Immune-related gene signature in predicting prognosis of early-stage colorectal cancer patients

Development and validation of a prognostic model for colon cancer based on mitotic gene signatures and immune microenvironment analysis

BACKGROUND

Mitotic processes play a pivotal role in tumor progression and immune responses. However, the correlation between mitosis-related genes, clinical outcomes, and the tumor microenvironment (TME) in colon cancer remains unclear. This study aims to develop a prognostic and therapeutic significance model for colon cancer based on mitosis-related genes.

METHODS

RNA expression profiles and clinical data of 453 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Mitosis-related genes were selected from the MsigDb database. The gene model was constructed using differential analysis, univariate and multivariate Cox regression, and Lasso regression analyses. The predictive model was validated using data from the GSE17536, GSE17537, and GSE39582 datasets. Predictive accuracy was evaluated via Receiver Operating Characteristic (ROC) curves, while nomograms were developed by integrating clinical and pathological features. Gene set enrichment analysis explored biological processes and pathways linked to the model. TME was assessed using ESTIMATE, and the proportion and function of immune cells were analyzed through CIBERSORT. Drug sensitivity analysis was conducted using the CTRP database.

RESULTS

A predictive model based on 17 mitosis-related genes (KIFC1, CCNF, EME1, CDC25C, ORC1, CCNJL, ANKRD53, MEIS2, FZD3, TPD52L1, MAPK3, CDKN2A, EDN3, NPM2, PSRC1, INHBA, BIRC5) was created. The model exhibited robust predictive performance across both training and validation cohorts. Nomograms for predicting 3-, 5-, and 7-year survival rates in colon cancer (COAD) patients were generated. The model's correlation with immune cell infiltration and function was highlighted.

CONCLUSION

The mitosis-related gene model serves as a valuable indicator for predicting survival outcomes in colon cancer patients.

A hot and cold tumor‑related prognostic signature for stage II colorectal cancer

Globally, colorectal cancer (CRC) is one of the most lethal and prevalent malignancies. Based on the presence of immune cell infiltration in the tumor microenvironment, CRC can be divided into immunologically 'hot' or 'cold' tumors, which in turn leads to the differential efficacy of immunotherapy. However, the immune characteristics of hot and cold CRC tumors remain largely elusive, prompting further investigation of their properties regarding the tumor microenvironment. In the present study, a predictive model was developed based on the differential expression of proteins between cold and hot CRC tumors. First, the differentially expressed proteins (DEPs) were identified using digital spatial profiling and mass spectrometry-based proteomics analysis, and the pathway features of the DEPs were analyzed using functional enrichment analysis. A novel eight-gene signature prognostic risk model was developed (IDO1, MAT1A, NPEPL1, NT5C, PTGR2, RPL29, TMEM126A and TUBB4B), which was validated using data obtained from The Cancer Genome Atlas. The results revealed that the risk score of the eight-gene signature acted as an independent prognostic indicator in patients with stage II CRC (T3-4N0M0). It was also found that a high-risk score in the eight-gene signature was associated with high immune cell infiltration in patients with CRC. Taken together, these findings revealed some of the differential immune characteristics of hot and cold CRC tumors, and an eight-gene signature prognostic risk model was developed, which may serve as an independent prognostic indicator for patients with stage II CRC (T3-4N0M0).

Prognostic significance of the Naples prognostic score in colorectal cancer patients undergoing curative resection: a propensity score matching analysis

BACKGROUND

Systemic inflammation is recognized as a hallmark of cancer that contributes to tumor development and progression in various malignancies. The Naples prognostic score (NPS) was established as a prognostic indicator for colorectal cancer (CRC). This study aims to examine the predictive value of the NPS for survival in CRC patients undergoing curative resection by a propensity score matching (PSM) analysis.

METHODS

A total of 533 CRC patients were enrolled in this study. Overall survival (OS) and disease-free survival (DFS) were compared between high-NPS and low-NPS groups. A time-dependent receiver operator characteristic (ROC) curve analysis was conducted to calculate the area under curve (AUC) of the NPS for OS. A multivariable Cox-proportional hazards regression analysis and PSM analysis were used to identify independent prognostic factors for OS and DFS. We compared the predictive value of the NPS to that of the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), Onodera prognostic nutritional index (PNI), and controlling nutritional status score (CONUT) for OS.

RESULTS

High-NPS was significantly associated with worse OS and DFS. After PSM, 123 patients were included in each group. A multivariate analysis revealed that Age ≥ 68, ASA-PS ≥ 3, high NPS and undifferentiated tumor type were independently associated with OS, while high NPS, advanced T and N stage were independently associated with DFS after PSM. The NPS had the greatest AUC for OS in comparison to the NLR, LMR, PNI and CONUT.

CONCLUSIONS

We successfully validated the prognostic utility of the NPS for CRC patients after curative resection.

An immune, stroma, and epithelial-mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II-III colorectal cancer

BACKGROUND

Debates exist on the treatment decision of the stage II/III colorectal cancer (CRC) due to the insufficiency of the current TNM stage-based risk stratification system. Epithelial-mesenchymal transition (EMT) and tumor microenvironment (TME) have both been linked to CRC progression in recent studies. We propose to improve the prognosis prediction of CRC by integrating TME and EMT.

METHODS

In total, 2382 CRC patients from seven datasets and one in-house cohort were collected, and 1640 stage II/III CRC patients with complete survival information and gene expression profiles were retained and divided into a training cohort and three independent validation cohorts. Integrated analysis of 398 immune, stroma, and epithelial-mesenchymal transition (ISE)-related genes identified an ISE signature independently associated with the recurrence of CRC. The underlying biological mechanism of the ISE signature and its influence on adjuvant chemotherapy was further explored.

RESULTS

We constructed a 26-gene signature which was significantly associated with poor outcome in Training cohort (p < 0.001, HR [95%CI] = 4.42 [3.25-6.01]) and three independent validation cohorts (Validation cohort-1: p < 0.01, HR [95%CI] = 1.70 [1.15-2.51]; Validation cohort-2: p < 0.001, HR [95% CI] = 2.30 [1.67-3.16]; Validation cohort-3: p < 0.01, HR [95% CI] = 2.42 [1.25-4.70]). After adjusting for known clinicopathological factors, multivariate cox analysis confirmed the ISE signature's independent prognostic value. Subgroup analysis found that stage III patients with low ISE score might benefit from adjuvant chemotherapy (p < 0.001, HR [95%CI] = 0.15 [0.04-0.55]). Hypergeometric test and enrichment analysis revealed that low-risk group was enriched in thr immune pathway while high-risk group was associated with the EMT pathway and CMS4 subtype.

CONCLUSION

We proposed an ISE signature for robustly predicting the recurrence of stage II/III CRC and help treatment decision by identifying patients who will not benefit from current standard treatment.

Exploration and validation of a combined immune and metabolism gene signature for prognosis prediction of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is still one of the most frequently diagnosed malignancy around the world. The complex etiology and high heterogeneity of CRC necessitates the identification of new reliable signature to identify different tumor prognosis, which may help more precise understanding of the molecular properties of CRC and identify the appropriate treatment for CRC patients. In this study, we aimed to identify a combined immune and metabolism gene signature for prognosis prediction of CRC from large volume of CRC transcriptional data.

METHODS

Gene expression profiling and clinical data of HCC samples was retrieved from the from public datasets. IRGs and MRGs were identified from differential expression analysis. Univariate and multivariate Cox regression analysis were applied to establish the prognostic metabolism-immune status-related signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were generated for diagnostic efficacy estimation. Real-time polymerase chain reaction (RT-PCR), Western blot and immunohistochemistry (IHC) was conducted to verified the expression of key genes in CRC cells and tissues.

RESULTS

A gene signature comprising four genes (including two IRGs and two MRGs) were identified and verified, with superior predictive performance in discriminating the overall survival (OS) of high-risk and low-risk compared to existing signatures. A prognostic nomogram based on the four-gene signature exhibited a best predictive performance, which enabled the prognosis prediction of CRC patients. The hub gene ESM1 related to CRC were selected via the machine learning and prognostic analysis. RT-PCR, Western blot and IHC indicated that ESM1 was high expressed in tumor than normal with superior predictive performance of CRC survival.

CONCLUSIONS

A novel combined MRGs and IRGs-related prognostic signature that could stratify CRC patients into low-and high- risk groups of unfavorable outcomes for survival, was identified and verified. This might help, to some extent, to individualized treatment and prognosis assessment of CRC patients. Similarly, the mining of key genes provides a new perspective to explore the molecular mechanisms and targeted therapies of CRC.

Prognostic value of the ratio of carcinoembryonic antigen concentration to maximum tumor diameter in patients with stage II colorectal cancer

Background

Recently, a study from our center indicated that the ratio of preoperative carcinoembryonic antigen (CEA) concentration to maximum tumor diameter (DMAX) may be a prognostic marker for patients with rectal cancer. Therefore, the study aimed to evaluate whether this ratio (CEA/DMAX) has prognostic value for patients with stage II colorectal cancer (CRC).

Methods

A prospectively maintained database was searched for patients with pathologically confirmed stage II CRC who underwent surgery between January 2010 and March 2019. Patients were stratified according to the mean CEA/DMAX value into low and high CEA/DMAX groups. Kaplan-Meier, univariable, and multivariable Cox regression analyses were used to evaluate whether the CEA/DMAX could predict overall survival (OS) and disease-free survival (DFS). Nomograms were constructed in terms of the results of multivariable Cox regression analyses.

Results

The study included 2,499 patients with stage II CRC. The mean CEA/DMAX value was 2.33 (ng/mL per cm). Kaplan-Meier analyses revealed that, relative to the low CEA/DMAX group, the high CEA/DMAX group had significantly poorer OS (67.31% vs. 85.02%, P<0.001) and DFS (61.41% vs. 77.10%, P<0.001). The multivariable Cox regression analysis revealed that CEA/DMAX independently predicted OS (hazard ratio: 2.58, 95% confidence interval: 1.51-4.38, P<0.001) and DFS (hazard ratio: 1.97, 95% confidence interval: 1.38-2.83, P<0.001). Two simple-to-use nomograms comprising CEA/DMAX, age, T stage, and lymphovascular invasion were developed to predict 1-, 3-, and 5-year rates of OS and DFS among patients with stage II CRC. The nomograms had good performance based on the concordance index, receiver operating characteristic (ROC) curve analysis, and calibration curves. Subgroup analyses further confirmed that a high CEA/DMAX was associated with poor OS and DFS among patients with stage II colon cancer and among patients with stage II rectal cancer (both P<0.05).

Conclusions

Among patients with stage II CRC, a high CEA/DMAX independently predicted poor OS and DFS, and the predictive abilities were also observed in subgroup analyses of patients with stage II colon cancer or rectal cancer. Furthermore, we developed two nomograms that had good accuracy for predicting the prognosis of stage II CRC.

Construction of a Signature Composed of 14 Immune Genes to Judge the Prognosis and Immune Infiltration of Colon Cancer

Background: Colon cancer (CC) is an immunogenic tumor and immune-targeting disease. In this study, we analyzed differentially expressed genes (DEGs) from the expression profile data in CC of The Cancer Genome Atlas. Methods and Results: Using univariate and multivariate Cox regression analysis, an immune gene-risk model containing 14 immune genes was established. Four hundred seventeen CC samples were divided into high-risk and low-risk groups, and Kaplan-Meier analysis revealed that high-risk score predicted poor survival. Meanwhile, we found the model was an independent prognostic factor for CC. Weighted gene coexpression network analysis was used to identify key gene modules between high- and low-risk groups. The methods of CIBERSORT and single-sample Gene Set Enrichment Analysis were used to evaluate the correlation between immune cells and our model. Conclusion: Taken together, our study suggested that the immune gene-related risk model may be developed as a potential tool in the prognostic assessment of CC.

Identification and Validation of a Six Immune-Related Genes Signature for Predicting Prognosis in Patients With Stage II Colorectal Cancer

Background

Immune-related genes (IRGs) play important roles in the tumor immune microenvironment and can affect the prognosis of cancer. This study aimed to construct a novel IRG signature for prognostic evaluation of stage II colorectal cancer (CRC).

Methods

Gene expression profiles and clinical data for stage II CRC patients were collected from the Cancer Genome Atlas and Gene Expression Omnibus database. Univariate, multivariate Cox regression, and least absolute shrinkage and selection operator regression were used to develop the IRG signature, namely IRGCRCII. A nomogram was constructed, and the “Cell Type Identification by Estimating Relative Subsets of RNA Transcripts” (CIBERSORT) method was used to estimate immune cell infiltration. The expression levels of genes and proteins were validated by qRT-PCR and immunohistochemistry in 30 pairs of primary stage II CRC and matched normal tissues.

Results

A total of 466 patients with stage II CRC were included, and 274 differentially expressed IRGs were identified. Six differentially expressed IRGs were detected and used to construct the IRGCRCII signature, which could significantly stratify patients into high-risk and low-risk groups in terms of disease-free survival in three cohorts: training, test, and external validation (GSE39582). Receiver operating characteristics analysis revealed that the area under the curves of the IRGCRCII signature were significantly greater than those of the OncotypeDX colon signature at 1 (0.759 vs. 0.623), 3 (0.875 vs. 0.629), and 5 years (0.906 vs. 0.698) disease-free survival, respectively. The nomogram performed well in the concordance index (0.779) and calibration curves. The high-risk group had a significantly higher percentage of infiltrated immune cells (e.g., M2 macrophages, plasma cells, resting mast cells) than the low-risk group. Finally, the results of qRT-PCR and immunohistochemistry experiments performed on 30 pairs of clinical specimens were consistent with bioinformatics analysis.

Conclusion

This study developed and validated a novel immune prognostic signature based on six differentially expressed IRGs for predicting disease-free survival and immune status in patients with stage II CRC, which may reflect immune dysregulation in the tumor immune microenvironment.

Immune infiltration and immune gene signature predict the response to fluoropyrimidine-based chemotherapy in colorectal cancer patients

Fluoropyrimidine-based chemotherapy is an essential component of systemic chemotherapy for colorectal cancer (CRC). The immune response is implicated in chemotherapy-induced cytotoxicity. Here, we reported an immune risk (Imm-R) model for prognostic prediction in patients receiving fluoropyrimidine-based chemotherapy. Gene expression profiles and corresponding clinical information were collected from four data sets and divided into training set (n = 183) and validation set (validation set1: n = 34; validation set2: n = 99). The composition of 22 tumor-infiltrating immune cells (TIICs) populations was characterized with the CIBERSORT deconvolution algorithm. A prognostic Imm-R model for predicting overall survival was established by performing least absolute shrinkage and selection operator (LASSO) penalized COX regression analysis. T follicular helper cells and M0 macrophages were associated with better survival, while eosinophils were associated with worse survival. TIICs signature was constructed based on the above three immune cell types. Furthermore, a Imm-R model was created by integrating TIICs signature with immune-related genes (IRGs), which effectively in distinguishing CRC patients with poorer prognosis. The Imm-R model was associated with activation of the TGF-beta signaling and suppression of DNA damage. Results of this research provide new insights into the role of immunity for in fluoropyrimidine-based chemotherapy as well as a useful tools to predict the outcome of CRC patients receiving fluoropyrimidine-based chemotherapy.