Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning.

BACKGROUND: Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC. METHODS: The data for this study were obtained from bulk, single-cell, and spatial transcriptomic sequencing data. Bioinformatics analysis, in vitro experiments, and machine learning methods were employed to investigate the functional characteristics of CAF subtypes and construct prognostic models. RESULTS: Our study demonstrates that Biglycan (BGN) positive cancer-associated fibroblasts (BGN + Fib) serve as a driver in colorectal cancer (CRC). The proportion of BGN + Fib increases gradually with the progression of CRC, and high infiltration of BGN + Fib is associated with poor prognosis in terms of overall survival (OS) and recurrence-free survival (RFS) in CRC. Downregulation of BGN expression in cancer-associated fibroblasts (CAFs) significantly reduces migration and proliferation of CRC cells. Among 101 combinations of 10 machine learning algorithms, the StepCox[both] + plsRcox combination was utilized to develop a BGN + Fib derived risk signature (BGNFRS). BGNFRS was identified as an independent adverse prognostic factor for CRC OS and RFS, outperforming 92 previously published risk signatures. A Nomogram model constructed based on BGNFRS and clinical-pathological features proved to be a valuable tool for predicting CRC prognosis. CONCLUSION: In summary, our study identified BGN + Fib as drivers of CRC, and the derived BGNFRS was effective in predicting the OS and RFS of CRC patients.

Integrated analysis of single cell and bulk RNA sequencing identifies CTHRC1+ INHBA+ CAF as drivers of colorectal cancer progression.

Cancer-associated fibroblasts (CAFs) are a key component of the tumor microenvironment and a critical factor in the progression of colorectal cancer (CRC). The aim of this study was to screen for CAFs specific genes that could serve as promising therapeutic targets for CRC patients. Our findings showed a significant increase in the proportion of fibroblasts in CRC tissues, and a high proportion of fibroblasts was associated with immune escape and poor prognosis in CRC. Collagen triple helix repeat containing 1 (CTHRC1) and inhibin subunit beta A (INHBA) were identified as key genes in the progression of CRC, primarily expressed in CAFs and significantly upregulated in CRC tissues. We defined CTHRC1 and INHBA as cancer-associated fibroblast-related genes (CAFRGs), which were associated with poor prognosis in CRC and macrophage polarization. CAFRGs promoted immune escape and metastasis in CRC and were good predictors of immune therapy response. Drug sensitivity analysis showed that the high expression group of CAFRGs was sensitive to 15 chemotherapy drugs, while the low expression group was sensitive to only 3. Clustering of fibroblasts in the tumor revealed that CTHRC1+ INHBA+ CAF was a poor prognostic factor in CRC and was associated with extracellular matrix remodeling and immune regulation. In conclusion, our study provides new theoretical basis for effective treatment strategies and therapeutic targets for CRC.

Selumetinib overcomes ITGA2-induced 5-fluorouracil resistance in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is the third most malignant tumor in the world. 5-fluorouracil (5­FU) -based chemotherapy is the first-line chemotherapy scheme for CRC, whereas acquired drug resistance poses a huge obstacle to curing CRC patients and the mechanism is still obscure. Therefore, identification of genes associated with 5­FU chemotherapy and seeking second-line treatment are necessary means to improve survival and prognosis of patients with CRC. METHODS: The Cancer Therapeutics Response Portal (CTRP) database and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to identify CRC-related genes and potential second-line therapies for 5-FU-resistant CRC. The single-cell RNA sequencing data for CRC tissues were obtained from a GEO dataset. The relationship between ITGA2 and 5-FU-resistant was investigated in vitro and in vivo models. RESULTS: ACOX1 and ITGA2 were identified as risk biomarkers associated with 5-FU-resistance. We developed a risk signature, consisting of ACOX1 and ITGA2, that was able to distinguish well between 5-FU-resistance and 5-FU-sensitive. The single-cell sequencing data showed that ITGA2 was mainly enriched in malignant cells. ITGA2 was negatively correlated with IC50 values of most small molecule inhibitors, of which selumetinib had the highest negative correlation. Finally, knocking down ITGA2 can make 5-FU-resistant CRC cells sensitive to 5-FU and combining with selumetinib can improve the therapeutic effect of 5-FU resistant cells. CONCLUSION: In summary, our findings demonstrated the critical role of ITGA2 in enhancing chemotherapy resistance in CRC cells and suggested that selumetinib can restore the sensitivity of chemotherapy-resistant CRC cells to 5-FU by inhibiting ITGA2 expression.

A novel Senescence-Based prognostic model unveils tumor interactions and drug resistance in colorectal cancer.

BACKGROUND: In China, CRC incidence is escalating. The main hurdles are heterogeneity and drug resistance. This research delves into cellular senescence in CRC, aiming to devise a prognostic model and pinpoint mechanisms impacting drug resistance. METHODS: Mendelian randomization (MR) analysis confirmed the association between CRC and cellular aging. The Cancer Genome Atlas (TCGA)-CRC data served as the training set, with GSE38832 and GSE39582 as validation sets. Various bioinformatics methods were employed to construct and validate a risk model. CRC cells with NADPH Oxidase 4 (NOX4) knockout were generated using CRISPR-Cas9 technology. Protein blotting and colony formation assays elucidated the role of NOX4 in CRC cell aging and drug resistance. RESULTS: A prognostic model, derived from dataset analysis, uncovered a link between high-risk groups and cancer progression. Notable differences in the tumor microenvironment were observed between risk groups. Finally, NOX4 was found to be linked with aging and drug resistance in CRC. CONCLUSION: This research presents a novel senescence-based CRC prognosis model. It identifies NOX4's role in CRC drug resistance, suggesting it is a potential treatment target.

Deciphering alternative splicing events and their therapeutic implications in colorectal Cancer.

Colorectal cancer (CRC) is one of the most common malignant tumors with complex molecular regulatory mechanisms. Alternative splicing (AS), a fundamental regulatory process of gene expression, plays an important role in the occurrence and development of CRC. This study analyzed AS Percent Spliced In (PSI) values from 49 pairs of CRC and normal samples in the TCGA SpliceSeq database. Using Lasso and SVM, AS features that can differentiate colorectal cancer from normal were screened. Univariate COX regression analysis identified prognosis-related AS events. A risk model was constructed and validated using machine learning, Kaplan-Meier analysis, and Decision Curve Analysis. The regulatory effect of protein arginine methyltransferase 5 (PRMT5) on poly(RC) binding protein 1 (PCBP1) was verified by immunoprecipitation experiments, and the effect of PCBP1 on the AS of Obscurin (OBSCN) was verified by PCR. Five AS events, including HNF4A.59461.AP and HNF4A.59462.AP, were identified, which can distinguish CRC from normal tissue. A machine learning model using 21 key AS events accurately predicted CRC prognosis. High-risk patients had significantly shorter survival times. PRMT5 was found to regulate PCBP1 function and then influence OBSCN AS, which may drive CRC progression. The study concluded that some AS events is significantly different in CRC and normal tissues, and some of these AS events are related to the prognosis of CRC. In addition, PRMT family-driven arginine modifications play an important role in CRC-specific AS events.

Bulk integrated single-cell-spatial transcriptomics reveals the impact of preoperative chemotherapy on cancer-associated fibroblasts and tumor cells in colorectal cancer, and construction of related predictive models using machine learning.

BACKGROUND: Preoperative chemotherapy (PC) is an important component of Colorectal cancer (CRC) treatment, but its effects on the biological functions of fibroblasts and epithelial cells in CRC are unclear. METHODS: This study utilized bulk, single-cell, and spatial transcriptomic sequencing data from 22 independent cohorts of CRC. Through bioinformatics analysis and in vitro experiments, the research investigated the impact of PC on fibroblast and epithelial cells in CRC. Subpopulations associated with PC and CRC prognosis were identified, and a predictive model was constructed using machine learning. RESULTS: PC significantly attenuated the pathways related to tumor progression in fibroblasts and epithelial cells. NOTCH3 + Fibroblast (NOTCH3 + Fib), TNNT1 + Epithelial (TNNT1 + Epi), and HSPA1A + Epithelial (HSPA1A + Epi) subpopulations were identified in the adjacent spatial region and were associated with poor prognosis in CRC. PC effectively diminished the presence of these subpopulations, concurrently inhibiting pathway activity and intercellular crosstalk. A risk signature model, named the Preoperative Chemotherapy Risk Signature Model (PCRSM), was constructed using machine learning. PCRSM emerged as an independent prognostic indicator for CRC, impacting both overall survival (OS) and recurrence-free survival (RFS), surpassing the performance of 89 previously published CRC risk signatures. Additionally, patients with a high PCRSM risk score showed sensitivity to fluorouracil-based adjuvant chemotherapy (FOLFOX) but resistance to single chemotherapy drugs (such as Bevacizumab and Oxaliplatin). Furthermore, this study predicted that patients with high PCRSM were resistant to anti-PD1therapy. CONCLUSION: In conclusion, this study identified three cell subpopulations (NOTCH3 + Fib, TNNT1 + Epi, and HSPA1A + Epi) associated with PC, which can be targeted to improve the prognosis of CRC patients. The PCRSM model shows promise in enhancing the survival and treatment of CRC patients.