Integrated analysis of necroptosis-related genes for evaluating immune infiltration and colon cancer prognosis

Deciphering human endogenous retrovirus expression in colorectal cancers: exploratory analysis regarding prognostic value in liver metastases

BACKGROUND

Human Endogenous RetroVirus (HERV) expression in tumours reflects epigenetic dysregulation of cancer and an oncogenic factor through promoter/enhancer action on genes. While more than 50% of colorectal cancers develop liver metastases, HERV has not been studied in this context.

METHODS

We collected 400 RNA-seq samples from over 200 patients with primary and liver metastases, including public data and a novel set of 200 samples.

FINDINGS

We observed global stability of HERV expression between liver metastases and primary colorectal cancers, suggesting an early oncogenic footprint. We identified a list of 17 HERV loci for liver metastatic colorectal cancer (lmCRC) characterization; with tumour-specificity validated in single-cell metastatic colorectal cancer data and normal tissue bulk RNA-seq. Eleven loci produced HERV-derived peptides as per tandem mass spectrometry from primary colorectal cancer. Six loci were associated with the risk of relapse after lmCRC surgery. Four, HERVH_Xp22.32a, HERVH_20p11.23b, HERVH_13q33.3, HERVH_13q31.3, had adverse prognostic value (log-rank p-value 0.028, 0.0083, 9e-4, 0.05, respectively) while two, HERVH_Xp22.2c (log-rank p-value 0.032) and HERVH_8q21.3b (in multivariable models) were associated with better prognosis. Moreover, the markers showed a cumulative effect on survival when expressed. Some were associated with decreased cytotoxic immune cells and most of them correlated with cell cycle pathways.

INTERPRETATION

These findings provide insights into the lmCRC transcriptome landscape by suggesting prognostic markers and potential therapeutic targets.

FUNDING

This work was supported by funding from institutional grants from Inserm, EFS, University of Bourgogne Franche-Comté, national found "Agence Nationale de la Recherche - ANR-JCJC: Projet HERIC and ANR-22-CE45-0007", and "La ligue contre le cancer".

The function of necroptosis in liver cancer

Liver cancer is one of the most lethal cancers, and apoptosis resistance is a major obstacle contributing to chemotherapy failure in liver cancer treatment. Inducing cancer cell death by bypassing the apoptotic pathway is considered a promising approach to overcome this problem. Necroptosis is a non-caspase-dependent regulated mode of cell death mainly mediated by receptor-interacting protein kinase 1 (RIPK1), RIPK3, and mixed lineage kinase domain-like (MLKL) protein, and the utilization of necroptosis for treating hepatocellular carcinoma (HCC) also offers a new hope for addressing liver cancer in the clinic. In this paper, the role of necroptosis in HCC as well as the effect on differentiation of liver cancer are reviewed. We also comparatively analyze the relationship among necroptosis, apoptosis, and necrosis, as well as summarize the characteristics and functions of key proteins involved in this pathway. The bidirectional regulation of necroptosis and the mitochondrial machinery within this pathway deserve attention.

Crosstalk between non-coding RNAs and programmed cell death in colorectal cancer: implications for targeted therapy

BACKGROUND

Colorectal cancer (CRC) remains one of the most common causes of cancer-related mortality worldwide. Its progression is influenced by complex interactions involving genetic, epigenetic, and environmental factors. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have been identified as key regulators of gene expression, affecting diverse biological processes, notably programmed cell death (PCD).

OBJECTIVE

This review aims to explore the relationship between ncRNAs and PCD in CRC, focusing on how ncRNAs influence cancer cell survival, proliferation, and treatment resistance.

METHODS

A comprehensive literature analysis was conducted to examine recent findings on the role of ncRNAs in modulating various PCD mechanisms, including apoptosis, autophagy, necroptosis, and pyroptosis, and their impact on CRC development and therapeutic response.

RESULTS

ncRNAs were found to significantly regulate PCD pathways, impacting tumor growth, metastasis, and treatment sensitivity in CRC. Their influence on these pathways highlights the potential of ncRNAs as biomarkers for early CRC detection and as targets for innovative therapeutic interventions.

CONCLUSION

Understanding the involvement of ncRNAs in PCD regulation offers new insights into CRC biology. The targeted modulation of ncRNA-PCD interactions presents promising avenues for personalized cancer treatment, which may improve patient outcomes by enhancing therapeutic effectiveness and reducing resistance.

Comprehensive Multi-Omics Analysis Reveals NPC2 and ITGAV Genes as Potential Prognostic Biomarkers in Gastrointestinal Cancers

BACKGROUND

Gastrointestinal cancers (GICs) continue to dominate in terms of both incidence and mortality worldwide. Due to the absence of efficient and accurate prognostic biomarkers, the prognosis and treatment outcomes of many GICs are poor. Identifying biomarkers to predict individual clinical outcomes efficiently is a fundamental challenge in clinical oncology. Although several biomarkers have been continually discovered, their predictive accuracy is relatively modest, and their therapeutic use is restricted. In light of this, the discovery of reliable biomarkers for predicting prognosis and outcome in GIC is urgently required.

MATERIALS AND METHODS

We evaluated the Human Protein Atlas dataset and identified NPC Intracellular Cholesterol Transporter 2 (NPC2) and Integrin Subunit Alpha V (ITGAV) as probable poor predictive genes for these cancers. In addition, we used the GEPIA2, cBioPortal, UALCAN, LinkedOmics, STRING, Enrichr, TISDB, TIMER2.0, hTFTarget, miRTarBase, circBank, and drug-gene interaction database databases to conduct a comprehensive and systematic analysis of the NPC2 and ITGAV genes.

RESULT

Our results found high expression levels of NPC2 and ITGAV in most GICs. The aforementioned gene expressions were linked to several clinicopathological characteristics of GICs as well as poorer prognosis in LIHC and STAD. The most common alteration type of NPC2 was amplification, and for ITGAV was deep deletion. Significant promotor hypermethylation was also seen in NPC2 and ITGAV in PAAD and COAD, respectively. For the immunologic significance, NPC2 and ITGAV were positively correlated with the abundance of tumor-infiltrating lymphocytes and macrophages. Furthermore, various immunomodulators showed strong correlations with the expression of these genes. There were currently 10 small molecule drugs targeting ITGAV.

CONCLUSION

Consequently, our bioinformatics analysis showed that NPC2 and ITGAV might be used as potential biomarkers to determine the prognosis of various GICs and are also related to immune infiltration.

GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer

Colorectal cancer is a leading cause of global mortality and presents a significant barrier to improving life expectancy. The primary objective of this study was to discern a unique differentially expressed gene (DEG) that exhibits a strong association with colorectal cancer. By achieving this goal, the research aims to contribute valuable insights to the field of translational medicine. We performed analysis of colorectal cancer microarray and the TCGA colon adenoma carcinoma (COAD) datasets to identify DEGs associated with COAD and common DEGs were selected. Furthermore, a pan-cancer analysis encompassing 33 different cancer types was performed to identify differential genes significantly expressed only in COAD. Then, comprehensively in-silico analysis including gene set enrichment analysis, constructing Protein-Protein interaction, co-expression, and competing endogenous RNA (ceRNA) networks, investigating the correlation between tumor-immune signatures in distinct tumor microenvironment and also the potential interactions between the identified gene and various drugs was executed. Further, the candidate gene was experimentally validated in tumoral colorectal tissues and colorectal adenomatous polyps by qRael-Time PCR. GUCA2A emerged as a significant DEG specific to colorectal cancer (|log2FC|> 1 and adjusted q-value < 0.05). Importantly, GUCA2A exhibited excellent diagnostic performance for COAD, with a 99.6% and 78% area under the curve (AUC) based on TCGA-COAD and colon cancer patients. In addition, GUCA2A expression in adenomatous polyps equal to or larger than 5 mm was significantly lower compared to smaller than 5 mm. Moreover, low expression of GUCA2A significantly impacted overall patient survival. Significant correlations were observed between tumor-immune signatures and GUCA2A expression. The ceRNA constructed included GUCA2A, 8 shared miRNAs, and 61 circRNAs. This study identifies GUCA2A as a promising prognostic and diagnostic biomarker for colorectal cancer. Further investigations are warranted to explore the potential of GUCA2A as a therapeutic biomarker.

A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer

BACKGROUND

The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI.

METHODS

Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes.

RESULTS

This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells.

CONCLUSION

We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

SLC4A4 is a novel driver of enzalutamide resistance in prostate cancer

The androgen receptor signaling inhibitor (ARSI) enzalutamide (Enz) has shown critical efficacy in the treatment of advanced prostate cancer (PCa). However, the development of drug resistance is a significant factor contributing to mortality in PCa patients. We aimed to explore the key mechanisms of Enz-resistance. Through analysis of GEO databases, we identified SLC4A4 as a novel driver in Enz resistance. Long-term Enz treatment leads to the up-regulation of SLC4A4, which in turn mediates P53 lactylation via the NF-κB/STAT3/SLC4A4 axis, ultimately leading to the development of Enz resistance and progression of PCa. SLC4A4 knockdown overcomes Enz resistance both in vitro and in vivo. Hence, our results suggest that targeting SLC4A4 could be a promising therapeutic strategy for Enz resistance. STATEMENT OF SIGNIFICANCE: SLC4A4 is a novel driver of enzalutamide resistance.

Identification of colon adenocarcinoma necroptosis subtypes and tumor antigens for the development of mRNA vaccines

Background

Colon adenocarcinoma (COAD) is a serious public health issue due to high incidence and mortality rate. This study aimed to identify possible tumor antigens and necroptosis subtypes of COAD for the development of mRNA vaccines and the selection of appropriate patients for precision therapy.

Methods

Gene expression profiles and clinical information for COAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. We comprehensively studied the alterations in necroptosis-related genes (NRGs) using cBioPortal, and screened the hub NRGs associated with the prognosis of patients with COAD using Gene Expression Profiling Interactive Analysis 2. Consensuses clustering analysis was performed to identify necroptosis subtypes. Weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules of the NRGs. The necroptosis landscape of COAD was assessed using graph learning-based dimensionality reduction. Finally, a drug sensitivity analysis of the two necroptosis subtypes was performed.

Findings

Two tumor antigens, BLC-2-associated X protein (BAX) and interleukin 1 beta (IL1B) were identified based on their associations with prognosis of patients and antigen presenting cell infiltration. Two necroptosis subtypes (N1 and N2) were distinguished in patients with COAD, and they were characterized by their differential survival status and molecular expression levels of immune checkpoint proteins and immunogenetic cell death modulators. Furthermore, the necroptosis landscape of COAD indicated that individual patients had obvious heterogeneity. Co-expression modules were identified using WGCNA, and the hub NRGs were found to be involved in various immune processes. Drug sensitivity analysis indicated that there were significant differences in drug sensitivity between the N1 and N2 subtypes. Cell experiments suggested that both overexpression of BAX and IL1B promoted necroptosis of COAD cells and enhanced the cytotoxicity of CD8+ T cells.

Interpretation

BAX and IL1B are potential antigens for the development of anti-COAD mRNA vaccines, specifically for patients with the N2 subtype. Consequently, this study will guide the development of more effective immunotherapeutic approaches and the identification of appropriate patients.

The fate and function of non-coding RNAs during necroptosis

Necroptosis is a novel form of cell death which is activated when apoptotic cell death signals are disrupted. Accumulating body of observations suggests that noncoding RNAs, which are the lately discovered mystery of the human genome, are significantly associated with necroptotic signaling circuitry. The fate and function of miRNAs have been well documented in human disease, especially cancer. Recently, lncRNAs have gained much attention due to their diverse regulatory functions. Although available studies are currently based on bioinformatic analysis, predicted interactions desires further attention, as these hold significant promise and should not be overlooked. In the light of these, here we comprehensively review and discuss noncoding RNA molecules that play significant roles during execution of necroptotic cell death.

Identification of glutamine metabolism-related gene signature to predict colorectal cancer prognosis

Backgrounds: Colorectal cancer (CRC) is a highly malignant gastrointestinal malignancy with a poor prognosis, which imposes a significant burden on patients and healthcare providers globally. Previous studies have established that genes related to glutamine metabolism play a crucial role in the development of CRC. However, no studies have yet explored the prognostic significance of these genes in CRC. Methods: CRC patient data were downloaded from The Cancer Genome Atlas (TCGA), while glutamine metabolism-related genes were obtained from the Molecular Signatures Database (MSigDB) database. Univariate COX regression analysis and LASSO Cox regression were utilized to identify 15 glutamine metabolism-related genes associated with CRC prognosis. The risk scores were calculated and stratified into high-risk and low-risk groups based on the median risk score. The model's efficacy was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. Cox regression analysis was employed to determine the risk score as an independent prognostic factor for CRC. Differential immune cell infiltration between the high-risk and low-risk groups was assessed using the ssGSEA method. The clinical applicability of the model was validated by constructing nomograms based on age, gender, clinical staging, and risk scores. Immunohistochemistry (IHC) was used to detect the expression levels of core genes. Results: We identified 15 genes related to glutamine metabolism in CRC: NLGN1, RIMKLB, UCN, CALB1, SYT4, WNT3A, NRCAM, LRFN4, PHGDH, GRM1, CBLN1, NRG1, GLYATL1, CBLN2, and VWC2. Compared to the high-risk group, the low-risk group demonstrated longer overall survival (OS) for CRC. Clinical correlation analysis revealed a positive correlation between the risk score and the clinical stage and TNM stage of CRC. Immune correlation analysis indicated a predominance of Th2 cells in the low-risk group. The nomogram exhibited excellent discriminatory ability for OS in CRC. Immunohistochemistry revealed that the core gene CBLN1 was expressed at a lower level in CRC, while GLYATL1 was expressed at a higher level. Conclusions: In summary, we have successfully identified and comprehensively analyzed a gene signature associated with glutamine metabolism in CRC for the first time. This gene signature consistently and reliably predicts the prognosis of CRC patients, indicating its potential as a metabolic target for individuals with CRC.

Prognostic value and immune landscapes of cuproptosis-related lncRNAs in esophageal squamous cell carcinoma

BACKGROUND

Precisely forecasting the prognosis of esophageal squamous cell carcinoma (ESCC) patients is a formidable challenge. Cuproptosis has been implicated in ESCC pathogenesis; however, the prognostic value of cuproptosis-associated long noncoding RNAs (CuRLs) in ESCC is unclear.

METHODS

Transcriptomic and clinical data related to ESCC were sourced from The Cancer Genome Atlas (TCGA). Using coexpression and Cox regression analysis to identify prognostically significant CuRLs, a prognostic signature was created. Nomogram models were established by incorporating the risk score and clinical characteristics. Tumor Immune Dysfunction and Rejection (TIDE) scores were derived by conducting an immune landscape analysis and evaluating the tumor mutational burden (TMB). Drug sensitivity analysis was performed to explore the underlying molecular mechanisms and guide clinical dosing.

RESULTS

Our risk score based on 5 CuRLs accurately predicted poorer prognosis in high-risk ESCC patients across almost all subgroups. The nomogram that included the risk score provided more precise prognostic predictions. Immune pathways, such as the B-cell receptor signaling pathway, were enriched in the datasets from high-risk patients. High TMB in high-risk patients indicated a relatively poor prognosis. High-risk patients with lower TIDE scores were found to benefit more from immunotherapy. High-risk patients exhibited greater responsiveness to Nilotinib, BI-2536, P22077, Zoledronate, and Fulvestrant, as revealed by drug sensitivity analysis. Real-time PCR validation demonstrated significant differential expression of four CuRLs between ESCC and normal cell lines.

CONCLUSIONS

The above risk score and nomogram can accurately predict prognosis in ESCC patients and provide guidance for chemotherapy and immunotherapy.

Identification of crucial modules and genes associated with backfat tissue development by WGCNA in Ningxiang pigs

Fat deposition is an economically important trait in pigs. Ningxiang pig, one of the four famous indigenous breeds in China, is characterized by high fat content. The underlying gene expression pattern in different developmental periods of backfat tissue remains unclear, and the purpose of this investigation is to explore the potential molecular regulators of backfat tissue development in Ningxiang pigs. Backfat tissue (three samples for each stage) was initially collected from different developmental stages (60, 120, 180, 240, 300, and 360 days after birth), and histological analysis and RNA sequencing (RNA-seq) were then conducted. Fragments per kilobase of transcript per million (FPKM) method was used to qualify gene expressions, and differentially expressed genes (DEGs) were identified. Furthermore, strongly co-expressed genes in modules, which were named by color, were clustered by Weighted gene co-expression network analysis (WGCNA) based on dynamic tree cutting algorithm. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment were subsequently implemented, and hub genes were described in each module. Finally, QPCR analysis was employed to validate RNA-seq data. The results showed that adipocyte area increased and adipocyte number decreased with development of backfat tissue. A total of 1,024 DEGs were identified in five comparison groups (120 days vs. 60 days, 180 days vs. 120 days, 240 days vs. 180 days, 300 days vs. 240 days, and 360 days vs. 300 days). The turquoise, red, pink, paleturquoise, darkorange, and darkgreen module had the highest correlation coefficient with 60, 120, 180, 240, 300, and 360 days developmental stage, while the tan, black and turquoise module had strong relationship with backfat thickness, adipocyte area, and adipocyte number, respectively. Thirteen hub genes (ACSL1, ACOX1, FN1, DCN, CHST13, COL1A1, COL1A2, COL6A3, COL5A1, COL14A1, OAZ3, DNM1, and SELP) were recognized. ACSL1 and ACOX1 might perform function in the early developmental stage of backfat tissue (60 days), and FN1, DCN, COL1A1, COL1A2, COL5A1, COL6A3, and COL14A1 have unignorable position in backfat tissue around 120 days developmental stage. Besides, hub genes SELP and DNM1 in modules significantly associated with backfat thickness and adipocyte area might be involved in the process of backfat tissue development. These findings contribute to understand the integrated mechanism underlying backfat tissue development and promote the progress of genetic improvement in Ningxiang pigs.

In silico analysis of the solute carrier (SLC) family in cancer indicates a link among DNA methylation, metabolic adaptation, drug response, and immune reactivity

Introduction: The oncogenic transformation is driven by genetic and epigenetic alterations influencing cancer cell fate. These alterations also result in metabolic reprogramming by modulating the expression of membrane Solute Carrier (SLC) transporters involved in biomolecules trafficking. SLCs act as tumor suppressors or promoters influencing cancer methylome, tumor growth, immune-escape, and chemoresistance. Methods: This in silico study aimed to identify the deregulated SLCs in various tumor types compared to normal tissues by analyzing the TCGA Target GTEx dataset. Furthermore, the relationship between SLCs expression and the most relevant tumor features was tackled along with their genetic regulation mediated by DNA methylation. Results: We identified 62 differentially expressed SLCs, including the downregulated SLC25A27 and SLC17A7, as well as the upregulated SLC27A2 and SLC12A8. Notably, SLC4A4 and SLC7A11 expression was associated with favorable and unfavorable outcome, respectively. Moreover, SLC6A14, SLC34A2, and SLC1A2 were linked to tumor immune responsiveness. Interestingly, SLC24A5 and SLC45A2 positively correlated with anti-MEK and anti-RAF sensitivity. The expression of relevant SLCs was correlated with hypo- and hyper-methylation of promoter and body region, showing an established DNA methylation pattern. Noteworthy, the positive association of cg06690548 (SLC7A11) methylation with cancer outcome suggests the independent predictive role of DNA methylation at a single nucleotide resolution. Discussion: Although our in silico overview revealed a wide heterogeneity depending on different SLCs functions and tumor types, we identified key SLCs and pointed out the role of DNA methylation as regulatory mechanism of their expression. Overall, these findings deserve further studies to identify novel cancer biomarkers and promising therapeutic targets.

Oxaliplatin related lncRNAs prognostic models predict the prognosis of patients given oxaliplatin-based chemotherapy

BACKGROUND

Oxaliplatin-based chemotherapy is the first-line treatment for colorectal cancer (CRC). Long noncoding RNAs (lncRNAs) have been implicated in chemotherapy sensitivity. This study aimed to identify lncRNAs related to oxaliplatin sensitivity and predict the prognosis of CRC patients underwent oxaliplatin-based chemotherapy.

METHODS

Data from the Genomics of Drug Sensitivity in Cancer (GDSC) was used to screen for lncRNAs related to oxaliplatin sensitivity. Four machine learning algorithms (LASSO, Decision tree, Random-forest, and support vector machine) were applied to identify the key lncRNAs. A predictive model for oxaliplatin sensitivity and a prognostic model based on key lncRNAs were established. The published datasets, and cell experiments were used to verify the predictive value.

RESULTS

A total of 805 tumor cell lines from GDSC were divided into oxaliplatin sensitive (top 1/3) and resistant (bottom 1/3) groups based on their IC50 values, and 113 lncRNAs, which were differentially expressed between the two groups, were selected and incorporated into four machine learning algorithms, and seven key lncRNAs were identified. The predictive model exhibited good predictions for oxaliplatin sensitivity. The prognostic model exhibited high performance in patients with CRC who underwent oxaliplatin-based chemotherapies. Four lncRNAs, including C20orf197, UCA1, MIR17HG, and MIR22HG, displayed consistent responses to oxaliplatin treatment in the validation analysis.

CONCLUSION

Certain lncRNAs were associated with oxaliplatin sensitivity and predicted the response to oxaliplatin treatment. The prognostic models established based on the key lncRNAs could predict the prognosis of patients given oxaliplatin-based chemotherapy.

Characterization of butyrate-metabolism in colorectal cancer to guide clinical treatment

Colorectal cancer (CRC) is the third most prevalent one in the world among the most common malignant tumors. Numerous studies have shown that butyrate has demonstrated promise as an antitumor agent in a variety of human cancer types. However, butyrate remains understudied in CRC tumorigenesis and progression. In this study, we explored therapeutic strategies to treat CRC by examining the role of butyrate metabolism. First, from the Molecular Signature Database (MSigDB), we identified 348 butyrate metabolism-related genes (BMRGs). Next, we downloaded 473 CRC and 41 standard colorectal tissue samples from The Cancer Genome Atlas (TCGA) database and the transcriptome data of GSE39582 dataset from Gene Expression Omnibus (GEO) database. Then we evaluated the expression patterns of butyrate metabolism-related genes with difference analysis in CRC. Through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, a prognostic model was constructed based on differentially expressed BMRGs. In addition, we discovered an independent prognostic marker for CRC patients. According to the expression levels and coefficients of identified BMRGs, the risk scores of all CRC samples were calculated. Utilizing differentially expressed genes in the high- and low-risk groups, we also constructed a Protein-Protein Interaction (PPI) network to visualize the interactions between proteins. Through the results of PPI network, we screened out differentially expressed target butyrate metabolism-related genes from ten hub genes. Finally, we performed clinical correlation analysis, immune cell infiltration analysis, and mutation analysis for these target genes. One hundred and seventy three differentially expressed butyrate metabolism-related genes were screened out in all the CRC samples. The prognostic model was established with univariate Cox regression and LASSO regression analysis. CRC patients' overall survival was significantly lower in the high-risk group than in the low-risk group for both training and validation set. Among the ten hub genes identified from the PPI network, four target butyrate metabolism-related genes were identified containing FN1, SERPINE1, THBS2, and COMP, which might provide novel markers or targets for treating CRC patients. Eighteen butyrate metabolism-related genes were used to develop a risk prognostic model that could be helpful for doctors to predict CRC patients' survival rate. Using this model, it is beneficial to forecast the response of CRC patients to immunotherapy and chemotherapy, thus making it easier to custom tailor cancer chemotherapy and immunotherapy to the individual patient.