Abstract
Background
There were not many studies on predictive indicators of elderly rectal cancer (ERC) based on transcriptome level. Immune related genes (IRGs) have been shown to be associated with cancer, however, their role in ERC had not been fully explored.
Methods
For the first time, IRGs associated with ERC prognosis were identified using univariate Cox regression analysis. Based on the screened IRGs, consistent clustering analysis and prognostic difference analysis were conducted and a prognostic model was constructed through multivariate Cox regression. The model was validated on an independent dataset. The patients were grouped based on the median risk score. Simultaneously conduct immune infiltration analysis, enrichment analysis and drug sensitivity evaluation.
Results
The prognostic value of IRGs in ERC patients had been comprehensively analyzed for the first time and identified 3 IRGs with prognostic values. Consensus cluster analysis based on 3 IRGs revealed that the two identified clusters had prognostic differences. A prognostic model was constructed based on 3 IRGs and validated in an independent dataset. The signature was an independent risk factor for ERC patients, 3 IRGs were related to the immune microenvironment of ERC.
Conclusions
This research provides a fundamental theoretical basis for guiding the treatment of ERC patients.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12672-026-04412-7.
Keywords: Elderly rectal cancer, Prognosis, Machine learning, Immune
Introduction
Rectal cancer was a common malignant tumor that imposes a great burden on patients’ lives [1]. In clinical practice, people aged 60 and above who suffer from rectal cancer were commonly referred to as elderly rectal cancer (ERC) [2]. The etiology of ERC was still not very clear, and its onset was related to social environment, dietary habits and genetic factors [2, 3]. As people age, their physical functions decline, their metabolic capacity for chemotherapy drugs decreases and their tolerance to toxic side effects also significantly decreases [4]. Given the increased serious adverse reactions associated with chemotherapy drugs, whether elderly patients should receive combination therapy containing oxaliplatin or monotherapy with fluoropyrimidine should depend on the patient’s prognosis evaluation [5]. However, there was currently no good prognostic tool for ERC patients in clinical practice. Therefore, there was an urgent need to actively study potential prognostic markers of ERC and explore therapeutic targets for ERC to improve the prognosis of ERC patients.
The interaction between tumor cells and tumor microenvironment has not been explored, especially in the context of cancer progression [6]. Although the immune system was initially developed to defend against pathogens, it was a key factor in the occurrence and progression of cancer [7]. The interaction between tumors and surrounding immune cells was constant and complex, leading to inhibition or stimulation of tumor growth [8]. At present, an immune disorder that can promote tumor development has been considered as a facilitating feature in the occurrence of ERC patients [9]. Meanwhile, some immune related parameters have been reported to predict disease prognosis, highlighting the importance of different immune states in identifying cancer outcomes [10]. However, the immune phenotype in the microenvironment of ERC and its relationship with prognosis have been rarely studied. Immune related genes (IRGs) analyses in ERC can provide us with a better understanding of the molecular mechanisms involved and provide us with new and more effective therapeutic approaches.
In this work, we systematically analyzed the prognostic value of IRGs in ERC patients and performed consensus clustering, signature construction, functional enrichment analysis of IRGs and their relationship with ERC immune microenvironment, which enriches the prognostic evaluation system and provides a novel therapeutic direction for ERC.
Materials and methods
ERC dataset preprocessing
According to research statistics, the median age for diagnosing rectal cancer was approximately 63 years old. Secondly, the World Health Organization defines elderly people as those aged 60 and above. In addition, a review of relevant literature indicates that 60 years old was a commonly used age threshold for studying elderly patients. Therefore, in clinical practice, people aged 60 and above who suffer from rectal cancer wereare commonly referred to as elderly rectal cancer (ERC) [2]. Two independent datasets (TCGA_READ and GSE87211), patients aged ≥ 60 years were selected for prognostic analysis, there are a total of 231 ERC samples. GSE87211 was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), and TCGA_READ dataset were obtained from the Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). TCGA_READ was served as training set, whereas GSE87211 was utilized as validation set for subsequent analysis. The raw data processing of microarrays and the evaluation of gene expression levels are carried out using the R 4.4.1 tool [11]. IMMPORT database (https://www.immport.org/home) was used to collect IRGs and 1793 IRGs were selected [12].
Identification of IRGs with potential prognostic value in ERC patients
Firstly, based on the TCGA_READ dataset, the survey_cutpoint function in the R package “surveyor” was used to divide 1793 IRGs in the ERC samples into high expression and low expression groups, and then determine the optimal threshold value for each gene. The youden index (specificity + sensitivity-1) was used to determine the optimal critical value, which aims to find the best classification point and minimize the p-value on the Kaplan Meier curve. The truncation values selected through this method can maximize the accuracy of classification while maintaining high specificity and sensitivity. Use univariate Cox regression to select IRGs that are significantly correlated with survival rate, validated using GSE87211 dataset. Overlapping molecules of genes associated with survival and IRGs are considered candidates. The Survivor software package, time-series analysis and Kaplan Meier (KM) curve were used to determine prognosis [13].
Consensus clustering confirms different subtypes of ERC patients
Based on the TCGA_READ dataset, the following parameters were used for consensus clustering of the selected IRGs: 1000 replicates and K = 10. Principal Component Analysis (PCA) (based on the R “ggplot2” package) was used to further confirm the number of subtypes in ERC patients in ERC patients, and Kaplan Meier curves were used to confirm the prognostic differences among ERC patients classified into different subtypes [14].
A IRGs prognostic model constructed and validated
Based on the TCGA_READ dataset, multivariate Cox proportional hazards regression was used to calculate the coefficients of IRG in ERC patients, and a risk score calculation formula (Risk score = β1⋅Exp1 + β2⋅Exp2+⋯+βn⋅Expn) for the prognostic characteristics of ERC patients was developed based on the analyzed coefficients, where β and Exp represent the coefficients and expression levels of IRGs in ERC patients, respectively. Based on the calculated median risk score, ERC patients were ultimately classified into high-risk subtype patients and low-risk subtype patients. The median risk score was obtained by inputting the expression levels of 3 IRGs (IL20RB, NR1H2, SSTR5) from all patients in the TCGA_READ dataset into the risk scoring formula and calculating the risk score for each patient. The ‘survminer’ package in R was used for Kaplan-Meier analysis followed by log-rank tests [15].
Pathway enrichment analysis
Based on Spearman correlation analysis (using the TCGA_READ dataset), genes related to the risk score of ERC patients (correlation coefficient | R |>0.3) were obtained. The R clusterProfiler software package was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Ontology (GO) enrichment analysis. The false discovery rate (FDR) < 0.05 and standardized P-value < 0.05 were the criteria for screening pathways with significant differences [16].
Differences in tumor immune microenvironment among high and low-risk ERC patients
The TIMER method was used to analyze 6 immune cells infiltration in ERC patients in the TCGA_READ dataset. We analyzed the Spearman relationship between IRGs selected for constructing prognostic risk models and immune cells and plotted a network diagram. The ggplot2 of R software was used to visualize the results [17].
Estimation of therapeutic drugs
Due to individual differences in ERC patients, there were differences in sensitivity to the same drug among different patients. In order to explore the differences in drug sensitivity between high-risk and low-risk groups, drug sensitivity analysis was conducted. The OncoPredict software package of R was used to predict the differences in drug activity between high-risk and low-risk groups [18].
Statistical analysis
Wilcoxon test and T-test were used to compare two variables in ERC patients. Kaplan Meier curves and log-rank tests were used to evaluate survival differences in ERC patients. Univariate and multivariate Cox regression models were used to evaluate the prognostic value of IRGs in ERC patients. Pearson’s chi-square test was used to evaluate changes in clinical characteristics of ERC patients. The R program (version R 4.4.1) was used for all analyses [19]. P < 0.05 was the threshold for statistical significance. The code used is shared at https://github.com/freezh144/ERC_code/tree/main.
Results
Identification of IRGs with potential prognostic value in ERC patients
Based on TCGA_READ dataset, A total of 76 IRGs were significantly associated with prognosis in ERC at P < 0.05. 76 IRGs were further validated using the GSE87211 dataset, resulting in 3 IRGs associated with prognosis (Supplementary Table 1, Figure S1). The Kaplan Meier curve indicates the prognostic value of each gene (Fig. 1A–B). In ERC, high expression of IL20RB and NR1H2 are linked with poorer prognosis and high expression of SSTR5 was linked with better prognosis. Meanwhile, we extracted the clinical pathological features of ERC from the TCGA_READ dataset and integrated them into Table 1.
Fig. 1.
Identification of IRGs with potential prognostic value in ERC patients. The Kaplan–Meier curves confirmed each gene’s prognostic value (A–B)
Table 1.
Based on the TCGA-READ dataset, the clinical pathological features of ERC were extracted
| Variables | groups | HR (univariable)+P-value |
|---|---|---|
| Risk_score | High | |
| Low | 0.14 (0.05-0.43, p<.001) | |
| Sex | Female | |
| Male | 0.69 (0.31-1.51, p=.351) | |
| Age | Greater than or equal to 70 | |
| Less than 70 | 0.32 (0.13-0.80, p=.015) | |
| Stage | I | |
| II | 0.88 (0.16-4.83, p=.884) | |
| III | 1.59 (0.32-7.98, p=.572) | |
| IV | 4.08 (0.87-19.09, p=.074) | |
| Pathologic_N | N0 | |
| N1 | 2.27 (0.84-6.13, p=.107) | |
| N2 | 3.26 (1.18-9.00, p=.023) | |
| Pathologic_M | M0 | |
| M1 | 2.55 (1.10-5.91, p=.029) | |
| IL20RB | Mean ± SD | 1.50 (1.11-2.02, p=.009) |
| NR1H2 | Mean ± SD | 1.87 (1.05-3.32, p=.032) |
| SSTR5 | Mean ± SD | 0.78 (0.66-0.92, p=.004) |
Consensus clustering confirms different subtypes of ERC patients
The consensus clustering calculation results of ERC patients show that the clustering results with k = 2 were more stable than those with k = 3–6 (Fig. 2A–B). The PCA results showed that when k = 3–5, the ERC patient samples exhibited high similarity (Figure S2). Therefore, ERC patients were ultimately divided into two clusters. The prognosis of ERC patients with cluster 2 was better than that of ERC patients with cluster 1 (Fig. 2C). IL20RB and SSTR5 showed cluster 1 and cluster 2 to significant differential expression in cluster (Fig. 2D).
Fig. 2.
Consensus clustering analysis of prognostic survival related IRGs. The consensus clustering analysis results indicate that k = 2 seems to be more stable than k = 3–6 (A–B). ERC patients in cluster 2 had a better prognosis than cluster 1 (C). IL20RB and SSTR5 showed cluster 1 and cluster 2 to significant differential expression in cluster (D)
A IRGs prognostic model constructed and validated
The detailed calculation formula for the risk score of ERC patients is as follows: risk score = (0.16×IL20RB) + (0.49×CCT5) + (0.71×NR1H2) + (−0.26×SSTR5). Based on the median risk score, ERC patients were divided into two different prognostic risk groups (low-risk patients group and high-risk patients group). Kaplan Meier curves showed that high-risk ERC patients had a poorer prognosis compared to low-risk patients (Fig. 3A). Then this prognostic feature was validated in the GSE8721 dataset (Figure S3). These computational analysis results validate the stability and reliability of the prognostic risk score for ERC patients. Meanwhile, risk score was an independent risk factor for the prognosis of ERC patients (Fig. 3B).
Fig. 3.
A IRGs prognostic model constructed and validated. Kaplan–Meier curves showed that ERC patients with high-risk had worse prognosis compared with those with low-risk (A). These analysis results demonstrate the stability and reliability of the prognostic features of ERC, and the risk score was identified as an independent risk factor for the prognosis of ERC patients by multivariate Cox regression analysis (P < 0.01 represents significance) (B)
Identification of the prognostic model-related biological pathways
Based on Spearman correlation analysis, 5 significantly negatively correlated genes and 1613 significantly positively correlated genes were ultimately selected. The results of the KEGG pathways and GO pathways were shown in Fig. 4A–B. The analysis results were mainly enriched in oxidative phosphorylation, mitochondrial ATP synthesis coupled electron transport, regulation of TOR signaling, regulation of autophagy, ficolin-1-rich granule.
Fig. 4.
Identification of the prognostic model-related biological pathways. GO enrichment analysis (A). KEGG pathway analysis (B)
The associations with the tumor immune microenvironment
Box-plots show a significant difference in immune cell infiltration between low-risk patients and high-risk patients (Fig. 5A). Immune cells also remain strongly associated with 3 IRGs (IL20RB, NR1H2 and SSTR5), which may have an impact on ERC patients prognosis (Fig. 5B).
Fig. 5.
The associations with the tumor immune microenvironment. Box-plots show significant differences in immune cell infiltration between low-risk patients and high-risk patients (A). Immune cells also remain strongly associated with 3 IRGs, which may have an impact on ERC patients prognosis (B)
Estimation of therapeutic drugs
The drug sensitivity of 198 drugs in ERC patients was calculated and analyzed. The results showed that there were significant differences in IC50 values of our four drugs (BMS.754807, Cediranib, Taselisib, Daporinad) between the low-risk patients and high-risk patients (BMS.754807, Cediranib, Taselisib, Daporinad) (Fig. 6A). Among them, the IL20RB and NR1H2 were associated with four drugs was negatively correlated (Fig. 6B).
Fig. 6.
Estimation of therapeutic drugs. We identified 4 drugs with significant differences in IC50 between the low-risk and high-risk groups (BMS.754807, Cediranib, Taselisib, Daporinad) (A). Among them, the IL20RB and NR1H2 are associated with 4 drugs is negatively correlated (B)
Discussion
ERC refers to RC that occurs when the patient was over 60 years old. The prognosis for ERC was poor, with a survival period of approximately 2–3 years. At present, there are no good predictive indicators in clinical practice to predict the treatment outcome of ERC. Some patients with poor prognosis do not receive timely treatment, which affects their survival [20]. Our current findings identified 3 IRGs that are associated with prognosis in patients with ERC, namely SSTR5, IL20RB and NR1H2, and survival analysis using Kaplan-Meier curve showed that high expression of IL20RB and NR1H2 are linked with poorer prognosis and high expression of SSTR5 is linked with better prognosis.
IRGs have been reported to play a crucial role in the development and occurrence of various cancers [21]. IL20RB had been reported as a potential oncogene [22]. IL20RB had not been reported in ERC, our present study provides an insight for the investigation of IL20RB in ERC. NR1H2 plays an important role in the prognosis of colorectal cancer, and its role in ERC has not been elucidated [23]. The root cause of apoptosis in colorectal cancer patients treated with certain drugs is that certain drugs may induce changes in the SSTR5 [24]. However, the clinical value of SSTR5 had not been reported in rectal cancer and ERC, our present study provides an insight for the investigation of SSTR5 in ERC.
Furthermore, we constructed a prognostic signature based on these 3 IRGs and the patients can be divided into high-risk and low-risk groups according to the risk scores for prognostic signature. As well, the risk score can be act as an independent risk factor for prognosis of ERC patients. Our prognostic signature may be severed as a valuable complement for the prognosis evaluation of ERC. However, an increased data was needed to analyze the interaction between our prognostic signature and existing risk stratification. Our study therefore found that 3 IRGs played an important role in ERC ‘s immune regulation, providing ERC ‘s immunotherapy a theoretical basis. The functional analysis results were mainly enriched in oxidative phosphorylation, mitochondrial ATP synthesis coupled electron transport, regulation of TOR signaling, regulation of autophagy, ficolin-1-rich granule. There is a certain correlation between oxidative phosphorylation and immune infiltration. In the tumor microenvironment, oxidative phosphorylation is a metabolic pathway that produces ATP through the oxidative phosphorylation process, which is closely related to the function and infiltration of immune cells [25]. For example, the oxidative phosphorylation pathway typically occurs in resting immune cells or immune cells that regulate inflammation, such as regulatory T cells or macrophages [26]. Oxidative phosphorylation metabolism may play an important role in maintaining the tissue-specific function of macrophages [27]. Our study also found significant differences in the infiltration of macrophages and CD8 + T cells between high and low-risk groups. Ficolin-1 mainly exists in local inflammatory areas and plays an important role in immunity [28]. Meanwhile, we found that immune cells are closely related to 3 IRGs. Therefore, high-risk patients may experience immune response disorders and inflammatory reactions due to changes in oxidative phosphorylation and energy pathways, which may be caused by alterations in IRGs expression. The specific mechanism needs further research.
There are several limitations to this study. Firstly, the data in this study mainly comes from the TCGA database and GEO database. Therefore, the study is retrospective, and potential confounding factors are inevitably present in the data (for example, racial factors, geographical factors, clinical biochemical indicators, etc.) that require prospective research for further exploration. Secondly, this study did not experimentally validate the value of IRGs in ERC patients, therefore, further in vitro and in vivo experimental validation and prospective cohort studies are essential.
Conclusion
This study systematically and comprehensively analyzed the prognostic value of IRGs in ERC patients for the first time, and identified 3 IRGs with prognostic value in ERC patients. A prognostic risk score for ERC patients was constructed based on 3 IRGs, and its prognostic value was further validated in an independent external ERC dataset. In ERC patients, this prognostic risk score was an independent risk factor. 3 IRGs were also related to the immune microenvironment of ERC patients. This study provides a fundamental theoretical basis for guiding the clinical treatment of ERC patients.
Supplementary Information
Supplementary Material 2. Supplementary Table 1. Identification of IRGs with potential prognostic value in ERC patients.
Author contributions
Jie Deng, Zixin Shu, Hui Yang, Bo Yang, Yuzhi Li, Xiang SunJie Deng, Zixin Shu: Conceived and designed the experiments; Jie Deng, Zixin Shu: Performed the experiments; Jie Deng, Hui Yang: Analyzed and interpreted the data; Xiang Sun: Contributed reagents, materials, analysis tools or data; Bo Yang, Yuzhi Li, Xiang Sun: Wrote the paper.All authors reviewed the manuscript.
Funding
This study was funded by the Hefei Municipal Health Commission (Hwk2021yb001).
Data availability
TCGA_READ was downloaded from The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). Microarray data sets (GSE87211) are downloaded from the Gene Expression Integrated Database (GEO) (https://www.ncbi.nlm.nih.gov/geo/).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jie Deng and Zixin Shu these authors contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 2. Supplementary Table 1. Identification of IRGs with potential prognostic value in ERC patients.
Data Availability Statement
TCGA_READ was downloaded from The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). Microarray data sets (GSE87211) are downloaded from the Gene Expression Integrated Database (GEO) (https://www.ncbi.nlm.nih.gov/geo/).