Construction of Colorectal Cancer Prognostic Model Utilizing Mitochondrial Energy Metabolism-Related Genes

Construction of Colorectal Cancer Prognostic Model Utilizing Mitochondrial Energy Metabolism-Related Genes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Construction of Colorectal Cancer Prognostic Model Utilizing Mitochondrial Energy Metabolism-Related Genes Peng Zhu, Kai Wang, Guo Ping Sun, Zheng Hui Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4301530/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The objective of this study was to construct a prognostic model and medicine therapeutic response by utilizing mitochondrial energy metabolism-related genes (MMRGs), thus establishing a risk score for colorectal cancer (CRC). Based on the TCGA-CRC and GEO data set, MMRGs expression levels were identified by clustering analysis. 10 differential expression genes were used to construct RiskScore by Cox regression. GSE 39582 data set was used for validation. The clinical characteristics,survival characteristics,SNV,CNV,methylation, immune features, and potential benefits of chemotherapy drugs were analyzed for two risk groups. RiskScore was constructed based on the genes ACOX1, ATP6V1G2, COX7A1, CPT2, DLAT, ECGS1, ECI2, NDUFA1, PPA2, and SUCLG2. Patients in the low risk group exhibited a superior overall survival. In addition, Univariate Cox regression analysis and Multivariate Cox regression analysis demonstrated that the risk score, stage and lymphatic invasion can serve as the independent prognostic factors.Trametinib exhibited positive correlations between IC50 values and MMRGs expression levels,which may be more sensitive to chemotherapy drugs. Mitochondrial Energy -Related Genes was a promising biomarker that can be used to distinguish CRC prognosis, immune features, and sensitivity to chemotherapy drugs. Biological sciences/Biochemistry Biological sciences/Cancer Biological sciences/Chemical biology Biological sciences/Computational biology and bioinformatics Mitochondrial energy metabolism Colorect cancer Prognosis model Tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Colorectal cancer (CRC) is the second significant contributor to cancer-related mortality globally . The high incidence and mortality due to its related with bad lifestyles , unhealthy dietary factors ,smoking , alcohol consumption genetic and other factors(1-2)While with the varity development of clinic treatments ,the incidence of CRC has been declining in recent years, there has been a significant increase in the death rate of CRC individuals in China(3).In light of this, researchers persist in undertaking novel studies to enhance the efficacy of CRC treatment and prognostic outcomes for individuals .In many studies,advanced clinical manifestation and molecular characteristics is related with colorectal cancer(1-2),including biomarkers ,mutations , methylation,Cellular senescence or signals biomarker signatures（4-6）. Cellular senescence was demonstrated that was connected with CRC or changed of metabolic pathways to inhibit cancer progression(6).Senescent cells are characterized by need sufficient energy like mitochondrial to synthesize various metabolish pathway(7).Recently studies have shown that Mitochondrial disorders have been discoved in varity tumor cells and have demonstrated various mitochondrial proteins as pathological mechanism part to play a important role(8).In support of this,To change the number of mitochondrial content and activity, can affect the growth of cell differentiation and toward oxidative phosphorylation in metabolism(8).The mitochondrial energy metabolism pathway can regulate stem cell functions through many mechanisms, including citrate cycle tca cycle,fatty acid metabolism , ketone body metabolism , glycolysis gluconeogenesis, oxidative phosphorylation(9).Mitochondrial dysfunction was demonstrated that was involved in cellular senescence,and may changed the metabolic pathways to favor carcinogenesis(10).Such as a high expression gene GAPDHs in the mitochondrial glycolytic pathway was associated with worse clinical outcome in stage III melanoma (10). Accumulating evidence has proven molecular testing can help to diagnose and intervent CRC, improve patient prognosis, reduce mortality, and lower the economic burden of disease on individuals(5,11). Hence, investigations into the metabolic aspects of mitochondrial are anticipated to offer valuable insights for prognostic predictions and optimization of treatment strategies for individuals with CRC. Nevertheless, at present, there is a lack of research exploring the correlation between mitochondrial energy metabolism pathway-related genes (MMRGs) and the prognosis of CRC patients. Therefore, differentiating clustering features and establishing features related to MMRGs may be effective in predicting the prognosis and immunotherapy response of CRC patients.We plan to utilize CRC samples from public databases to study the linkage between MMRGs and clinical features and prognosis of CRC. We established a prognostic model through regression analysis and conducted an independent analysis of the prognosis. We also conducted tumor mutation analysis, immunotherapy response, and chemotherapy drug sensitivity analysis, aiming to contribute additional data support toward improving the prognosis and treatment options available for individuals with CRC. Methods Data source and acquisition TCGA-COAD was downloaded in the TCGA database from University of California Santa Cruz (UCSC) was used to get the latest clinical follow-up information and corresponding STAD, COAD, and READ gene expression data . We downloaded GSE39582 data from National Center for Biotechnology Information (NCBI), containing 562 samples, respectively. The following steps were performed for pretreatment: 1) Remove samples with no clinical data or uncompleted data. 2) Remove samples of normal tissue. 3) Remove the gene with a transcript per million (TPM) <0.01 in half the samples.The raw dataset count and the corresponding clinical information was showed in Table1 and Table S1. Mitochondrial energy metabolism pathway-related genes(MMRGs) were derived from KEGG and MsigDB databases（Version 7.5.1）. We downloaded MMRG corresponding to pathways containing keywords such as KEGG_GLYCOLYSIS_GLUCONEOGENESIS, KEGG_CITRATE_CYCLE_TCA_CYCLE, KEGG_OXIDATIVE_PHOSPHORYLATION, REACTOME_KETONE_BODY_METABOLISM, KEGG_FATTY_ACID_METABOLISM, respectively. It was verified that the differentially gene expression level of mitochondrial metabolism in rectal canner data obeyed a normal distribution, subsequently, we matched the clinical feather and gene expression (include genomic alteration 、 methylation analysis 、survival expression 、immunotherapy response) , and then verificate the independent risk factor and classifed those differentlly express genes with expression above the average level as high expression group, and those differently express genes with expression below the average level as low expression group to construct nomogram predict the dignosis value. Table 1 The raw dataset count and the corresponding clinical information TCGA-CRC GSE39582 stage I 55 32 II 133 262 III 114 204 IV 53 60 N/A 18 4 gender Female 170 253 Male 203 309 N/A 0 0 age >60 231 405 <=60 142 157 N/A 0 0 Tumor location1 LSCRC 188 RSCC 170 N/A 15 Tumor location2 Distal 342 Proximal 220 N/A 0 Lymphatic invasion No 224 Yes 103 N/A 46 OS Deceased 85 191 Living 288 371 Mitochondrial energy metabolism pathway-related genes(MMRGs) express analysis of rectal cancer Differential analysis of the GEO dataset and TCGA-CRC dataset (log2(TPM + 1)) was conducted to screen the DEGs using the R ``limma'' package with FDR < 0.05 as the threshold. According to the type of dataset ,divide the sample into adjacent and cancerous tissues, and calculate the differentially expressed genes using the Bayes method of limma (V3.50.3) package. Data between the two groups were compared using the Wilcoxon rank sum test. The R clusterProfiler（V4.2.2）"pheatmap" and ``ggplot2′' software packages were used to plot heat maps and GSEA anylysis to explore and understand the mitochondrial metabollism in rectal cancer.All analyses in this study were performed under R cluster Profiler version 4.2.2 Genomic alteration and methylation analysis According the MMRG, we used maftools（v2.10.05）to analyze the genomic alterations of mitochondrial metabolism in rectal canner , evaluate the expression of MMRG correlated with copy-number variation (CNV) and DNA methylation, and used ggplot2（v3.3.5） Fisher’s statistical test method to visualize the genomic alteration of DEGs. The DEGs was selected for genomic profle change analysis, including mutations, putative CNAs from genomic identifcation of signifcant targets of mitochondrial metabolismin rectal canner, and structural variants. Additionally, we analyzed the Spearman’s correlations between DNA methylation level and its expression in MMRG by using R ChAMP package（version 2.26.0). Correlation analysis between MMRG and clinical characteristics According to the MMRG ,we used T test method to assess the differentially gene expression level between paracancer and cancerous tissues;We used Kruskal-Wallis rank test to value the connection between DEGs and clinical characteristics(including Age, Gender, Grade,BMI, histological type，TNM stage, Tumor stage,and survival) .We categorized DEGS into the high-risk group and the low-risk group, based on the median values of gene expression values and to calculate the difference between two group by using software R survival package（v3.3-1）. Then,the K-M curve was drawn to evaluate the prognostic value of the risk model by using software R survminer package（v0.4.9）. Immune microenvironment and immune relative analysis To investigate whether the immune microenvironment relative with MMRG and has the ability to assist in affecting mitochondrial metabolism pathway in rectal canner.We used softwareCIBERSORT, GSVA（v1.42.0）,xCell（v1.1.0）and estimate（v1.0.13） to calculate the correlation between the expression of the MMRG core genes and the immune infiltration score. Further research was conducted to determine whether the signature affects the clinical outcomes of patients with comparable immune infiltration gene expression levels. Constructure and Validate MMRGs differentilly gene express signature In order to understand the gene and protein interaction networks of MMRG were constructed using online STRING database (http://string-db.org) and using software STRINGdb（v2.6.5） to complish the gene-set enrichment analysis to understand the biological processes of MMRG. In order to achieve the differentially gene express signature ,we used software glmnet（v4.1-4）to constructure LASSO-Cox model.Further,the differentially gene express signature of MMRG was identified the independence of the clinical factors(P value<0.05 ) (age、stage、gender、tumor location、perineural invasion、venous invasion、lymphatic invasion)and the prognostic risk model through the univariate Cox regression analysis and the multivariate Cox regression analysis by software R packagesurviv（v3.3-1). Drug sensitivity assessment Drug sensitivity data for rectal canner patients is available in the The Cancer Therapeutics Response Portal database (http://portals.broadinstitute.org/ctrp/) and Genomics of Drug Sensitivity in Cancer(https://www.cancerrxgene.org/). the drug response and the relationship of differentially gene express signature was presented by the half-maximal inhibitory concentration (IC50/AUC) which used the R software. The results have appeared in box plots. Statistical analysis All data analysis was conducted in the R environment (version 4.1.0). The Student's T test or one-way ANOVA was used to analyze group differences in variables with normal distribution; the chi-square test was used to compare differences between categorical variables; and the Wilcoxon rank test was used to compare group differences in variables with unknown distribution.Unless otherwise stated, P value<0.05 was regarded as statistically significant.* represent p <0.05, * *represent p <0.01, * * *represent p <0.001, * * * * represent p <0.0001and ns, p ≥ 0.05. Results Identification of MMRGs between adjacent and cancerous tissues Based on TCGA-CRC expression profile data, we used the R limma package gene set enrichment analysis (GSEA) to compare the difference between the adjacent and cancerous tissues of mitochondrial energy metabolism gene expression, and the genes were ranked according to the statistical value (logFC), and the unregulated genes ranked before and downregulated ranked the last. Then, enrichment analysised the preranked genes. The results showed that a total of 114 Mitochondrial energy metabolism pathway-related genes (MMRGs) was obtained and all pathways(including Citrate cycle tca cycle, Fatty acid metabolism, Ketone body metabolism, Glycolysis gluconeogenesis, Oxidative phosphorylation) were significantly enriched (p < 0.05),and most pathways were concentrated in upregulated regions, these genes were significantly more expressed in adjacent tissues than cancerous tissues (Fig. 1 A-E). These analysis results suggested that mitochondrial energy metabolism activity was reduced in cancer cells. Analysis of the association between SNV ,CNV ,methylation and different clinical features based on MMRGs Our study of somatic alteration analysis was TCGA-CRC MMRGs by using software maftools(v2.10.05).The gene base mutations replace model almost was C > T、T > C、C > A,and the proportion of base transition is significantly higher than base tranversion(Fig. 2 A). Only OGDHL and PC gene sets in the MMRGs have associated gene mutations, which are generally rare with frequencies ranging 10%, where OGDHL has the highest mutation frequency(Fig. 2 B) .We obtained differentially methylated sites (DMP) significantly enriched by using the R package ChAMP (V2.26.0), along with their corresponding MMRGs related target genes(Fig. 2 C). Differentially methylation sites were screened against a threshold deltaBeta > 0.1 and a corrected p-value < 0.05. Kruskal-Wallis Rank sum test was used to analyze the correlation between MMRGs expression level and clinical characteristics (age, tumor stage, tumor location, lymphatic invasion, perineural invasion, venous invasion). Based on the results, the clinic opathological characteristics were depicted in Fig. 3 A, highlighting signifcant diferences in tumor location, T stage,pathologic stage,perineural invasion ,venous invasion ,lymphatic invasion between two risk stratifcations of the TCGA-CRC (all p < 0.05). In addtion, The number of the MMRGs specific biomarkers were 28 in tumor location,and the highlight expression of MMRGs biomarkers were ACOX1, NDUFV2, OGDHL, PCK1 (Fig. 3 A); the number of MMRGs specific biomarkers associated with pathologic stage were 64,the high expression of MMRGs specific biomarkers were ACADM, ACADS, ACADSB (Fig. 3 B); the number of MMRGs specific biomarkers associated with perineural invasion were 4 (Fig. 3 C), the number of MMRGs specific biomarkers associated with venous invasion were 12,and the highlight expression of MMRGs biomarkers were ACADSB, ATP6V0C, COX6B2, MT-ND4 (Fig. 3 D) the number of MMRGs specific biomarkers associated with lymphatic invasion were 36, and the highlight expression biomarkers were UQCRC1, SUCLG1, SDHB, ATP6V1G2, COX5A, ACADSB (Fig. 3 E) (all p < 0.05). Survival analysis of the expression levels of the MMRGs Based on the median value of the gene expression,samples were divided into high and low expression groups to delve deeper into the potential prognostic value of MMRGs, and we utilized R package survival to analyze their association with patient survival across CRC. The results, presented in Fig. 4 and supplementary revealed a complex landscape of relationships between MMRGs expression and survival outcomes. Overall survival (OS)showed different prognostic effects depending on the different gene. In this results the high expression of PGAM2,COX7A1a and ATP6V1G2 predicted poorer overall survival (OS) in MMRGs.And the opposite result is the low expression of other MMRGs were associated with poorer overall survival(OS). Evaluation of MMGRs and Immune enviroment Levels in TCGA-CRC gene set We assessed any possible association between the risk model and immune-associated biomarkers and observed a significant relationship by using online software CIBERSORT, xCell(v1.1.0), estimate༈v1.0.13༉ and the ssgsea method of GSVA (v1.42.0) .MMRGs in TCGA-CRC gene set were assessed to observe whether there was an association between MMRGs expression and immune infiltration microenvironment. The results of online software CIBERSORT showed that Spearman’s correlation analysis revealed that the most MMRGs expression was positively associated with immune infiltration, including Plasma cell, resting mast cell infiltration ; In contrast, this group was negatively associated with activatedMast cells, macrophages M0, neutrophils, initial CD4 + T cells, and memory-activated CD4 + T cellsion risk cores in the tumor(Fig. 5 .A) .Similar results were obtained by software xCell༈v1.1.0༉.As shown in Fig. 5 C, MMRGs had a significantly higher level of correlation between immune matrix score and immune score was found by following estimate (v1.0.13) software (Fig. 5 .B) .Furthermore,based on the TCGA-CRC gene expression data and the manually collected immune infiltration gene set (PMID: 30594216), the immune infiltration status of TCGA-CRC patients was calculated using the ssgsea method of GSVA (v1.42.0),the results showed that most of the MMRG core gene expression showed a high positive correlation with the immune infiltration scores of 29 immune cells, but a small number of mitochondrial genes were negatively correlated with the immune infiltration scores of immune cells(Fig. 5 .D).These results suggested that we observed a significant relationship between MMGRs and various immune cells levels and the immune enviroment may affect the the expression of MMRGS. Molecular networks and functional enrichment of MMRGs To unlock the MMRGs family’s functional roles, we explored their molecular networks and enriched pathways using bioinformatic tools. Using STRING ( https://string-db.org/ ), we predicted protein-protein interaction networks and analyzied this combined network, revealing likely partners for MMRGs, respectively. By analyzing this combined network, we unraveled enrichment patterns in biology process analysis.The results showed showed positive correlation between most MMRGs, but the correlation between mitochondrial genes and nuclear genes was low and negative correlation, and there were strong positive correlation between mitochondrial genes(P<0.05)(Fig. 6 A).The protein interaction network diagram of MMRGs showed that the type of genes are clearly divided into three parts. The first part is several proteins related to ATP enzyme hydrogen ion transport, which have interaction with other parts; the second part is ubiquinone redox-related protein, whose protein interaction is complex, and the third part is other proteins, with no obvious rules(P<0.05)(Fig. 6 B).Furthermore,the results of MMRGs enrichment analysis showed that genes are mainly enriched in metabolic related of generation of precursor metabolites, energyand oxidation-reduction process,drug metabolic process,cellular respiration,small molecule metabolic process(Table 2 ). Table 2 The enrichment analysis results of the MMRGs in biology progress term description count fdr GO.0006091 generation of precursor metabolites and energy 83 of 400 1.96E-107 GO.0055114 oxidation-reduction process 95 of 932 1.62E-101 GO.0017144 drug metabolic process 80 of 622 7.66E-88 GO.0045333 cellular respiration 60 of 153 1.44E-87 GO.0044281 small molecule metabolic process 98 of 1779 8.52E-82 Validation of the Prognostic Model To examine the MMRGs express for the prognostic prediction of rectal cancer, 23 MMRGs of TCGA-CRC, which exhibited associations with overall survival in rectal cancer patients, underwent LASSO-Cox regression analysis. We tested the reliability of the prognostic risk score model in the GSE39582 data set. This analysis led to the establishment of a signature comprising ten genes, as follows:MMRGS = ACOX1 expression * -0.12684 + ATP6V1G2 expression * 0.36042 + COX7A1 expression * 0.02761 + CPT2 expression *-0.07471 + DLAT expression * -0.03181 + ECGS1 expression * 0.06075 + ECI2 expression * -0.0480 + NDUFA1 expression * 0.03525 + PPA2 expression * -0.0706 + SUCLG2 expression * -0.028727. The TCGA-CRC and GSE39582 MMRGs, was used to cut-of median score value to classify all cases into high- and low-risk groups. Expression levels of these ten candidate genes of TCGA-CRC and GSE 39582 in high- and low-risk groups were visualized in a heatmap (Fig. 7 A) .Based on LASSO-Cox regression model,the higher MMRG scores the lower survival status and survival time in TCGA-CRC and GSE 39582 (Fig. 7 B).Kaplan–Meier survival analysis demonstrated the same result of TCGA-CRC set and GSE39582 of rectal cancer patients with higher risk scores experienced worse prognosis(Fig. 7 C). AUC values of TCGA-CRC set under the ROC curve were 0.678 at 1 year, 0.669 at 3 years, and 0.764 at 5 years (Fig. 7 D),and AUC values of GSE 39582 set under the ROC curve were 0.5 at 1 year, 0.577 at 3 years, and 0.562 at 5 years ,both results indicating that the prognostic model of MMRGs had a good predictive ability. Therefore,We used R package survival(v3.3-1) to evaluate the independence of the risk model, we performed a univariate Cox regression analysis and a multivariate Cox regression analysis on clinical factors and risk score. As shown in ( Fig. 7 E,F )we established a nomogram based on these independent factors and demonstrated that the risk score, stage and lymphatic invasion can serve as the independent prognostic factors༈p < 0.05༉. This reaffirms the influence of varied MMRGs expression patterns on prognosis and supports the use of signature-derived risk scores as clinical prognostic factors.In the GSE39582 data set, MMRGS was significantly associated with tumor stage and chromosomal instability status (CIN)(P<0.05)༈Fig. 7 G༉. Patients with advanced tumor stage and positive chromosomal instability had higher MMRGs scores.These findings aligned with the aforementioned survival analysis, indicating that an elevated risk score is associated with tumor stage, implying an unfavorable prognosis. Prediction of the MMRGs therapeutic response To identify potential anti-CC agents, we searched the The Cancer Therapeutics Response Portal database ( http://portals.broadinstitute.org/ctrp/ ) and Genomics of Drug Sensitivity in Cancer( https://www.cancerrxgene.org/ ) for potentially sensitive and selective chemotherapeutic agents. According to providing the half-maximal inhibitory concentration (IC50) as a measure of drug sensitivity, lower IC50 values indicate greater sensitivity to treatment. According to drug sensitivity of TCGA and GEO MMRGs to drugs in GDSC database, different reactive drugs were plotted and listed according to KruskalWallis test, respectively. We selected and visualized drugs with a statistically significant difference in sensitivity between the two groups (p < 0.05) .Among them, nine drugs, including BCL-LZH-4, MG-132, MI-2, BRD-K99006945, Trametinib, RDEA119, 17-AAG,RDEA119, PD-0325901, exhibited positive correlations between IC50 values and MMRGs expression levels, indicating higher sensitivity in cells with higher MMRGs expression. Conversely, ten drugs, such as ML210, SB-225002, SRT-1720, Fingolimod, Cytarabine Hydrochloride, ABT-263, BX-912, Tubastatin A, GSK429286A, GSK1070916, showed negative IC50 correlations with MMRGs expression, suggesting lower sensitivity when MMRGs levels were elevated(Fig. 8 ). Discussion Previous studies have demonstrated that DEGs between tumor and peritumor tissues in patients with CRC can be used to assess patient prognosis ( 12 ). Consistent with this, the present study found that MMRGs was obtained between tumor and peritumor tissues and all pathways(including Citrate cycle tca cycle,Fatty acid metabolism,Ketone body metabolism,Glycolysis gluconeogenesis,Oxidative phosphorylation) were significantly enriched (p < 0.05). Previous research has found that mutations and methylaton in DNA are commonly found in cancer cells and regulated tumor metabolism and progression to promote the tumor of bioenergetics and biosynthesis( 13 – 14 ).In our study,the same results is that we obtained mutations sites and differentially methylated sites (DMP) significantly enriched by using the R package ChAMP (V2.26.0), along with their corresponding MMRGs related target genes(Fig. 3 C). Increasing evidence has shown that the different regions of CRC originate from different factors, the rate of different anatomical regions of colorectal cancer associated with embryonic origins ,prognostic factors ,clinical presentations( 15 – 16 ).In our study, Kruskal-Wallis Rank sum test ,immune infiltration analysis and protein-protein interaction (PPI) network identified key immune signature gene sets associated with CRC, and MMRGs expression level was associated with the clinical characteristics (age, tumor stage, tumor location, lymphatic invasion, perineural invasion, venous invasion,survival rate).This finding aligns with previous reports this study reveals unbalanced distribution of genes in the immune environment, the previous reported that the migration of immune cells was coordinated with infiltration ,we hypothesize that the immune enviroment may affect the the expression of MMRGS,and this process might regulate in tumor micro environment( 17 ). To better predict the risk of mortality and prognosis of patients with CRC, the present study selected 114 MMRGs from TCGA datasets of patients with CRC, and GEO datasets for the validation results.From the result ,We identified 10 prognostic genes (namely ACOX1, ATP6V1G2, COX7A1, CPT2, DLAT, ECGS1, ECI2, NDUFA1, PPA2 ,SUCLG2) based on singlefactor and multifactor Cox analyses. Therefore a prognostic model was built using the comprehensive risk scores of the aforementioned 10 genes, and the results showed that the predictive effectiveness of the model was good when using GEO datasets. Consistent with this, the clinical data showed that the risk scores of these 10 genes were independent predictive factors when using both TCGA and GEO datasets.Previous studies have found that ACOX1 is significantly regulated in CRC tissues and dephosphorylation of ACOX1 promotes β-catenin palmitoylation to drive colorectal cancer progression(18).ATP6V1G2 was associated With Polymorphic Variations in Breast Cancer Patients(19). COX7A1 may connected with methylation and suppressed the viability of human non-small cell lung cancer cells via regulating autophagy(20–21).The regulation of CPT2 was found downregulation in cancer tissue(22–23).The downregulation of CPT2 promoted proliferation and inhibited apoptosis through p53 pathway in colorectal cancer(23).And CPT2 downregulation triggered stemness and oxaliplatin resistance in colorectal cancer via activating the ROS/Wnt/β-catenin-induced glycolytic metabolism (24).DLAT as an immunological and prognostic biomarker correlated with Prognosis, Chemoresistance, and Immune Infiltration in variety of cancer(25–26).A previous study revealed ECI2 was connected with epigenetic and genetic changes ECI2 in high-grade serous ovarian carcinoma(27).Downregulation of NDUFA1 and other oxidative phosphorylation-related genes was a consistent feature of basal cell carcinoma(28).PPA2 was connected sudden cardiac death and the Mitochondrial Inorganic Pyrophosphatase (29).SUCLG2 participated in epigenetic expression and regulated cancer express(30).According this results,ATP6V1G2, ECI2, COX7A1, SUCLG2 was connected with epigenetic regulation.And the expression of ACOX1, PPA, NDUFA1 may regulate the mitochondria-related pathways in CRC.Therefore, according to drug sensitivity of TCGA and GEO MMRGs ,We found drug target prediction of the model's 10 genes, evaluated the potential therapeutic value of these genes, and screened for possible drug . These results suggested that MMRGs was a critical gene affecting the proliferation and migration of tumor cells in CRC. In conclusion, the present study developed and constructed a prognostic model with potential clinical application. The functional role of the key gene, ACOX1, ATP6V1G2, COX7A1, CPT2, DLAT, ECGS1, ECI2, NDUFA1, PPA2 ,SUCLG2 in CRC was identified, providing evidence for its potential application as a prognostic biomarker and therapeutic target in CRC. However, the present study had some limitations.Our study may have inherent biases and inaccuracies,lack external experimental to validate ATP6V1G2, ECI2, COX7A1, SUCLG2 can lead to CRC gene mutation or other change. Conclusion In conclusion, our study provided a systematic analysis of mitochondrial energy metabolism-gene in colorectal cancer, covering their expression, clinical feature,prognostic significance, biological functions, immune microenviroment, survival prognosis, medicine sensitive. Notably, we identified mitochondrial energy metabolism as an essential factor in colorectal cancer, playing a crucial role as a key determinant of colorectal cancer prognosis. Our study provides new potential targets for prognosis and therapeutic prediction in colorectal cancer.The nomogram constructed based on the risk score and clinical variables is more benefcial for clinicians to assess the survival prognosis of CRC. These findings may have some limitation to consider, as our study being bioinformatics research based on data collected from public databases. Declarations Financial Disclosure This study was supported by grants from by the Natural Science Foundation of Shen Zhen techology plan (JCYJ20210324135205016). Author Contribution (I) Conception and design: Guo Ping Sun. (II) Collection and assembly of data: Guo Ping Sun and Peng Zhu. (III) Data analysis and interpretation: Peng Zhu ,Zheng Hui Yang and Kai Wang. (V) Manuscript writing: Kai Wang. 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Discovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration. Transl Oncol. 2021; 14 (3): 101011. doi: 10.1016/j.tranon.2021.101011 Zhang, Q, Yang, X, Wu, J, et al. Reprogramming of palmitic acid induced by dephosphorylation of ACOX1 promotes β-catenin palmitoylation to drive colorectal cancer progression. Cell Discov. 2023; 9 (1): 26. doi: 10.1038/s41421-022-00515-x Todorova, VK, Makhoul, I, Dhakal, I, et al. Polymorphic Variations Associated With Doxorubicin-Induced Cardiotoxicity in Breast Cancer Patients. ONCOL RES. 2017; 25 (8): 1223–1229. doi: 10.3727/096504017X14876245096439 Rönn, T, Poulsen, P, Hansson, O, et al. Age influences DNA methylation and gene expression of COX7A1 in human skeletal muscle. DIABETOLOGIA. 2008; 51 (7): 1159–68. doi: 10.1007/s00125-008-1018-8 Zhao, L, Chen, X, Feng, Y, et al. COX7A1 suppresses the viability of human non-small cell lung cancer cells via regulating autophagy. Cancer Med. 2019; 8 (18): 7762–7773. doi: 10.1002/cam4.2659 Zhang, X, Zhang, Z, Liu, S, et al. CPT2 Down-Regulation Promotes Tumor Growth and Metastasis Through Inducing ROS/NFκB Pathway in Ovarian Cancer SSRN. 2020; doi: 10.2139/ssrn.3736010 Liu, F, Li, X, Yan, H, et al. Downregulation of CPT2 promotes proliferation and inhibits apoptosis through p53 pathway in colorectal cancer. CELL SIGNAL. 2022; 92 110267. doi: 10.1016/j.cellsig.2022.110267 Li, H, Chen, J, Liu, J, et al. CPT2 downregulation triggers stemness and oxaliplatin resistance in colorectal cancer via activating the ROS/Wnt/β-catenin-induced glycolytic metabolism. EXP CELL RES. 2021; 409 (1): 112892. doi: 10.1016/j.yexcr.2021.112892 Zhou, C, Jin, L, Yu, J, et al. Integrated analysis identifies cuproptosis-related gene DLAT and its competing endogenous RNAs network to predict the prognosis of pancreatic adenocarcinoma patients. MEDICINE. 2024; 103 (9): e37322. doi: 10.1097/MD.0000000000037322 Fang, Z, Wang, W, Liu, Y, et al. Cuproptosis-Related Gene DLAT as a Novel Biomarker Correlated with Prognosis, Chemoresistance, and Immune Infiltration in Pancreatic Adenocarcinoma: A Preliminary Study Based on Bioinformatics Analysis. CURR ONCOL. 2023; 30 (3): 2997–3019. doi: 10.3390/curroncol30030228 Němejcová, K, Bártů, M, Hojný, J, et al. A comprehensive analysis of the expression, epigenetic and genetic changes of HNF1B and ECI2 in 122 cases of high-grade serous ovarian carcinoma. ONCOL LETT. 2021; 21 (3): 185. doi: 10.3892/ol.2021.12446 Mamelak, AJ, Kowalski, J, Murphy, K, et al. Downregulation of NDUFA1 and other oxidative phosphorylation-related genes is a consistent feature of basal cell carcinoma. EXP DERMATOL. 2005; 14 (5): 336–48. doi: 10.1111/j.0906-6705.2005.00278.x Guimier, A, Achleitner, MT, Moreau de Bellaing, A, et al. PPA2-associated sudden cardiac death: extending the clinical and allelic spectrum in 20 new families. GENET MED. 2021; 23 (12): 2415–2425. doi: 10.1038/s41436-021-01296-6 Hu, X, Wu, J, Feng, Y, et al. METTL3-stabilized super enhancers-lncRNA SUCLG2-AS1 mediates the formation of a long-range chromatin loop between enhancers and promoters of SOX2 in metastasis and radiosensitivity of nasopharyngeal carcinoma. Clin Transl Med. 2023; 13 (9): e1361. doi: 10.1002/ctm2.1361 Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4301530","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":297284572,"identity":"e4fada14-00e7-4ece-b74b-a6ba6b557402","order_by":0,"name":"Peng Zhu","email":"","orcid":"","institution":"Shen Zhen Ping Shan People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhu","suffix":""},{"id":297284580,"identity":"8cc2dd2f-5904-4976-8ae5-059d255d3dfe","order_by":1,"name":"Kai Wang","email":"","orcid":"","institution":"The college of Zhe Jiang Shu Ren","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wang","suffix":""},{"id":297284586,"identity":"0be59ff4-88b5-4783-bb80-96605fc62e9d","order_by":2,"name":"Guo Ping Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACNmb2AwcS/9jU2x9vPkCcFj52nsQDDxvSEhjOHEsgToscP4PxwYcNhxIYbuQYEOswhoQDiTsO5DH2nPl44w2DnZxuA0EtjEC/nLlTzMzeu9lyDkOysdkBYmxJYHvG2MZzdps0D8OBxG1EaDEAajnM2COR84wELYlthxNnSOSwEauFB+iwM2nGBjzHjC3nGBDhF/n+44c//qiwkTNgb354402FnRxBLShAgofIqEHWQqqOUTAKRsEoGBEAALSBRvUOIebQAAAAAElFTkSuQmCC","orcid":"","institution":"Shen Zhen Ping Shan People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Guo","middleName":"Ping","lastName":"Sun","suffix":""},{"id":297284591,"identity":"2afd9ca5-6c38-411d-9449-40a1e1ee68a5","order_by":3,"name":"Zheng Hui Yang","email":"","orcid":"","institution":"Hang Zhou QuanMei Dental Clinic","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"Hui","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-04-21 16:26:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4301530/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4301530/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55769260,"identity":"db1ff9c3-8501-4047-a1ae-255d63a0ab81","added_by":"auto","created_at":"2024-05-02 20:41:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":236753,"visible":true,"origin":"","legend":"\u003cp\u003eThe enrichment of heatmap of MMRGs in different pathway. \u003cstrong\u003e(A) \u003c/strong\u003eCitrate cycle tca cycle;\u003cstrong\u003e(B)\u003c/strong\u003eFatty acid metabolism;\u003cstrong\u003e(C)\u003c/strong\u003eKetone body metabolism ; \u003cstrong\u003e(D)\u003c/strong\u003e Glycolysis gluconeogenesis; \u003cstrong\u003e(E) \u003c/strong\u003eOxidative phosphorylation.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4301530/v1/4541cfb453077c69b99f4a2f.png"},{"id":55769267,"identity":"6c3fe017-68f8-4831-9233-75f8594256d7","added_by":"auto","created_at":"2024-05-02 20:41:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":315572,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eCNV plots depict the distribution of base mutations in genomic variants ;\u003cstrong\u003e(B)\u003c/strong\u003e The SNV plots depict the distribution of mutations in GGM-related genes, along with classifications of the various SNV types;\u003cstrong\u003e(C) \u003c/strong\u003eDNA methylation differences in adjacent and cancerous tissues of MMRGs\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4301530/v1/e8d67846826c73ce0d045bac.png"},{"id":55769263,"identity":"c8cb7172-60ed-4f52-8c08-1b0ef661b02e","added_by":"auto","created_at":"2024-05-02 20:41:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161835,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap illustrating the variation in expression levels of the genes in the CRC signature and the specifc CRC-related biomarkers between the two risk stratifcations.\u003cstrong\u003e（A）\u003c/strong\u003ethe highlight \u0026nbsp;expression MMRGs associated with tumor location;\u003cstrong\u003e (B)\u003c/strong\u003ethe highlight \u0026nbsp;expression MMRGs associated with pathologic stage; \u003cstrong\u003e(C)\u003c/strong\u003ethe highlight \u0026nbsp;expression MMRGs associated with perineural invasion ; \u003cstrong\u003e(D)\u003c/strong\u003ethe highlight \u0026nbsp;expression MMRGs associated with venous invasion (E)the highlight \u0026nbsp;expression MMRGs associated withlymphatic invasion.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4301530/v1/a8d7d6a0248f67ad7027624a.jpg"},{"id":55769833,"identity":"484fba61-5f8f-410f-9b4c-232fc57241e6","added_by":"auto","created_at":"2024-05-02 20:49:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":300353,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier plots depicting correlation of MMRGs expression with OS in CRC.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4301530/v1/4e9d773f6a997b430c7a6427.png"},{"id":55770205,"identity":"237a8f76-268d-4727-87a2-e073d2fee5da","added_by":"auto","created_at":"2024-05-02 20:57:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":245273,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between immune cell infiltration risk scores and MMRGs gene expression were visualized on the different tool software.\u003cstrong\u003e(A)\u003c/strong\u003eThe results from software CIBERSORT;\u003cstrong\u003e(B)\u003c/strong\u003eThe results from software xCell（v1.1.0）;\u003cstrong\u003e(C)\u003c/strong\u003eThe results from software estimate（v1.0.13） ;\u003cstrong\u003e(D)\u003c/strong\u003eThe results from software GSVA (v1.42.0) .\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4301530/v1/0025846626f6f1f0087dbf30.jpg"},{"id":55769261,"identity":"51d91284-d38e-4cf3-83ee-22d3f806c8ce","added_by":"auto","created_at":"2024-05-02 20:41:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":738570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe plot of correlation analysis of MMRGs;\u003cstrong\u003e(B)\u003c/strong\u003eThe molecular interaction networks\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4301530/v1/ea3c5d7c10558d035d829e83.png"},{"id":55769266,"identity":"1059eeca-fb2c-43c6-8621-9f985438ba84","added_by":"auto","created_at":"2024-05-02 20:41:26","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":428849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e A heatmap displays the differences in the expression levels of the ten genes between the high-risk and low-risk groups of TCGA-CRC set and GSE39582 set.（B）Model scores associated with patient survival status (OS) and survival time (OS time) of TCGA-CRC set and GSE39582 set.\u003cstrong\u003e（C）\u003c/strong\u003eSurvival analysis indicates that in both the TCGA-CRC set and GSE39582 set, patients with high-risk scores have signifcantly worse prognoses compared to those with low-risk scores.\u003cstrong\u003e ( D)\u003c/strong\u003e AUC curves of 1/3/5 year survival of patients in the TCGA-CRC set and GSE 39582 set.\u003cstrong\u003e(E) \u003c/strong\u003eUnivariate Cox regression analysis results of risk score and clinical factors.\u003cstrong\u003e(F)\u003c/strong\u003eMultivariate Coxregression analysis results of risk score and clinical factors.\u003cstrong\u003e(G)\u003c/strong\u003eThe plot between MMRGS and other clinical features in the TCGA-CRC set and GSE39582 set.\u003c/p\u003e","description":"","filename":"Figure701.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4301530/v1/626a00acb6cf0de43221bd9e.jpg"},{"id":58803147,"identity":"b74900d4-ba10-4b13-92c5-4a0332b73a5c","added_by":"auto","created_at":"2024-06-21 10:09:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3106983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4301530/v1/687ade6d-a27d-42c2-a996-a8c8260e959d.pdf"},{"id":55769268,"identity":"3acb5ae6-c196-4f4b-b8bc-9be67118d492","added_by":"auto","created_at":"2024-05-02 20:41:27","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":62038,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4301530/v1/0f9e56734f4960e74e41c918.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of Colorectal Cancer Prognostic Model Utilizing Mitochondrial Energy Metabolism-Related Genes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the second significant contributor to cancer-related mortality globally . \u0026nbsp;The high incidence and mortality due to its related with bad lifestyles , unhealthy dietary factors ,smoking , alcohol consumption genetic and other factors(1-2)While with the varity development of clinic treatments ,the incidence of CRC has been declining in recent years, there has been a significant increase in the \u0026nbsp; death rate of \u0026nbsp;CRC individuals in \u0026nbsp;China(3).In light of this, researchers persist in undertaking novel studies to enhance the efficacy of CRC treatment \u0026nbsp;and prognostic outcomes for individuals .In many studies,advanced clinical manifestation and molecular characteristics is related with \u0026nbsp; colorectal cancer(1-2),including biomarkers ,mutations , methylation,Cellular senescence or signals biomarker signatures（4-6）.\u003c/p\u003e\n\u003cp\u003eCellular senescence was demonstrated that was connected with CRC or changed of metabolic pathways to inhibit cancer progression(6).Senescent cells are \u0026nbsp; characterized by need sufficient energy like mitochondrial \u0026nbsp;to synthesize various metabolish pathway(7).Recently studies have shown that Mitochondrial disorders have been discoved in varity tumor cells and have demonstrated various mitochondrial proteins as pathological mechanism part \u0026nbsp;to \u0026nbsp;play a important role(8).In support of this,To change the number of mitochondrial content and activity, can affect the growth of \u0026nbsp;cell differentiation and \u0026nbsp;toward oxidative phosphorylation in metabolism(8).The mitochondrial energy metabolism pathway can regulate stem cell functions through many mechanisms, including citrate cycle tca cycle,fatty acid metabolism , ketone body metabolism , glycolysis gluconeogenesis, oxidative phosphorylation(9).Mitochondrial dysfunction was demonstrated that was \u0026nbsp;involved in cellular senescence,and may changed the metabolic pathways to favor carcinogenesis(10).Such as a high expression gene GAPDHs \u0026nbsp;in the mitochondrial glycolytic pathway \u0026nbsp;was associated with worse clinical outcome in stage III melanoma (10). Accumulating evidence has proven molecular testing can help to diagnose and intervent CRC, improve patient prognosis, reduce mortality, and lower the economic burden of disease on individuals(5,11). Hence, investigations into the metabolic aspects of mitochondrial are anticipated to offer valuable insights for prognostic predictions and optimization of treatment strategies for individuals with CRC.\u003c/p\u003e\n\u003cp\u003eNevertheless, at present, there is a lack of research exploring the correlation between mitochondrial energy metabolism pathway-related genes (MMRGs) and the prognosis of CRC patients. Therefore, differentiating clustering features and establishing features related to MMRGs may be effective in predicting the prognosis and immunotherapy response of CRC patients.We plan to utilize CRC samples from public databases to study the linkage between MMRGs and clinical features and prognosis of CRC. We established a prognostic model through regression analysis and conducted an independent analysis of the prognosis. We also conducted \u0026nbsp;tumor mutation analysis, immunotherapy response, and chemotherapy drug sensitivity analysis, aiming to contribute additional data support toward improving the prognosis and treatment options available for individuals with CRC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData source and acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTCGA-COAD was downloaded in the TCGA database \u0026nbsp;from University of California Santa Cruz (UCSC) was used to get the latest clinical follow-up information and corresponding STAD, COAD, and READ gene expression data . We downloaded GSE39582 data from National Center for Biotechnology Information (NCBI), containing 562 samples, respectively. The following steps were performed for pretreatment: 1) Remove samples with no clinical data or uncompleted data. 2) Remove samples of normal tissue. 3) Remove the gene with a transcript per million (TPM) \u0026lt;0.01 in half the samples.The raw dataset count and the corresponding clinical information was showed in Table1 and Table S1.\u003c/p\u003e\n\u003cp\u003eMitochondrial energy metabolism pathway-related genes(MMRGs) were derived from KEGG and MsigDB databases（Version 7.5.1）. We downloaded MMRG \u0026nbsp;corresponding to pathways containing keywords such as KEGG_GLYCOLYSIS_GLUCONEOGENESIS, KEGG_CITRATE_CYCLE_TCA_CYCLE, KEGG_OXIDATIVE_PHOSPHORYLATION, REACTOME_KETONE_BODY_METABOLISM, KEGG_FATTY_ACID_METABOLISM, respectively. It was verified that the differentially gene expression level of mitochondrial metabolism in rectal canner data obeyed a normal distribution, subsequently, we matched the clinical feather and gene expression (include genomic alteration\u0026nbsp;、\u0026nbsp;methylation analysis\u0026nbsp;、survival expression\u0026nbsp;、immunotherapy response) , and then verificate \u0026nbsp;the independent risk factor and classifed those differentlly express genes \u0026nbsp;with \u0026nbsp; expression above the average level as high expression group, and those differently express genes with \u0026nbsp;expression below the average level as low expression group to construct nomogram predict the dignosis value.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 The raw dataset count and the corresponding clinical information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"450\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eTCGA-CRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eGSE39582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003estage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003egender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor location1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eLSCRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eRSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor location2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eDistal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eProximal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphatic invasion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eDeceased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eLiving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMitochondrial energy metabolism pathway-related genes(MMRGs) express analysis of \u0026nbsp;rectal cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential analysis of the GEO dataset and TCGA-CRC dataset (log2(TPM + 1)) was conducted to screen the DEGs using the R ``limma\u0026apos;\u0026apos; package with FDR \u0026lt; 0.05 as the threshold. According to the type of dataset ,divide the sample into adjacent and cancerous tissues, and calculate the differentially expressed genes using the Bayes method of limma (V3.50.3) package. Data between the two groups were compared using the Wilcoxon rank sum test. The R clusterProfiler（V4.2.2）\u0026quot;pheatmap\u0026quot; and ``ggplot2\u0026prime;\u0026apos; software packages were used to plot heat maps and GSEA anylysis to\u0026nbsp;explore and understand the mitochondrial metabollism in rectal cancer.All analyses in this study were performed under R cluster Profiler version 4.2.2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic alteration and methylation analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording the MMRG, we used\u0026nbsp;maftools（v2.10.05）to analyze the genomic alterations of\u0026nbsp;\u0026nbsp;mitochondrial metabolism in rectal canner\u0026nbsp;, evaluate the expression of MMRG correlated with copy-number variation (CNV) and DNA methylation, and used\u0026nbsp;ggplot2（v3.3.5）\u0026nbsp;Fisher\u0026rsquo;s\u0026nbsp;statistical test method to visualize the genomic alteration of DEGs. The DEGs was selected for genomic profle change analysis, including mutations, putative CNAs from genomic identifcation of signifcant targets\u0026nbsp;of\u0026nbsp;mitochondrial metabolismin\u0026nbsp;rectal canner, and structural variants. Additionally, we analyzed the Spearman\u0026rsquo;s correlations between DNA methylation level and its expression in MMRG by using R ChAMP\u0026nbsp;package（version 2.26.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis between MMRG and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the MMRG ,we used T test method to assess the differentially gene expression level between paracancer and cancerous tissues;We used Kruskal-Wallis rank test \u0026nbsp;to value the connection between DEGs and clinical characteristics(including Age, Gender, Grade,BMI, histological type，TNM stage, \u0026nbsp;Tumor stage,and survival) .We categorized DEGS into the high-risk group and the low-risk group, based on the median values of gene expression values and to calculate the difference between two group by using software R\u0026nbsp;survival package（v3.3-1）. Then,the K-M curve was drawn \u0026nbsp;to evaluate the prognostic value of the risk model by using software R survminer\u0026nbsp;package（v0.4.9）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune microenvironment and immune relative analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate whether the \u0026nbsp;immune microenvironment \u0026nbsp;relative with MMRG and has the ability to assist in affecting mitochondrial metabolism pathway in rectal canner.We used softwareCIBERSORT, GSVA（v1.42.0）,xCell（v1.1.0）and estimate（v1.0.13）\u0026nbsp;to calculate the correlation between the expression of the MMRG core genes and the immune infiltration score. Further research was conducted to determine whether the signature affects the clinical outcomes of patients with comparable immune infiltration gene expression levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Constructure and Validate MMRGs differentilly gene express signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to understand the gene and protein interaction networks of MMRG were constructed using online \u0026nbsp;STRING database (http://string-db.org) and using software STRINGdb（v2.6.5） to complish the gene-set enrichment analysis to understand the biological processes of MMRG. In order to achieve the differentially gene express signature ,we used software glmnet（v4.1-4）to constructure LASSO-Cox model.Further,the differentially gene express signature of MMRG was identified the independence of the clinical factors(P value\u0026lt;0.05 ) (age、stage、gender、tumor location、perineural invasion、venous invasion、lymphatic invasion)and the prognostic risk model \u0026nbsp; through the univariate Cox regression analysis and the multivariate Cox regression analysis by software R packagesurviv（v3.3-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug sensitivity assessment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrug sensitivity data for rectal canner patients is available in the The Cancer Therapeutics Response Portal database (http://portals.broadinstitute.org/ctrp/) and Genomics of Drug Sensitivity in Cancer(https://www.cancerrxgene.org/). the drug response \u0026nbsp;and the relationship of differentially gene express signature was presented by the half-maximal inhibitory concentration (IC50/AUC) which used the R software. The results have appeared in box plots.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysis was conducted in the R environment (version 4.1.0). The Student\u0026apos;s T test or one-way ANOVA was used to analyze group differences in variables with normal distribution; the chi-square test was used to compare differences between categorical variables; and the Wilcoxon rank \u0026nbsp;test was used to compare group differences in variables with unknown distribution.Unless otherwise stated, P value\u0026lt;0.05 was regarded as statistically significant.* represent p \u0026lt;0.05, * *represent p \u0026lt;0.01, * * *represent p \u0026lt;0.001, * * * * represent p \u0026lt;0.0001and ns, p \u0026ge; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of MMRGs between adjacent and cancerous tissues\u003c/h2\u003e \u003cp\u003eBased on TCGA-CRC expression profile data, we used the R limma package gene set enrichment analysis (GSEA) to compare the difference between the adjacent and cancerous tissues of mitochondrial energy metabolism gene expression, and the genes were ranked according to the statistical value (logFC), and the unregulated genes ranked before and downregulated ranked the last. Then, enrichment analysised the preranked genes. The results showed that a total of 114 Mitochondrial energy metabolism pathway-related genes (MMRGs) was obtained and all pathways(including Citrate cycle tca cycle, Fatty acid metabolism, Ketone body metabolism, Glycolysis gluconeogenesis, Oxidative phosphorylation) were significantly enriched (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05),and most pathways were concentrated in upregulated regions, these genes were significantly more expressed in adjacent tissues than cancerous tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-E). These analysis results suggested that mitochondrial energy metabolism activity was reduced in cancer cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of the association between SNV ,CNV ,methylation and different clinical features based on MMRGs\u003c/h3\u003e\n\u003cp\u003eOur study of somatic alteration analysis was TCGA-CRC MMRGs by using software maftools(v2.10.05).The gene base mutations replace model almost was C\u0026thinsp;\u0026gt;\u0026thinsp;T、T\u0026thinsp;\u0026gt;\u0026thinsp;C、C\u0026thinsp;\u0026gt;\u0026thinsp;A,and the proportion of base transition is significantly higher than base tranversion(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Only OGDHL and PC gene sets in the MMRGs have associated gene mutations, which are generally rare with frequencies ranging 10%, where OGDHL has the highest mutation frequency(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) .We obtained differentially methylated sites (DMP) significantly enriched by using the R package ChAMP (V2.26.0), along with their corresponding MMRGs related target genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Differentially methylation sites were screened against a threshold deltaBeta\u0026thinsp;\u0026gt;\u0026thinsp;0.1 and a corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKruskal-Wallis Rank sum test was used to analyze the correlation between MMRGs expression level and clinical characteristics (age, tumor stage, tumor location, lymphatic invasion, perineural invasion, venous invasion). Based on the results, the clinic opathological characteristics were depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, highlighting signifcant diferences in tumor location, T stage,pathologic stage,perineural invasion ,venous invasion ,lymphatic invasion between two risk stratifcations of the TCGA-CRC (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addtion, The number of the MMRGs specific biomarkers were 28 in tumor location,and the highlight expression of MMRGs biomarkers were ACOX1, NDUFV2, OGDHL, PCK1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA); the number of MMRGs specific biomarkers associated with pathologic stage were 64,the high expression of MMRGs specific biomarkers were ACADM, ACADS, ACADSB (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB); the number of MMRGs specific biomarkers associated with perineural invasion were 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), the number of MMRGs specific biomarkers associated with venous invasion were 12,and the highlight expression of MMRGs biomarkers were ACADSB, ATP6V0C, COX6B2, MT-ND4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) the number of MMRGs specific biomarkers associated with lymphatic invasion were 36, and the highlight expression biomarkers were UQCRC1, SUCLG1, SDHB, ATP6V1G2, COX5A, ACADSB (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSurvival analysis of the expression levels of the MMRGs\u003c/h3\u003e\n\u003cp\u003eBased on the median value of the gene expression,samples were divided into high and low expression groups to delve deeper into the potential prognostic value of MMRGs, and we utilized R package survival to analyze their association with patient survival across CRC. The results, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and supplementary revealed a complex landscape of relationships between MMRGs expression and survival outcomes. Overall survival (OS)showed different prognostic effects depending on the different gene. In this results the high expression of PGAM2,COX7A1a and ATP6V1G2 predicted poorer overall survival (OS) in MMRGs.And the opposite result is the low expression of other MMRGs were associated with poorer overall survival(OS).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEvaluation of MMGRs and Immune enviroment Levels in TCGA-CRC gene set\u003c/h3\u003e\n\u003cp\u003eWe assessed any possible association between the risk model and immune-associated biomarkers and observed a significant relationship by using online software CIBERSORT, xCell(v1.1.0), estimate༈v1.0.13༉ and the ssgsea method of GSVA (v1.42.0) .MMRGs in TCGA-CRC gene set were assessed to observe whether there was an association between MMRGs expression and immune infiltration microenvironment. The results of online software CIBERSORT showed that Spearman\u0026rsquo;s correlation analysis revealed that the most MMRGs expression was positively associated with immune infiltration, including Plasma cell, resting mast cell infiltration ; In contrast, this group was negatively associated with activatedMast cells, macrophages M0, neutrophils, initial CD4\u0026thinsp;+\u0026thinsp;T cells, and memory-activated CD4\u0026thinsp;+\u0026thinsp;T cellsion risk cores in the tumor(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.A) .Similar results were obtained by software xCell༈v1.1.0༉.As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, MMRGs had a significantly higher level of correlation between immune matrix score and immune score was found by following estimate (v1.0.13) software (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e .B) .Furthermore,based on the TCGA-CRC gene expression data and the manually collected immune infiltration gene set (PMID: 30594216), the immune infiltration status of TCGA-CRC patients was calculated using the ssgsea method of GSVA (v1.42.0),the results showed that most of the MMRG core gene expression showed a high positive correlation with the immune infiltration scores of 29 immune cells, but a small number of mitochondrial genes were negatively correlated with the immune infiltration scores of immune cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.D).These results suggested that we observed a significant relationship between MMGRs and various immune cells levels and the immune enviroment may affect the the expression of MMRGS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMolecular networks and functional enrichment of MMRGs\u003c/h2\u003e \u003cp\u003eTo unlock the MMRGs family\u0026rsquo;s functional roles, we explored their molecular networks and enriched pathways using bioinformatic tools. Using STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we predicted protein-protein interaction networks and analyzied this combined network, revealing likely partners for MMRGs, respectively. By analyzing this combined network, we unraveled enrichment patterns in biology process analysis.The results showed showed positive correlation between most MMRGs, but the correlation between mitochondrial genes and nuclear genes was low and negative correlation, and there were strong positive correlation between mitochondrial genes(P\u0026lt;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).The protein interaction network diagram of MMRGs showed that the type of genes are clearly divided into three parts. The first part is several proteins related to ATP enzyme hydrogen ion transport, which have interaction with other parts; the second part is ubiquinone redox-related protein, whose protein interaction is complex, and the third part is other proteins, with no obvious rules(P\u0026lt;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).Furthermore,the results of MMRGs enrichment analysis showed that genes are mainly enriched in metabolic related of generation of precursor metabolites, energyand oxidation-reduction process,drug metabolic process,cellular respiration,small molecule metabolic process(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe enrichment analysis results of the MMRGs in biology progress\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eterm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003efdr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO.0006091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egeneration of precursor metabolites and energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 of 400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96E-107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO.0055114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoxidation-reduction process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 of 932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62E-101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO.0017144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edrug metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 of 622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.66E-88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO.0045333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecellular respiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 of 153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44E-87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO.0044281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esmall molecule metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 of 1779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.52E-82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the Prognostic Model\u003c/h2\u003e \u003cp\u003eTo examine the MMRGs express for the prognostic prediction of rectal cancer, 23 MMRGs of TCGA-CRC, which exhibited associations with overall survival in rectal cancer patients, underwent LASSO-Cox regression analysis. We tested the reliability of the prognostic risk score model in the GSE39582 data set. This analysis led to the establishment of a signature comprising ten genes, as follows:MMRGS\u0026thinsp;=\u0026thinsp;ACOX1 expression * -0.12684\u0026thinsp;+\u0026thinsp;ATP6V1G2 expression * 0.36042\u0026thinsp;+\u0026thinsp;COX7A1 expression * 0.02761\u0026thinsp;+\u0026thinsp;CPT2 expression *-0.07471\u0026thinsp;+\u0026thinsp;DLAT expression * -0.03181\u0026thinsp;+\u0026thinsp;ECGS1 expression * 0.06075\u0026thinsp;+\u0026thinsp;ECI2 expression * -0.0480\u0026thinsp;+\u0026thinsp;NDUFA1 expression * 0.03525\u0026thinsp;+\u0026thinsp;PPA2 expression * -0.0706\u0026thinsp;+\u0026thinsp;SUCLG2 expression * -0.028727. The TCGA-CRC and GSE39582 MMRGs, was used to cut-of median score value to classify all cases into high- and low-risk groups. Expression levels of these ten candidate genes of TCGA-CRC and GSE 39582 in high- and low-risk groups were visualized in a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) .Based on LASSO-Cox regression model,the higher MMRG scores the lower survival status and survival time in TCGA-CRC and GSE 39582 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).Kaplan\u0026ndash;Meier survival analysis demonstrated the same result of TCGA-CRC set and GSE39582 of rectal cancer patients with higher risk scores experienced worse prognosis(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). AUC values of TCGA-CRC set under the ROC curve were 0.678 at 1 year, 0.669 at 3 years, and 0.764 at 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD),and AUC values of GSE 39582 set under the ROC curve were 0.5 at 1 year, 0.577 at 3 years, and 0.562 at 5 years ,both results indicating that the prognostic model of MMRGs had a good predictive ability.\u003c/p\u003e \u003cp\u003eTherefore,We used R package survival(v3.3-1) to evaluate the independence of the risk model, we performed a univariate Cox regression analysis and a multivariate Cox regression analysis on clinical factors and risk score. As shown in ( Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE,F )we established a nomogram based on these independent factors and demonstrated that the risk score, stage and lymphatic invasion can serve as the independent prognostic factors༈p\u0026thinsp;\u0026lt;\u0026thinsp;0.05༉. This reaffirms the influence of varied MMRGs expression patterns on prognosis and supports the use of signature-derived risk scores as clinical prognostic factors.In the GSE39582 data set, MMRGS was significantly associated with tumor stage and chromosomal instability status (CIN)(P\u0026lt;0.05)༈Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG༉. Patients with advanced tumor stage and positive chromosomal instability had higher MMRGs scores.These findings aligned with the aforementioned survival analysis, indicating that an elevated risk score is associated with tumor stage, implying an unfavorable prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrediction of the MMRGs therapeutic response\u003c/h3\u003e\n\u003cp\u003eTo identify potential anti-CC agents, we searched the The Cancer Therapeutics Response Portal database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://portals.broadinstitute.org/ctrp/\u003c/span\u003e\u003cspan address=\"http://portals.broadinstitute.org/ctrp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Genomics of Drug Sensitivity in Cancer(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for potentially sensitive and selective chemotherapeutic agents. According to providing the half-maximal inhibitory concentration (IC50) as a measure of drug sensitivity, lower IC50 values indicate greater sensitivity to treatment. According to drug sensitivity of TCGA and GEO MMRGs to drugs in GDSC database, different reactive drugs were plotted and listed according to KruskalWallis test, respectively. We selected and visualized drugs with a statistically significant difference in sensitivity between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) .Among them, nine drugs, including BCL-LZH-4, MG-132, MI-2, BRD-K99006945, Trametinib, RDEA119, 17-AAG,RDEA119, PD-0325901, exhibited positive correlations between IC50 values and MMRGs expression levels, indicating higher sensitivity in cells with higher MMRGs expression. Conversely, ten drugs, such as ML210, SB-225002, SRT-1720, Fingolimod, Cytarabine Hydrochloride, ABT-263, BX-912, Tubastatin A, GSK429286A, GSK1070916, showed negative IC50 correlations with MMRGs expression, suggesting lower sensitivity when MMRGs levels were elevated(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have demonstrated that DEGs between tumor and peritumor tissues in patients with CRC can be used to assess patient prognosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Consistent with this, the present study found that MMRGs was obtained between tumor and peritumor tissues and all pathways(including Citrate cycle tca cycle,Fatty acid metabolism,Ketone body metabolism,Glycolysis gluconeogenesis,Oxidative phosphorylation) were significantly enriched (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003ePrevious research has found that mutations and methylaton in DNA are commonly found in cancer cells and regulated tumor metabolism and progression to promote the tumor of bioenergetics and biosynthesis(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).In our study,the same results is that we obtained mutations sites and differentially methylated sites (DMP) significantly enriched by using the R package ChAMP (V2.26.0), along with their corresponding MMRGs related target genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Increasing evidence has shown that the different regions of CRC originate from different factors, the rate of different anatomical regions of colorectal cancer associated with embryonic origins ,prognostic factors ,clinical presentations(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).In our study, Kruskal-Wallis Rank sum test ,immune infiltration analysis and protein-protein interaction (PPI) network identified key immune signature gene sets associated with CRC, and MMRGs expression level was associated with the clinical characteristics (age, tumor stage, tumor location, lymphatic invasion, perineural invasion, venous invasion,survival rate).This finding aligns with previous reports this study reveals unbalanced distribution of genes in the immune environment, the previous reported that the migration of immune cells was coordinated with infiltration ,we hypothesize that the immune enviroment may affect the the expression of MMRGS,and this process might regulate in tumor micro environment(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo better predict the risk of mortality and prognosis of patients with CRC, the present study selected 114 MMRGs from TCGA datasets of patients with CRC, and GEO datasets for the validation results.From the result ,We identified 10 prognostic genes (namely ACOX1, ATP6V1G2, COX7A1, CPT2, DLAT, ECGS1, ECI2, NDUFA1, PPA2 ,SUCLG2) based on singlefactor and multifactor Cox analyses. Therefore a prognostic model was built using the comprehensive risk scores of the aforementioned 10 genes, and the results showed that the predictive effectiveness of the model was good when using GEO datasets. Consistent with this, the clinical data showed that the risk scores of these 10 genes were independent predictive factors when using both TCGA and GEO datasets.Previous studies have found that ACOX1 is significantly regulated in CRC tissues and dephosphorylation of ACOX1 promotes β-catenin palmitoylation to drive colorectal cancer progression(18).ATP6V1G2 was associated With Polymorphic Variations in Breast Cancer Patients(19). COX7A1 may connected with methylation and suppressed the viability of human non-small cell lung cancer cells via regulating autophagy(20\u0026ndash;21).The regulation of CPT2 was found downregulation in cancer tissue(22\u0026ndash;23).The downregulation of CPT2 promoted proliferation and inhibited apoptosis through p53 pathway in colorectal cancer(23).And CPT2 downregulation triggered stemness and oxaliplatin resistance in colorectal cancer via activating the ROS/Wnt/β-catenin-induced glycolytic metabolism (24).DLAT as an immunological and prognostic biomarker correlated with Prognosis, Chemoresistance, and Immune Infiltration in variety of cancer(25\u0026ndash;26).A previous study revealed ECI2 was connected with epigenetic and genetic changes ECI2 in high-grade serous ovarian carcinoma(27).Downregulation of NDUFA1 and other oxidative phosphorylation-related genes was a consistent feature of basal cell carcinoma(28).PPA2 was connected sudden cardiac death and the Mitochondrial Inorganic Pyrophosphatase (29).SUCLG2 participated in epigenetic expression and regulated cancer express(30).According this results,ATP6V1G2, ECI2, COX7A1, SUCLG2 was connected with epigenetic regulation.And the expression of ACOX1, PPA, NDUFA1 may regulate the mitochondria-related pathways in CRC.Therefore, according to drug sensitivity of TCGA and GEO MMRGs ,We found drug target prediction of the model's 10 genes, evaluated the potential therapeutic value of these genes, and screened for possible drug .\u003c/p\u003e \u003cp\u003eThese results suggested that MMRGs was a critical gene affecting the proliferation and migration of tumor cells in CRC. In conclusion, the present study developed and constructed a prognostic model with potential clinical application. The functional role of the key gene, ACOX1, ATP6V1G2, COX7A1, CPT2, DLAT, ECGS1, ECI2, NDUFA1, PPA2 ,SUCLG2 in CRC was identified, providing evidence for its potential application as a prognostic biomarker and therapeutic target in CRC. However, the present study had some limitations.Our study may have inherent biases and inaccuracies,lack external experimental to validate ATP6V1G2, ECI2, COX7A1, SUCLG2 can lead to CRC gene mutation or other change.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study provided a systematic analysis of mitochondrial energy metabolism-gene in colorectal cancer, covering their expression, clinical feature,prognostic significance, biological functions, immune microenviroment, survival prognosis, medicine sensitive. Notably, we identified mitochondrial energy metabolism as an essential factor in colorectal cancer, playing a crucial role as a key determinant of colorectal cancer prognosis. Our study provides new potential targets for prognosis and therapeutic prediction in colorectal cancer.The nomogram constructed based on the risk score and clinical variables is more benefcial for clinicians to assess the survival prognosis of CRC. These findings may have some limitation to consider, as our study being bioinformatics research based on data collected from public databases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eFinancial Disclosure\u003c/h2\u003e \u003cp\u003eThis study was supported by grants from by the Natural Science Foundation of Shen Zhen techology plan (JCYJ20210324135205016).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e(I) Conception and design: Guo Ping Sun. (II) Collection and assembly of data: Guo Ping Sun and Peng Zhu. (III) Data analysis and interpretation: Peng Zhu ,Zheng Hui Yang and Kai Wang. (V) Manuscript writing: Kai Wang.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eNone to declare.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll relevant data are within the manuscript and its Additional files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Clin Transl Med. 2023; 13 (9): e1361. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ctm2.1361\u003c/span\u003e\u003cspan address=\"10.1002/ctm2.1361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mitochondrial energy metabolism, Colorect cancer, Prognosis model, Tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-4301530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4301530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe objective of this study was to construct a prognostic model and medicine therapeutic response by utilizing mitochondrial energy metabolism-related genes (MMRGs), thus establishing a risk score for colorectal cancer (CRC). Based on the TCGA-CRC and GEO data set, MMRGs expression levels were identified by clustering analysis. 10 differential expression genes were used to construct RiskScore by Cox regression. GSE 39582 data set was used for validation. The clinical characteristics,survival characteristics,SNV,CNV,methylation, immune features, and potential benefits of chemotherapy drugs were analyzed for two risk groups. RiskScore was constructed based on the genes ACOX1, ATP6V1G2, COX7A1, CPT2, DLAT, ECGS1, ECI2, NDUFA1, PPA2, and SUCLG2. Patients in the low risk group exhibited a superior overall survival. In addition, Univariate Cox regression analysis and Multivariate Cox regression analysis demonstrated that the risk score, stage and lymphatic invasion can serve as the independent prognostic factors.Trametinib exhibited positive correlations between IC50 values and MMRGs expression levels,which may be more sensitive to chemotherapy drugs. Mitochondrial Energy -Related Genes was a promising biomarker that can be used to distinguish CRC prognosis, immune features, and sensitivity to chemotherapy drugs.\u003c/p\u003e","manuscriptTitle":"Construction of Colorectal Cancer Prognostic Model Utilizing Mitochondrial Energy Metabolism-Related Genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 20:41:21","doi":"10.21203/rs.3.rs-4301530/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7935f39b-c15c-4535-9f75-bfe68ac6e15a","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31361969,"name":"Biological sciences/Biochemistry"},{"id":31361970,"name":"Biological sciences/Cancer"},{"id":31361971,"name":"Biological sciences/Chemical biology"},{"id":31361972,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2024-06-21T10:01:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 20:41:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4301530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4301530","identity":"rs-4301530","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}