, receiver operating characteristic curve analysis showed the area under the curve values of 0.943, 0.976, and 0.868 for DHCR7, HSD11B2, and RGL1, respectively. In the CMU cohort, SREBF2 and DHCR7 expression levels were correlated with TNM stage and tumour invasion depth (P < 0.05), and high DHCR7 expression was related to poor prognosis of colon cancer (P < 0.05). Furthermore, DHCR7 gene expression was positively correlated with the abundance of M0 and M1 macrophages and inversely correlated with the abundance of M2 macrophages, suggesting that the immune microenvironment may play a role in colon cancer surveillance. There was a correlation between SREBF2 and DHCR7 expression across cancers in the TCGA database. This study highlights the potential of DHCR7 as a diagnostic marker and therapeutic target for colon cancer.

Prognostic value of fatty acid metabolism-related genes in colorectal cancer and their potential implications for immunotherapy.

Colorectal cancer is one of the most common gastrointestinal cancers and the second leading cause of cancer-related death. Although colonoscopy screening has greatly improved the early diagnosis of colorectal cancer, its recurrence and metastasis are still significant problems. Tumour cells usually have the hallmark of metabolic reprogramming, while fatty acids play important roles in energy storage, cell membrane synthesis, and signal transduction. Many pathways of fatty acid metabolism (FAM) are involved in the occurrence and development of colon cancer, and the complex molecular interaction network contains a variety of genes encoding key enzymes and related products. Clinical information and RNA sequencing data were collected from TCGA and GEO databases. The prognosis model of colon cancer was constructed by LASSO-Cox regression analysis among the selected fatty acid metabolism genes with differential expression. Nomogram for the prognosis model was also constructed in order to analyze its value in evaluating the survival and clinical stage of the colon cancer patients. The differential expression of the selected genes was verified by qPCR and immunohistochemistry. GSEA and GSVA were used to analyze the enrichment pathways for high- and low-risk groups. CIBERSORT was used to analyze the immune microenvironment of colon cancer and to compare the infiltration of immune cells in the high- and low-risk groups. The "circlize" package was used to explore the correlation between the risk score signature and immunotherapy for colon cancer. We analysed the differential expression of 704 FAM-related genes between colon tumour and normal tissue and screened 10 genes with prognostic value. Subsequently, we constructed a prognostic model for colon cancer based on eight optimal FAM genes through LASSO Cox regression analysis in the TCGA-COAD dataset, and its practicality was validated in the GSE39582 dataset. Moreover, the risk score calculated based on the prognostic model was validated as an independent prognostic factor for colon cancer patients. We further constructed a nomogram composed of the risk score signature, age and American Joint Committee on Cancer (AJCC) stage for clinical application. The colon cancer cohort was divided into high- and low-risk groups according to the optimal cut-off value, and different enrichment pathways and immune microenvironments were depicted in the groups. Since the risk score signature was significantly correlated with the expression of immune checkpoint molecules, the prognostic model might be able to predict the immunotherapy response of colon cancer patients. In summary, our findings expand the prognostic value of FAM-related genes in colon cancer and provide evidence for their application in guiding immunotherapy.