
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39287898
1350
10.1007/s12672-024-01350-0
Analysis
Prognostic aging gene-based score for colorectal cancer: unveiling links to drug resistance, mutation burden, and personalized treatment strategies
Duan Ling 12
Xia Yang 23
Fan Rui 2
Shuai Yuxi 2
Li Chunmei 12
Hou Xiaoming houxiaoming19@sina.com

12
1 https://ror.org/05d2xpa49 grid.412643.6 Department of Oncology, The First Hospital of Lanzhou University, Lanzhou, Gansu China
2 https://ror.org/01mkqqe32 grid.32566.34 0000 0000 8571 0482 The First School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu China
3 Department of Hematology, The First People’s Hospital of Lanzhou, Lanzhou, Gansu China
17 9 2024
17 9 2024
12 2024
15 45413 8 2024
13 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Objective

Colorectal cancer (CRC) is characterized by high incidence and mortality rates worldwide. In this study, we present a novel aging-related gene-based risk scoring system (Aging score) as a predictive tool for CRC prognosis. Method: We identified prognostic aging-related genes using univariate Cox regression analysis, revealing key biological processes in CRC progression. We then constructed a robust prognostic model using LASSO and multivariate Cox regression analyses, including four critical genes: CAV1, FOXM1, MAD2L1, and WT1. Result: The Aging score demonstrated high prognostic performance across the training, testing, and entire TCGA-CRC datasets, proving its reliability. High-risk patients identified by the Aging score had significantly shorter overall survival times than low-risk patients, indicating its potential for patient stratification and personalized treatment. The Aging score remained an independent prognostic factor compared to age, gender, and tumor stage. Additionally, the score was linked to tumor mutation burden and microsatellite instability, indicators of immune checkpoint inhibitor response. High-risk patients also showed higher estimated IC50 values for common chemotherapeutic drugs, suggesting possible treatment resistance. Conclusion: Our findings highlight the Aging score's potential to enhance clinical decision-making and pave the way for personalized CRC management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01350-0.

Keywords

Aging-related genes
Risk scoring system
Prognosis
Colorectal cancer (CRC)
Personalized treatment
Natural Science Foundation of Gansu Province China22JR11RA024 Duan Ling Hospital Fund Project of the First Hospital of Lanzhou Universityldyyyn 2022-26 Duan Ling issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Colorectal cancer (CRC) represents a significant global health burden, being the third most common cancer and the second leading cause of cancer-related deaths worldwide [1–3]. It is characterized by a heterogenous nature, which translates into diverse clinical outcomes and responses to therapies [4, 5]. Despite substantial advancements in the understanding of the disease's genetic and molecular landscapes and improvements in early detection and treatment methods, the prognosis for patients with advanced-stage CRC remains dismal [6, 7]. This calls for an urgent need to explore new prognostic biomarkers and therapeutic targets, which could enable a more personalized approach to managing the disease.

Aging is an inevitable physiological process that is associated with progressive changes at cellular and systemic levels, leading to an increased risk of developing several diseases, including cancer [8, 9]. The intersection between aging and cancer is complex, with each process influencing the other at various levels [10]. Aging, characterized by genomic instability, telomere attrition, epigenetic alterations [11], loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication, creates a conducive environment for carcinogenesis and tumor progression [8, 12–14]. Therefore, a deeper understanding of the interplay between aging and cancer, particularly CRC, could shed light on novel strategies for cancer prevention, early detection, and treatment [15].

There is a growing body of evidence supporting the potential of gene expression profiling in cancer prognosis and treatment [16–18]. Several studies have reported gene expression signatures associated with prognosis, therapeutic response, and mechanisms of resistance in various types of cancers, including CRC [19–21]. Nevertheless, the interplay between aging-related gene signatures and CRC prognosis remains to be fully elucidated.

Therefore, in this study, we aim to investigate the role of aging-related genes in CRC and to develop an aging-related gene-based prognostic model for CRC. We hypothesize that an understanding of the interplay between aging and CRC could inform clinical decision-making and contribute to personalized patient management strategies, ultimately improving patient outcomes. This approach aligns with the burgeoning trend in oncology of shifting away from a 'one-size-fits-all' treatment strategy towards more patient-centric, personalized treatments. Our findings could have significant implications not only for the treatment of CRC but also potentially for other aging-related cancers.

Materials and methods

Data collection and processing

The data for this study was sourced from The Cancer Genome Atlas (TCGA) database, specifically from the TCGA Colorectal Cancer (TCGA-CRC) cohort. This cohort includes a diverse range of patient samples, encompassing both normal and tumorous tissues, providing a comprehensive dataset for analysis. To prepare the data for downstream analysis, all count data retrieved from the TCGA-CRC cohort were subjected to logarithmic transformation. This transformation compresses the wide range of values inherent in count data, making higher counts more manageable and amplifying lower counts. This adjustment enhances the clarity of patterns and trends within the data, facilitating more robust analysis. Our study focuses on aging-related genes, which play a pivotal role in the biological aging process and potentially in tumorigenesis. We curated a list of 279 such genes from the CellAge database (https://genomics.senescence.info/cells/index.html), a comprehensive resource dedicated to genes associated with cellular senescence.

Differential expression analysis

Differential expression analysis was performed using the limma package in R [22]. This approach allowed us to identify significant differences in the expression of aging-related genes between colorectal cancer and normal colorectal tissue.

Survival analysis

Univariate Cox regression analysis was conducted to associate differentially expressed genes with patient prognosis. This analysis enables us to identify genes acting as protective factors or risk factors for colorectal cancer prognosis.

Gene set enrichment analysis

The enrichment analysis of prognostically relevant genes was carried out using the clusterProfiler package in R [23]. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify the biological processes, cellular components, and molecular functions related to these genes [24].

Unsupervised Clustering

The Consensus Clustering methodology [25] was used for unsupervised clustering of TCGA-CRC samples. By comparing the consistency within clusters and differences between clusters, we determined the optimal number of subgroups for the samples.

Survival and enrichment analysis between subgroups

Survival rates between subgroups were compared using survival and survminer R packages. Moreover, the GSVA package in R [26] was used to perform GSVA enrichment analysis to identify significantly enriched signaling pathways in different subgroups.

Tumor immune microenvironment analysis

Single-sample Gene Set Enrichment Analysis (ssGSEA) and ESTIMATE algorithm were used to analyze the immune microenvironment composition of each subgroup. The TIDE score for each subtype was calculated and compared using TIDE web tool and ggpubr R package.

Regression model construction

The prognostically relevant genes were used to construct a regression model. The patient data was randomly split into a training set and a validation set. A combination of LASSO regression (implemented in the glmnet R package) and multivariate Cox regression was used to screen survival-related genes and build the prognostic model. The risk score for each patient was calculated based on the expression of these genes and their corresponding regression coefficients.

Gene expression analysis between high and low-risk groups

The expression levels of the genes included in the risk model were compared between high-risk and low-risk groups using the limma and pheatmap R packages. The validation of the model was performed using the training set, validation set, and the whole TCGA dataset.

Survival analysis

Kaplan–Meier survival data were used to investigate the relationship between Aging scores and overall survival (OS). The performance of the prognostic model was evaluated using timeROC to construct Receiver Operating Characteristic (ROC) curves, which aimed to differentiate between high-risk and low-risk patients based on a risk score.

Gene set enrichment analysis

Gene Set Enrichment Analysis (GSEA) was conducted on patients stratified into high-risk and low-risk groups based on the Aging score. This technique was applied to identify significantly enriched or depleted sets of genes in each risk group, providing insights into the biological pathways that differentiate these groups.

Immune infiltration and regulation analysis

Seven immune deconvolution methods included in TIMER 2.0—CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, EPIC, and TIMER—were employed to explore the immune cell infiltration among different risk groups.

Additionally, immune modulator data, comprising immunoinhibitors, immunostimulators, and MHC types, were retrieved from the online platform (http://cis.hku.hk/TISIDB/). These data were analyzed and visualized in a heatmap using the limma and pheatmap R packages.

The GSEABase method was applied for the analysis of the functional states of immune cells across different risk groups. The ssgsea method was utilized to examine the immune cell infiltration in different risk groups, and plots were created using the ggpubr package.

Expression of HLA-related genes and immune checkpoints

The expression of common immune checkpoints and HLA-related genes across the risk groups was analyzed using the ggpubr package.

Subgroup analysis

Subgroup tests were conducted to assess the prognostic value of Aging scores among patients with varying clinical characteristics, such as age, gender, stage, and TNM staging. The survival package in R was utilized to construct Kaplan–Meier survival curves, aiming to understand the prognostic relationships between different clinical features across varying groups.

Univariate and multivariate cox regression analysis

Univariate and multivariate Cox regression analyses were conducted to explore the independent prognostic value of Aging in comparison with other typical clinical pathological variables. The RMS package in R was used to create a dynamic nomogram model with Aging risk score parameters for the prediction of overall survival. The accuracy of the nomogram was validated using calibration curves, the C-index, Decision Curve Analysis (DCA), and ROC curves. These were all created using the regplot and timeROC packages in R.

Tumor mutation burden (TMB) analysis

The relationship between TMB and Aging score was analyzed using the maftools package [27]. Differences in TMB scores between the high and low-risk groups were assessed using the limma package in R, and violin plots were created using the ggpubr package to display the distribution of TMB scores across the groups.

Survival curves were drawn with the survival package in R to analyze the correlation between different TMB groups, different risk groups, and patient prognosis.

Microsatellite instability (MSI) and TIDE analysis

The MSI status between the two risk groups was also analyzed. Additionally, TIDE scores, expression of immune checkpoints, and aging phenotype-related genes were assessed for the different risk groups. TIDE scores were downloaded from the TIDE website (http://tide.dfci.harvard.edu/login/).

Analysis of chemotherapeutic drug response

Chemotherapeutic drug response was analyzed between the different risk groups using the oncoPredict package in R [28]. This analysis looked at common chemotherapy drugs such as 5-fluorouracil, docetaxel, and oxaliplatin.

Statistical analysis

Unless otherwise specified, all statistical analyses were conducted using R.

Results

Differential expression and enrichment analysis of aging-related genes

Differential expression analysis of aging-related genes in colorectal cancer and normal colonic tissues was performed using the limma package, revealing 23 downregulated and 54 upregulated genes, as represented in a heatmap (Fig. 1A). These differentially expressed genes were subjected to univariate Cox regression analysis, identifying 18 genes associated with prognosis in colorectal cancer patients—one as a protective factor and 17 as risk factors (Fig. 1B). Enrichment analysis of these 18 prognostic genes suggested involvement in biological processes such as replicative senescence and muscle cell migration, cellular components such as heterochromatin and chromosomal regions, and molecular functions such as insulin-like growth factor I binding and fibrinogen binding (Fig. 1C). KEGG pathway analysis revealed that these genes were primarily enriched in cellular senescence, the p53 signaling pathway, and transcriptional misregulation in cancer (Fig. 1D).Fig. 1 Analysis of differentially expressed aging-related genes in colorectal cancer A: Heatmap representing differentially expressed aging-related genes in colorectal cancer using the limma package. B: Univariate Cox regression of differentially expressed aging-related genes. C–D: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the selected module using the clusterProfiler package

Unsupervised clustering of TCGA-CRC samples and subsequent survival analysis

An unsupervised clustering analysis was conducted on 554 TCGA-CRC samples using the ConsensusClusterPlus package. Based on the cumulative distribution function (CDF) and cluster heatmap, optimal clustering was achieved with two subgroups, which showed maximum between-group differences and minimum within-group differences (Fig. 2A–B). A differential analysis of aging prognostic genes between these two subgroups revealed significant gene differences (Fig. 2C). Survival analysis indicated better overall survival for the Aging low subgroup (Fig. 2D). A GSVA enrichment analysis of these two subtypes in TCGA-CRC patients revealed major enrichments in chemokine signaling pathways, Jak-STAT signaling pathways, and the p53 signaling pathway, suggesting that Aging high patients are more likely to undergo aging-related signaling pathways (Fig. 2E). The Aging high group was more prone to death, later stage tumors, and were generally older (Fig. 2F).Fig. 2 Subtyping and Prognostic Analysis in Colorectal Cancer A–B: Unsupervised clustering of genes associated with prognosis in colorectal cancer patients. C: Differential analysis of prognostic genes based on the two subtypes using the limma and pheatmap packages. D: Visual survival analysis of the two subtypes and genes associated with cellular aging prognosis using the survival and survminer R packages. E: GSVA enrichment analysis of the two subtypes using the clusterProfiler package in R; Differential analysis of the GSVA results between the two subtypes using the limma package. F: Pie chart comparing the clinical features of the two subtypes using the ggplot2 package

Analysis of immune microenvironment in different aging subtypes

An ssGSEA analysis between the different subtypes revealed higher levels of immune cell infiltration in the Aging high subtype (Fig. 3A). Furthermore, the ESTIMATE analysis revealed that the Aging high subtype had higher stromal, immune, and estimate scores, while the Aging low subtype had a higher tumor purity score, corroborating previous findings (Fig. 3B–E). The TIDE score analysis between the two subtypes showed a lower TIDE score for the Aging low subtype (Fig. 3F). There was no notable difference in immune subtypes between the two subtypes (Fig. 3G).Fig. 3 Immune Microenvironment Analysis in Aging Subtypes of Colorectal Cancer. A–E: Composition analysis of the immune microenvironment in the aging subtypes: ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) algorithms. F: Violin plot of the TIDE scores between the two subtypes using the ggpubr package. G: Analysis of the immune subtypes of colorectal cancer between the different subtypes using the RColorBrewer package. The major types of colorectal cancer include Wound Healing (Immune C1), Immunologically Quiet (Immune C5), IFN-gamma Dominant (Immune C2), Lymphocyte Depleted (Immune C4), Inflammatory (Immune C3), TGF-beta Dominant (Immune C6)

Development and validation of prognostic model using aging-related genes

The univariate analysis was performed on all aging-related genes to select those associated with prognosis. The TCGA-CRC patients were randomly divided into a training set and a validation set with a 1:1 ratio. Two selection methods were further utilized to filter survival-related genes: 1) LASSO regression (Fig. 4A–B) based on the optimal lambda value, retaining six genes; 2) Multivariate Cox regression analysis, which resulted in four genes (CAV1, FOXM1, MAD2L1, and WT1) for model construction. The risk score was calculated based on the expression levels and regression coefficients of these four genes as follows: risk score = CAV10.018 + FOXM10.013 + MAD2L1*(–0 .072) + WT1*0.387.Fig. 4 Selection of prognostic genes and development of the prognostic model. A–B LASSO regression analysis was performed to select the most valuable prognostic genes. C Risk scores were calculated based on the expression of prognostic genes and their coefficients in the prognostic model. D Patients' survival status distribution and their risk score distribution. E Heatmap of the expression profiles of the prognostic genes in high- and low-risk groups. F–H Expression of the prognostic genes CAV1, FOXM1, MAD2L1, and WT1 in high- and low-risk groups

Evaluation of prognostic model performance and differentiation of high and low-risk groups

Patients were classified into high and low-risk groups based on their risk scores. As the risk scores increased, survival rates decreased, corroborating the characteristics of the risk model (Fig. 4C, D, E). When comparing the expression of the four genes in the high and low-risk groups, CAV1, FOXM1, and WT1 were found to be overexpressed, while MAD2L1 was underexpressed in the high-risk group (Fig. 4F–H). These findings were consistent across the validation set, training set, and the entire TCGA data.

Survival differences and sensitivity analysis of the prognostic model

Statistically significant overall survival differences were observed between the high-risk and low-risk groups, with a p-value of < 0.05 (Fig. 5A, C, E). The ROC curves demonstrated that the risk scores accurately differentiated between high and low-risk patients, with an AUC value indicating a high degree of sensitivity (Fig. 5B, D, F).Fig. 5 The prognostic value of the risk score in the training and validation sets. A, C, E Kaplan–Meier survival curves for patients in the high- and low-risk groups in the training set, validation set, and combined TCGA-CRC set. B, D, F ROC curves showing the predictive efficiency of the risk score model in the training set, validation set, and combined TCGA-CRC set

Gene set enrichment analysis (GSEA) in different risk groups

The GSEA of patients in different risk groups showed that high-risk patients were primarily enriched in pathways associated with cell adhesion molecules (CAMs) and ECM-receptor interactions, whereas the low-risk group was primarily enriched in cell cycle and oxidative phosphorylation pathways (Fig. 6A, B).Fig. 6 GSEA enrichment analysis of different risk groups. A–B Significantly enriched KEGG pathways in high- and low-risk groups based on GSEA

Comparative immune landscape analysis in different risk groups

Seven deconvolution methods were used to depict the immune landscape in high and low-risk groups (Fig. 7A). Comparison of immune regulatory factors between the two groups revealed higher expression of these factors in the high-risk group (Fig. 7B). Comparative analysis of immune cells and functional pathways suggested differing immune cell subsets in the two risk groups, including checkpoints, HLA molecules, MHC class I, T cell co-inhibition, T cell co-stimulation, type I and II IFN responses, as well as B cells, iDCs, mast cells, neutrophils, NK cells, T helper cells, Th1, Th2, and Treg (Fig. 7C, D). The expression of the majority of immune checkpoints was significantly different between the two risk groups, suggesting the possible regulation of immune checkpoint inhibitors by the Aging score (Fig. 7E). Analysis of HLA-associated genes showed that these genes were overexpressed in the high-risk group (Fig. 7F).Fig. 7 Analysis of immune cell infiltration and immune checkpoint expression in different risk groups. A Immune infiltration profiles of high—and low-risk groups obtained using seven immune deconvolution methods. B Differential expression of immune regulatory factors between high- and low-risk groups. C Differential immune cell and immune function pathways between high—and low-risk groups. D Immune checkpoints inhibitor analysis in high—and low-risk groups. E Differential expression of immune checkpoints between high- and low-risk groups. F Differential expression of HLA-related genes between high- and low-risk groups

Prognostic relevance of aging score in subgroups defined by various clinical features

We performed subgroup testing to evaluate the prognostic significance of the Aging score in patients with different clinical characteristics, including age (Fig. 8A, B), sex (Fig. 8C–D), stage (Fig. 8E), and TNM classification (Fig. 8F–J). Our findings suggest that apart from Stage III-IV and T1 T2 M1 N2 N1, where no significant difference was observed, the high-risk group was associated with worse prognosis across all subgroups.Fig. 8 The prognostic value of the risk score in different subgroups of clinical features. A–J Kaplan–Meier survival curves showing the prognostic value of the risk score in different subgroups, including age, gender, and tumor stage

Single and multivariate cox regression analysis and prospective independence of aging score

We performed both univariate and multivariate Cox regression analysis to explore the prospective independence of Aging score as compared to other typical clinical pathological variables. The results suggest that the Aging score model has a high predictive ability for patient prognosis in practical applications (Figure S1A, B). A nomogram was created with the ‘‘RMS’’ R package using the Aging risk score parameters for overall survival prediction (Figure S1C). The nomogram's predictive efficacy was validated using calibration curves, DCA curves, and C-index (Figure S1D-H). The ROC curve (Figure S1I) suggested that the risk score had good performance in predicting the prognosis of colorectal cancer patients.

Analysis of tumor mutation burden and aging score

Next, we analyzed the relationship between tumor mutation burden (TMB) and the Aging score. The results revealed that the mutation frequency between the high and low-risk groups was not significantly different (Figure S2A, B). However, analysis based on TMB results showed that the TMB values for patients in the low-risk group were higher than those in the high-risk group (Figure S2C). Survival curve analysis combining Aging score and high/low TMB values showed that patients with high TMB values had a better prognosis than those with low TMB values. In the same TMB value groups, high-risk patients had a worse prognosis (Figure S2D, E). The comparison of MSI status between the two groups showed that patients in the low-risk group were more prone to microsatellite instability (Figure S2F, G).

An analysis of TIDE, immune checkpoint, and aging phenotype-related genes was conducted for patients in different risk groups. The results showed that the low-risk group had a lower TIDE score, higher expression of immune checkpoint, and higher expression of aging phenotype-related genes (Figure S2H–J).

Chemotherapy treatment analysis in different risk groups

Lastly, we analyzed the sensitivity to chemotherapy drugs between the high-risk and low-risk groups. The results indicated that the IC50 values of chemotherapeutic agents such as 5-fluorouracil, docetaxel, and oxaliplatin were significantly higher in the high-risk group compared to the low-risk group (Figure S3A–D). This increase in IC50 values suggests that patients in the high-risk group may exhibit greater resistance to these drugs, whereas those in the low-risk group are more likely to respond favorably to chemotherapy treatment.

Discussion

Cancer, an array of diseases with distinctive molecular, genetic, and clinical characteristics, stands as one of the leading causes of death worldwide [29, 30]. The vast heterogeneity within the cancerous phenotype accounts for its extensive complexity, making it exceedingly difficult to pinpoint effective therapeutic interventions that are universally applicable [6, 31, 32]. In recent years, CRC has been the focus of substantial investigation due to its widespread prevalence and substantial mortality rates [33, 34]. Existing research has unveiled some of the fundamental mechanisms underpinning the development of CRC [33, 34]. However, a more comprehensive understanding of the pathophysiology of CRC is required to illuminate promising therapeutic strategies and diagnostic markers.

The aging process presents a significant risk factor for the development of a multitude of cancers, including CRC [35]. While the intricate association between aging and cancer is becoming increasingly acknowledged, it remains only partially understood [36, 37]. Aging is characterized by a gradual decline in physiological function, attributed to a series of complex processes such as genomic instability, epigenetic alterations, loss of proteostasis, and disrupted intercellular communication, many of which also participate in tumorigenesis. Nevertheless, the majority of the existing research on this topic has been limited to the effects of individual genes or pathways involved in aging. The inherent complexity and multifaceted interplay between aging and cancer warrant a more integrative and systematic approach.

In response to this recognized need, our study undertook a comprehensive evaluation of the role of aging-related genes in CRC prognosis. By utilizing bioinformatic tools, we constructed a novel multi-gene prognostic model—termed the Aging score—that incorporates four aging-related genes: CAV1, FOXM1, MAD2L1, and WT1. The selection of these genes was rooted in rigorous statistical analyses, thereby emphasizing their collective relevance in determining the prognosis of CRC patients. Each of these genes has been independently implicated in the biology of various cancers, and their roles in CRC are particularly noteworthy. CAV1 (Caveolin-1) is known to influence tumor progression and metastasis through its involvement in cell signaling pathways and the regulation of cell proliferation. In CRC, CAV1 has been associated with tumor cell invasion and poor prognosis, possibly due to its role in enhancing epithelial-mesenchymal transition (EMT) [38]. FOXM1 (Forkhead Box M1) is a transcription factor that plays a critical role in cell cycle regulation and has been linked to CRC progression through the promotion of cell proliferation and resistance to apoptosis [39]. MAD2L1 (Mitotic Arrest Deficient 2 Like 1) is involved in the mitotic checkpoint, ensuring proper chromosome segregation, and its dysregulation in CRC has been associated with chromosomal instability and poor outcomes [40]. Finally, WT1 (Wilms Tumor 1) is a gene that, although traditionally known for its tumor suppressor function, has shown oncogenic properties in CRC, particularly through the modulation of growth factor signaling pathways [41]. The combined use of these genes in our prognostic model offers an innovative approach to enhancing the predictive accuracy for CRC patient outcomes, surpassing the limitations of models based on individual genes.

The superior predictive power of the Aging score relative to conventional clinical-pathological parameters serves as a testament to its potential utility. It aligns with an emerging consensus in cancer research, underscoring the value of integrating molecular information into traditional prognostic models to improve their predictive accuracy. The identification of a reliable and accurate prognostic model such as the Aging score is particularly beneficial, given the extensive heterogeneity of CRC. Such a model could aid in risk stratification, informing treatment decisions and potentially improving patient outcomes. It could also expedite the transition towards more personalized treatment regimens in the management of CRC.

Furthermore, we observed a striking association between the Aging score and two pivotal molecular characteristics of CRC—TMB and MSI [42, 43]. TMB and MSI have been acknowledged as predictive markers for response to immunotherapies, which constitute a rapidly evolving and promising domain in oncology. The correlation between the Aging score and these markers suggests a potential role for the Aging score as a supplementary marker in predicting response to immunotherapy, thus offering another dimension for personalizing treatment in CRC.

Another intriguing observation pertains to the relationship between the Aging score and the estimated IC50 values of various chemotherapy drugs. This association raises the possibility that the expression of aging-related genes might influence the response to chemotherapy. If validated, these findings could guide the selection of chemotherapy agents for individual patients based on their Aging score, thereby advancing personalized treatment in CRC.

Despite the promising findings of our study, some limitations need to be acknowledged. Firstly, the use of publicly available datasets may introduce inherent biases and limitations. Independent validation in diverse cohorts is necessary for the reproducibility and generalizability of our findings. Secondly, further evaluation in prospective clinical studies is needed to assess the clinical applicability and utility of the Aging score as a prognostic tool. Thirdly, additional functional experiments are required to elucidate the underlying mechanisms linking aging-related genes to CRC prognosis. Future investigations should explore the interactions with other molecular pathways and consider the influence of various factors on aging to obtain a comprehensive understanding of CRC pathogenesis. Lastly, conducting clinical trials and translational studies will be crucial in evaluating the clinical implications and therapeutic potential of the Aging score. By addressing these limitations, we can enhance the clinical relevance and utility of our findings, ultimately improving the management and outcomes for CRC patients.

In conclusion, our study highlights the prognostic significance of aging-related genes in CRC and introduces a novel multi-gene prognostic model, the Aging score, with promising predictive performance. The integration of aging-related genes into prognostic models holds great potential for improving risk stratification and personalizing treatment decisions in CRC. However, further validation and functional studies are warranted to establish the clinical utility and unravel the underlying mechanisms of aging-related genes in CRC. Our findings contribute to the growing body of evidence elucidating the intricate relationship between aging and cancer and offer valuable insights into the management of CRC patients.

Supplementary Information

Supplementary material 1. Figure S1: Validation of the prognostic value of the Aging risk score.Univariate and multivariate Cox regression analyses showing the independence of the Aging risk score from other clinical variables.Nomogram incorporating the Aging risk score for predicting overall survival.Nomogram incorporating the Aging risk score for predicting overall survival.Calibration curves, DCA curves, and C-index for evaluating the accuracy of the nomogram.ROC curve showing the predictive efficiency of the Aging risk score

Supplementary material 2.Comparison of the mutation landscape between high- and low-risk groups.Comparison of TMB scores between high- and low-risk groups.Kaplan-Meier survival curves showing the prognostic value of TMB scores and the Aging risk score.Comparison of MSI status between high- and low-risk groups.TIDE scores comparison between high- and low-risk groups.Differential expression of immune checkpoints and senescence phenotype-related genes between high- and low-risk groups

Supplementary material 3. Analysis of chemotherapeutic drug response between different risk groups.Comparison of the IC50 values of different chemotherapeutic drugsbetween high- and low-risk groups

Abbreviations

CRC Colorectal cancer

TCGA The Cancer Genome Atlas

GO Gene ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

ssGSEA Single-sample gene set enrichment analysis

OS Overall survival

ROC Receiver operating characteristic

GSEA Gene set enrichment analysis

DCA Decision curve analysis

TMB Tumor mutation burden

MSI Microsatellite Instability

CDF Cumulative distribution function

CAMs Cell adhesion molecules

Acknowledgements

Not applicable.

Author contributions

XH designed the study. LD, YX, RF, YS, and CL performed data analysis. LD drafted the manuscript. XH revised the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the Natural Science Foundation of Gansu Province China (22JR11RA024) and the Hospital Fund Project of the First Hospital of Lanzhou University (ldyyyn 2022–26).

Data availability

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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