Identification and Comprehensive Analysis of FREM2 Mutation as a Potential Prognostic Biomarker in Colorectal Cancer

Exploring extracellular matrix and prostaglandin pathway alterations across varying resection margin distances of right-sided colonic adenocarcinoma

BACKGROUND

Surgical resection followed by indicated adjuvant therapy offers potential curative treatment in colonic adenocarcinoma. Beyond the well-established seed and soil theory of colon cancer progression, the 'normal-appearing' tissues near the tumor are not genuinely normal and remain as remnants in patients following surgery. Our objective was to elucidate the alteration of gene expression and pathways across various distances of resection margins in right-sided colonic adenocarcinoma.

METHODS

Twenty-seven fresh samples of primary cancer and 56 matched non-tumor tissues adjacent to the tumor (NAT) were collected from patients with resectable right-sided colon cancer. NAT were systematically obtained at varying distances (1, 5, and 10 cm) on both proximal and distal sides. Comprehensive gene expression analysis was performed using 770-gene PanCancer Progression Panel, delineating distinctive pathways and functional predictions for each region.

RESULTS

Distinctive gene signatures and pathways exhibited by normal-appearing tissues were discovered at varying distances from cancer. Notably, SFRP2, PTGDS, COL1A1, IL1B, THBS2, PTGIS, COL1A2, NPR1, and BGN were upregulated, while ENPEP, MMP1, and NRCAM were downregulated significantly in 1-cm tissue compared to farther distances. Substantial alterations in the extracellular matrix (ECM) and prostaglandin/thromboxane synthesis were significantly evident at the 1-cm distance. Functional analysis indicated enhanced cell viability and survival, alongside reduced cellular death and apoptosis.

CONCLUSIONS

Different distances exerted a significant impact on gene alteration within the normal-looking mucosa surrounding primary cancer, influenced by various mechanisms. These findings may highlight potential therapeutic targets related to the ECM and prostaglandin/thromboxane pathways for treatment strategies.

A pyroptosis-related gene signature that predicts immune infiltration and prognosis in colon cancer

Background

Colon cancer (CC) is a highly heterogeneous malignancy associated with high morbidity and mortality. Pyroptosis is a type of programmed cell death characterized by an inflammatory response that can affect the tumor immune microenvironment and has potential prognostic and therapeutic value. The aim of this study was to evaluate the association between pyroptosis-related gene (PRG) expression and CC.

Methods

Based on the expression profiles of PRGs, we classified CC samples from The Cancer Gene Atlas and Gene Expression Omnibus databases into different clusters by unsupervised clustering analysis. The best prognostic signature was screened and established using least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. Subsequently, a nomogram was established based on multivariate COX regression analysis. Next, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential molecular mechanisms between the high- and low-risk groups and to explore the differences in clinicopathological characteristics, gene mutation characteristics, abundance of infiltrating immune cells, and immune microenvironment between the two groups. We also evaluated the association between common immune checkpoints and drug sensitivity using risk scores. The immunohistochemistry staining was utilized to confirm the expression of the selected genes in the prognostic model in CC.

Results

The 1163 CC samples were divided into two clusters (clusters A and B) based on the expression profiles of the 33 PRGs. Genes with prognostic value were screened from the DEGs between the two clusters, and an eight PRGs prognostic model was constructed. GSEA and GSVA of the high- and low-risk groups revealed that they were mainly enriched in inflammatory response-related pathways. Compared to those in the low-risk group, patients in the high-risk group had worse overall survival, an immunosuppressive microenvironment, and worse sensitivity to immunotherapy and drug treatment.

Conclusion

Our findings provide a foundation for future research targeting pyroptosis and new insights into prognosis and immunotherapy from the perspective of pyroptosis in CC.

Exploring the prognostic function of TMB-related prognostic signature in patients with colon cancer

Tumor mutation burden (TMB) level is identified as a useful predictor in multiple tumors including colon adenocarcinoma (COAD). However, the function of TMB related genes has not been explored previously. In this study, we obtained patients' expression and clinical data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI). TMB genes were screened and subjected to differential expression analysis. Univariate Cox and LASSO analyses were utilized to construct the prognostic signature. The efficiency of the signature was tested by using a receiver operating characteristic (ROC) curve. A nomogram was further plotted to assess the overall survival (OS) time of patients with COAD. In addition, we compared the predictive performance of our signature with other four published signatures. Functional analyses indicated that patients in the low-risk group have obviously different enrichment of tumor related pathways and tumor infiltrating immune cells from that of high-risk patients. Our findings suggested that the ten genes' prognostic signature could exert undeniable prognostic functions in patients with COAD, which might provide significant clues for the development of personalized management of these patients.

Characterization of the basement membrane in kidney renal clear cell carcinoma to guide clinical therapy

Background

Renal cell carcinoma (RCC) is the most common kidney cancer in adults. According to the histological features, it could be divided into several subtypes, of which the most common one is kidney renal clear cell carcinoma (KIRC), which contributed to more than 90% of cases for RCC and usually ends with a dismal outcome. Previous studies suggested that basement membrane genes (BMGs) play a pivotal role in tumor development. However, the significance and prognostic value of BMGs in KIRC still wrap in the mist.

Methods

KIRC data were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A prognostic risk score (PRS) model based on BMGs was established using univariate and least absolute shrinkage and selection operator (LASSO) and the Cox regression analysis was performed for prognostic prediction. The Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, receiver operating characteristic (ROC) curves, nomogram, and calibration curves were utilized to evaluate and validate the PRS model. All KIRC cases were divided into the high-risk score (HRS) group and the low-risk score (LRS) group according to the median risk scores. In addition, single-sample gene set enrichment analysis (ssGSEA), immune analysis, tumor microenvironment (TME) analysis, principal component analysis (PCA), and half-maximal inhibitory concentration (IC50) were also applied. Expression levels of BMGs were confirmed by qRT-PCR in both human renal cancer cell lines and tissues.

Results

We established the BMGs-based prognostic model according to the following steps. Within the TCGA cohort, patients’ prognosis of the HRS group was significantly worse than that of the LRS group, which was consistent with the analysis results of the GEO cohort. PCA patterns were significantly distinct for LRS and HRS groups and pathological features of the HRS group were more malignant compared with the LRS group. Correlation analysis of the PRS model and TME features, such as immune cell scores, stromal cell scores, and ESTIMATE values, revealed a higher immune infiltration in the HRS group compared with the LRS group. The chemotherapeutic response was also evaluated in KIRC treatment. It showed that the HRS group exhibited stronger chemoresistance to chemotherapeutics like FR-180204, GSK1904529A, KIN001-102, and YM201636. The therapeutic reactivity of the other 27 chemotherapeutic agents was summarized as well. Furthermore, the FREM2 level was measured in both human kidney tissues and associated cell lines, which suggested that lower FREM2 expression prompts a severer pathology and clinical ending.

Conclusions

Our study showed that KIRC is associated with a unique BMG expression pattern. The risk scores related to the expression levels of 10 BMGs were assessed by survival status, TME, pathological features, and chemotherapeutic resistance. All results suggested that FREM2 could be a potential candidate for KIRC prognosis prediction. In this study, we established a valid model and presented new therapeutic targets for the KIRC prognosis prediction as well as the clinical treatment recommendation, and finally, facilitated precision tumor therapy for every single individual.

Genomic Characterization by Whole-Exome Sequencing of Hypermobility Spectrum Disorder

No genetic basis is currently established that differentiates hypermobility spectrum disorders (HSD) from hypermobile Ehlers–Danlos syndrome (hEDS). Diagnosis is entirely based on clinical parameters with high overlap, leading to frequent misdiagnosis of these two phenotypes. This study presents a landscape of DNA mutations through whole-exome sequencing of patients clinically diagnosed with generalized HSD. In this study, three genes (MUC3A, RHBG, and ZNF717) were mutated in all five patients evaluated. The functional enrichment analysis on all 1162 mutated genes identified the extracellular matrix (ECM) structural constituent as the primary overrepresented molecular function. Ingenuity pathway analysis identified relevant bio-functions, such as the organization of ECM and hereditary connective tissue disorders. A comparison with the matrisome revealed 55 genes and highlighted MUC16 and FREM2. We also contrasted the list of mutated genes with those from a transcriptomic analysis on data from Gene Expression Omnibus, with only 0.5% of the genes at the intersection of both approaches supporting the hypothesis of two different diseases that inevitably share a common genetic background but are not the same. Potential biomarkers for HSD include the five genes presented. We conclude the study by describing five potential biomarkers and by highlighting the importance of genetic/genomic approaches that, combined with clinical data, may result in an accurate diagnosis and better treatment.

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer

Objective. Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods. Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results. Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa ( $p<0.05$ ). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions. The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.