Development and validation of nomograms for predicting the prognosis of colorectal cancer patients

Introduction

Colorectal cancer (CRC) is an important health concern worldwide, ranking third in incidence and second in cancer-related mortality among all cancers (1). Despite advancements in treatment options that have improved the prognosis of CRC patients (2), the 5-year postoperative survival rate remains suboptimal (3).

Prognostic assessment of CRC patients is essential for implementing personalized treatment strategies and improving quality of life (4-6). Conventional pathological tumor-node-metastasis (TNM) staging has been a robust tool for stratifying patient prognosis and is widely used by oncologists (5,7). However, surgical specimens sometimes fail to meet the accuracy requirements for TNM staging, such as when the number of lymph nodes examined does not reach the recommended minimum of twelve according to the National Comprehensive Cancer Network (NCCN) guidelines (8,9). Furthermore, high-risk factors for tumor recurrence, such as tumor differentiation grade, perineural invasion, and vascular invasion, are not included in the TNM staging system. These factors substantially impact the accuracy and effectiveness of the TNM staging system (10). In addition, CRC patients with the same pathological TNM stage often exhibit significantly different prognosis according to various studies (11), leading to the inclusion of risk factors such as vascular invasion and extranodal tumor deposits (ENTD) to enhance the predictive power of conventional pathological staging (12). This finding suggests that there is room for improvement in the ability of pathological TNM staging to predict CRC patient prognosis (13).

In the present study, based on a large sample of clinical data, we aimed to develop a prognostic prediction model with enhanced predictive ability by integrating common pathological parameters, offering a reference for clinical prognostic assessment and treatment decision-making. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1924/rc).

Methods

Research population

This study included patients with CRC who underwent surgical treatment at the Department of Gastrointestinal Surgery, Tongji Hospital, Wuhan, from 2014 to 2018. The inclusion criteria were as follows: (I) diagnosis of CRC confirmed by biopsy and (II) completion of curative surgical treatment. The exclusion criteria were as follows: (I) patients younger than eighteen years; (II) patients with unclear primary tumor locations or multiple primary tumors; (III) patients with an American Society of Anesthesiologists (ASA) (14) grade ≥ IV; (IV) patients received any anti-tumor treatment before surgery, including chemotherapy, radiotherapy, targeted therapy and immunotherapy; and (V) patients with missing data. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Review Committee of Tongji Hospital, Wuhan (No. TJ-IRB202405086, May 31st, 2024) and informed consent was taken from all the patients.

A retrospective analysis of the clinicopathological data of patients with CRC was also conducted. The data included sex, age, body mass index (BMI), hypertension history, diabetes history, previous abdominal surgeries, pathological characteristics of the tumor, and follow-up information. Tumor staging was performed using the 8th edition American Joint Committee on Cancer (AJCC) staging system. The log odds of positive lymph nodes (LODDS) is an innovative lymph node staging system that contributes to accurate risk stratification in patients with insufficient lymph node retrieval (15-17). Thus, LODDS as a variable was also included in this study.

Follow-up

Regular follow-ups were conducted via outpatient visits and telephone consultations. The follow-up protocol was structured as follows: patients were evaluated quarterly during the first year, biannually in the second year, and annually thereafter until death. The primary objectives of these follow-ups included monitoring patient survival status, detecting serum tumor marker levels, performing abdominal computed tomography (CT) scans, and performing electronic colonoscopy examinations. Overall survival (OS) was defined as the interval from the surgery date to the date of death from any cause or the last follow-up. Disease-free survival (DFS) was defined as the time interval from surgery to the occurrence of tumor recurrence, death, or the last follow-up, whichever occurred first.

Statistical analysis

Quantitative data are expressed as the mean [standard deviation (SD)], and categorical data are presented as n (%). The patient cohort was randomly divided into development and validation sets at a ratio of 7:3. The Chi-squared test or Fisher’s exact test was applied to assess the significance of differences in categorical variables. The Shapiro-Wilk test was used to evaluate the normality of continuous variables. The significance of differences in continuous variables was assessed using the Mann-Whitney U test or t-test. DFS and OS curves were plotted using the Kaplan-Meier method, and statistical analyses were performed using the log-rank test (Mantel-Cox test). Univariate and multivariate Cox regression analyses were subsequently performed to identify significant prognostic factors for OS and DFS in the study cohort. Schoenfeld residuals were calculated to ensure that the variables met the proportional hazards assumption. The least absolute shrinkage and selection operator (LASSO) regression method was employed to select independent variables for inclusion in the model. Risk factors identified by LASSO regression were used to construct nomograms for OS and DFS. Receiver operating characteristic (ROC) analysis was also conducted to compare the prognostic prediction capabilities of the nomograms and the conventional AJCC staging system, with larger areas under the curve (AUCs) indicating superior predictive performance. Calibration plots and decision curve analysis (DCA) were also employed for the evaluation of the nomogram models.

A two-tailed P value of less than 0.05 was considered to indicate statistical significance. All the statistical analyses were conducted using R software version 4.2.1 (http://www.r-project.org/).

Results

Demographic and clinicopathological characteristics

The records of the Gastrointestinal Surgery Department of Tongji Hospital in Wuhan were used to identify 3,055 CRC patients. According to the inclusion criteria and exclusion criteria, we finally had a cohort of 2,435 patients for the study (Figure 1). The cohort comprised 56.9% males, with an average age of 57.35±12.00 years (Table 1).

Figure 1 Flow chart of this single-center study. ASA, American Society of Anesthesiologists; CRC, colorectal cancer.

The study cohort was randomly divided into a development group and a validation group at a 7:3 ratio, with 1,707 patients in the former and 728 in the latter. A statistical test confirmed that the baseline characteristics were well balanced between these two subsets (Table 1). Additionally, Kaplan-Meier survival analysis revealed no significant differences in survival outcomes between the two groups (Figure S1).

Predictor identification

The pathological factors analyzed in this study included tumor invasion depth, distant metastasis, tumor differentiation grade, tumor location, tumor size, perineural invasion, vascular invasion, ENTD, R1 resection status, number of lymph nodes harvested, number of positive lymph nodes, and LODDS. All pathological factors conformed to the proportional hazards assumption upon testing. Univariate Cox regression models were utilized to identify pathologic risk factors associated with survival (Table 2). The pathological factors significantly associated with OS were tumor invasion depth (P<0.001), distant metastasis (P<0.001), tumor differentiation grade (P<0.001), tumor size (P=0.050), perineural invasion (P=0.001), vascular invasion (P<0.001), ENTD (P<0.001), positive surgical margins (P=0.001), number of lymph nodes harvested (P=0.002), number of positive lymph nodes (P<0.001), and LODDS (P<0.001). LASSO regression was employed to identify the most crucial variables associated with survival risk. The algorithm was iterated 1,000 times to ensure precision. Additionally, tenfold cross-validation was applied to mitigate the risk of overfitting (Figure S2). Six parameters with nonzero coefficients were selected for the final model: tumor invasion depth, distant metastasis, tumor differentiation grade, ENTD, R1 resection, and LODDS.

Similarly, in the analysis of DFS (Table 2 and Figure S2), tumor invasion depth, distant metastasis, tumor differentiation grade, ENTD, and LODDS were determined as the final variables for inclusion in the nomogram model.

Nomogram construction

For OS and DFS, we constructed corresponding OS-nomograms (Figure 2A) and DFS-nomograms (Figure 2B) utilizing six and five predetermined parameters, respectively, as described above. Patients in the cohort were stratified into high-risk and low-risk groups based on the median risk scores calculated from the nomograms. Kaplan-Meier analysis revealed a significant difference in survival outcomes between these two groups (Figure S3).

Figure 2 Nomograms for predicting survival of CRC patients. (A) The nomogram for predicting OS. (B) The nomogram for predicting DFS. CRC, colorectal cancer; DFS, disease-free survival; ENTD, extranodal tumor deposits; LODDS, log odds of positive lymph nodes; OS, overall survival.

The ROC analysis demonstrated that the six-parameter OS nomogram showed robust predictive performance in the training cohort, with AUCs of 0.812, 0.771, and 0.791 for 1-, 3-, and 5-year OS, respectively (Figure 3A). In the validation set, the OS nomogram maintained its efficacy, yielding AUCs of 0.783, 0.787, and 0.774 for 1-, 3-, and 5-year OS, respectively (Figure 3B). Calibration plots indicated that the nomogram reliably predicted OS outcomes in both the training and validation sets (Figure 3C,3D).

Figure 3 ROC analysis and calibration curve for verifying the OS prediction ability of the nomogram in development and validation sets. (A,B) ROC analyses for verifying the discrimination of the nomogram in development set and validation set, respectively. (C,D) Calibration curves for verifying the accuracy of the nomogram in development set and validation set, respectively. AUC, area under the curve; OS, overall survival; ROC, receiver operating characteristic.

Regarding the DFS nomogram, which utilized five parameters, the area under the curve (AUC) in the training set for 1-, 3-, and 5-year DFS was 0.783, 0.784, and 0.772, respectively (Figure 4A). In the validation cohort, the AUC was 0.805, 0.758, and 0.743 for 1-, 3-, and 5-year DFS, respectively (Figure 4B). Calibration plots confirmed the accuracy of the nomogram for predicting DFS (Figure 4C,4D).

Figure 4 ROC analysis and calibration curve for verifying the DFS prediction ability of the nomogram in development and validation sets. (A,B) ROC analyses for verifying the discrimination of the nomogram in development set and validation set, respectively. (C,D) Calibration curves for verifying the accuracy of the nomogram in development set and validation set, respectively. AUC, area under the curve; DFS, disease-free survival; ROC, receiver operating characteristic.

Comparison with the AJCC staging system

We conducted a comparative analysis of the predictive performance of the developed nomogram and the conventional AJCC staging system for predicting survival outcomes in the whole CRC cohort. According to the ROC analysis, the OS nomogram exhibited AUC values of 0.786, 0.776, and 0.803 for 1-, 3-, and 5-year OS, respectively, surpassing the AUC values of 0.768, 0.750, and 0.782, respectively, for the AJCC staging system (Figure 5A-5C). And the DCA results indicated a greater net benefit with the nomogram than with the AJCC staging system for 1-, 3- and 5-year forecasts (Figure 5D-5F). For DFS prediction, the DFS nomogram yielded AUC values of 0.764, 0.777, and 0.789 for 1-, 3-, and 5-year forecasts, respectively, which were larger than the AUC values of 0.762, 0.761, and 0.770 obtained with the AJCC staging system (Figure 5G-5I). ROC analysis further revealed that, across all patients, the AUC values of the nomogram were consistently greater than those of the AJCC staging system. Additionally, the DCA results also indicated a greater net benefit with the nomogram than with the AJCC staging system for 1-, 3- and 5-year forecasts (Figure 5J-5L). These findings demonstrated the good prognostic prediction capability of our constructed nomogram for predicting shot-term survival (especially for 1- and 3-year OS and DFS).

Figure 5 The comparison of the predictive ability of the nomogram constructed in this study and AJCC staging system for the prognosis of CRC. (A-C) Prediction performance comparison of the nomogram and AJCC staging system by ROC analysis for 1-year (A), 3-year (B), and 5-year (C) OS, respectively. (D-F) Prediction performance comparison of the nomogram and AJCC staging system by DCA analysis for 1-year (D), 3-year (E), and 5-year (F) OS, respectively. (G-I) Prediction performance comparison of the nomogram and AJCC staging system by ROC analysis for 1-year (G), 3-year (H), and 5-year (I) DFS, respectively. (J-L) Prediction performance comparison of the nomogram and AJCC staging system by DCA analysis for 1-year (J), 3-year (K), and 5-year (L) DFS, respectively. AJCC, American Joint Committee on Cancer; CRC, colorectal cancer; DCA, decision curve analysis; DFS, disease-free survival; OS, overall survival; ROC, receiver operating characteristic.

Discussion

Nomograms are statistical tools that offer predictions through straightforward methods and have been extensively utilized in prognosticating outcomes for cancer patients (18). In this study, we developed nomograms for predicting OS and DFS in CRC patients based on pathological data from a single-center cohort. These nomograms had been demonstrated to have robust predictive performance, underscoring their potential utility in postsurgical prognostication for CRC patients.

The strength of this study lies in the development of our nomogram model within a large cohort, bolstered by extensive longitudinal follow-up. The large sample size enabled robust internal validation, leading to more reliable conclusions (19). ROC analysis was instrumental in determining the diagnostic capabilities of the model, while calibration plots provided valuable insights, particularly for models with a narrow range of predictions (20). In our development cohort, the AUC values for 5-year OS and DFS were 0.791 and 0.774, respectively, indicating the favorable discriminative ability of these nomograms. Additionally, calibration plots, as shown in Figures 3,4, demonstrated good estimations of survival probabilities. These prognostic nomograms are valuable decision-making tools that utilize clinically accessible variables and offer the advantage of integrating multiple risk factors for probability estimation as opposed to relying on singular variables.

Despite important advancements in multimodal treatment strategies such as surgery, chemotherapy, radiotherapy, and immunotherapy, the survival outcomes in CRC patients are still unclear (21-23). To improve the survival and quality of life of CRC patients, accurate prognosis prediction and personalized treatment plans are imperative (24). Prognostic predictions and postoperative management strategies for CRC largely depend on postsurgical pathological staging information (24,25), with different pathological stages dictating varied postoperative treatment modalities, i.e., either more aggressive adjuvant therapies or conservative observation. Our study compared the performance of our developed nomogram model with that of traditional TNM staging for prognostic prediction in CRC patients. Both the ROC analysis and DCA indicated that our developed nomogram had predictive performance superior to that of the traditional TNM staging system. These findings suggested that our developed nomogram model has strong clinical applicability and may be more suitable as a reference for formulating treatment strategies for CRC patients.

The pathological parameters included in our developed nomogram model have been extensively validated (26-29). Emerging evidence suggests a strong correlation between positive lymph nodes and poor OS, positioning the former as a potent risk factor in advanced CRC patients and potentially guiding subsequent adjuvant treatment and monitoring strategies (30). Moreover, for accurate N staging in CRC, the widely accepted recommendation for number is a minimum of 12 lymph nodes (9). However, studies have indicated that nearly half of patients have insufficient lymph node data available, partly due to the complexities related to tumor size, invasion depth, and tumor microenvironment (31). The LODDS is an emerging staging system that describes lymph node status. Increasing evidence has shown that the LODDS is more accurate than traditional N staging and the lymph node ratio in evaluating survival in CRC patients (27,32,33). Consistent with the findings of previous reports, our Cox regression analysis indicated that the LODDS played a pivotal role in the progression and development of CRC. Furthermore, the retention of the LODDS in LASSO regression screening underscores its strong predictive capability for CRC, making it a valuable indicator for patients.

While tumor multimodal sequencing provides a wealth of biological information and is a promising tool for cancer prognosis prediction, it undoubtedly increases the economic burden on patients (34). Given the superior accessibility of postoperative pathological information, prognostic models based on postsurgical tumor pathology align more closely with the practical needs of clinical practice.

Our study has certain limitations. First, its retrospective nature and partial lack of data may introduce biases in data interpretation. Second, as the data were exclusively sourced from an Eastern Asian single center, the findings might not be generalizable to other populations. Additionally, our study lacked information on aspects such as tumor microsatellite stability and genomic data that could be beneficial for prognostic predictions. Moreover, robust external validation of our results could enhance the credibility of the findings.

Conclusions

In conclusion, we developed nomogram models for OS and DFS based on postoperative pathological information. These practical nomograms demonstrated good prognostic prediction capabilities and robust discriminative power, even surpassing the traditional TNM staging system in terms of prognostic precision.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1924/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1924/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1924/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1924/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Review Committee of Tongji Hospital, Wuhan (No. TJ-IRB202405086, May 31st, 2024) and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
2. Trommer M, Marnitz S, Skoetz N, et al. Exercise interventions for adults with cancer receiving radiation therapy alone. Cochrane Database Syst Rev 2023;3:CD013448. [Crossref] [PubMed]
3. Cheng B, Rong A, Zhou Q, et al. LncRNA LINC00662 promotes colon cancer tumor growth and metastasis by competitively binding with miR-340-5p to regulate CLDN8/IL22 co-expression and activating ERK signaling pathway. J Exp Clin Cancer Res 2020;39:5. [Crossref] [PubMed]
4. Khan K, Cascinu S, Cunningham D, et al. Imaging and clinical correlates with regorafenib in metastatic colorectal cancer. Cancer Treat Rev 2020;86:102020. [Crossref] [PubMed]
5. Dienstmann R, Villacampa G, Sveen A, et al. Relative contribution of clinicopathological variables, genomic markers, transcriptomic subtyping and microenvironment features for outcome prediction in stage II/III colorectal cancer. Ann Oncol 2019;30:1622-9. [Crossref] [PubMed]
6. Alexander PG, McMillan DC, Park JH. The local inflammatory response in colorectal cancer - Type, location or density? A systematic review and meta-analysis. Cancer Treat Rev 2020;83:101949. [Crossref] [PubMed]
7. Zhuang Z, Chen L, Mao Y, et al. Diagnostic, progressive and prognostic performance of m(6)A methylation RNA regulators in lung adenocarcinoma. Int J Biol Sci 2020;16:1785-97. [Crossref] [PubMed]
8. Endo S, Isohata N, Kojima K, et al. Prognostic factors of patients with left-sided obstructive colorectal cancer: post hoc analysis of a retrospective multicenter study by the Japan Colonic Stent Safe Procedure Research Group. World J Surg Oncol 2022;20:24. [Crossref] [PubMed]
9. Benson AB, Venook AP, Al-Hawary MM, et al. Colon Cancer, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2021;19:329-59. [Crossref] [PubMed]
10. Wang LM, Hirano YM, Ishii TM, et al. The role of apical lymph node metastasis in right colon cancer. Int J Colorectal Dis 2020;35:1887-94. [Crossref] [PubMed]
11. Ng TH, Sham KWY, Xie CM, et al. Eukaryotic elongation factor-2 kinase expression is an independent prognostic factor in colorectal cancer. BMC Cancer 2019;19:649. [Crossref] [PubMed]
12. Ma DJ, Price KA, Moore EJ, et al. Phase II Evaluation of Aggressive Dose De-Escalation for Adjuvant Chemoradiotherapy in Human Papillomavirus-Associated Oropharynx Squamous Cell Carcinoma. J Clin Oncol 2019;37:1909-18. [Crossref] [PubMed]
13. Ruan GT, Song MM, Zhang KP, et al. A novel nutrition-related nomogram for the survival prediction of colorectal cancer-results from a multicenter study. Nutr Metab (Lond) 2023;20:2. [Crossref] [PubMed]
14. Irlbeck T, Zwißler B, Bauer A. ASA classification: Transition in the course of time and depiction in the literature. Anaesthesist 2017;66:5-10. [Crossref] [PubMed]
15. Zhang QW, Zhang CH, Pan YB, et al. Prognosis of colorectal cancer patients is associated with the novel log odds of positive lymph nodes scheme: derivation and external validation. J Cancer 2020;11:1702-11. [Crossref] [PubMed]
16. Zhu J, Hao J, Ma Q, et al. A Novel Prognostic Model and Practical Nomogram for Predicting the Outcomes of Colorectal Cancer: Based on Tumor Biomarkers and Log Odds of Positive Lymph Node Scheme. Front Oncol 2021;11:661040. [Crossref] [PubMed]
17. Persiani R, Cananzi FC, Biondi A, et al. Log odds of positive lymph nodes in colon cancer: a meaningful ratio-based lymph node classification system. World J Surg 2012;36:667-74. [Crossref] [PubMed]
18. Iasonos A, Schrag D, Raj GV, et al. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 2008;26:1364-70. [Crossref] [PubMed]
19. Chandrakumaran K, Carr NJ, Mohamed F, et al. Development and Validation of Nomograms to Predict Survival in Patients Undergoing Complete Cytoreduction and Hyperthermic Intraperitoneal Chemotherapy for Pseudomyxoma Peritonei of Appendiceal Origin. JAMA Surg 2023;158:522-30. [Crossref] [PubMed]
20. Clavien PA, Barkun J, de Oliveira ML, et al. The Clavien-Dindo classification of surgical complications: five-year experience. Ann Surg 2009;250:187-96. [Crossref] [PubMed]
21. Hossain MS, Karuniawati H, Jairoun AA, et al. Colorectal Cancer: A Review of Carcinogenesis, Global Epidemiology, Current Challenges, Risk Factors, Preventive and Treatment Strategies. Cancers (Basel) 2022;14:1732. [Crossref] [PubMed]
22. Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl Oncol 2021;14:101174. [Crossref] [PubMed]
23. Fan A, Wang B, Wang X, et al. Immunotherapy in colorectal cancer: current achievements and future perspective. Int J Biol Sci 2021;17:3837-49. [Crossref] [PubMed]
24. Dekker E, Tanis PJ, Vleugels JLA, et al. Colorectal cancer. Lancet 2019;394:1467-80. [Crossref] [PubMed]
25. Sierzega M, Bobrzynski L, Kolodziejczyk P, et al. Nomogram-Based Prognostic Evaluation of Gastric Cancer Patients with Low Counts of Examined Lymph Nodes Outperforms the Predictive Ability of the 7(th) and 8(th) Editions of the American Joint Committee on Cancer Staging System. J Gastrointest Surg 2023;27:7-16. [Crossref] [PubMed]
26. Lord AC, D'Souza N, Pucher PH, et al. Significance of extranodal tumour deposits in colorectal cancer: A systematic review and meta-analysis. Eur J Cancer 2017;82:92-102. [Crossref] [PubMed]
27. Li Y, Wu G, Zhang Y, et al. Log odds of positive lymph nodes as a novel prognostic predictor for colorectal cancer: a systematic review and meta-analysis. BMC Cancer 2022;22:290. [Crossref] [PubMed]
28. Wei FZ, Mei SW, Chen JN, et al. Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy. World J Gastroenterol 2020;26:6638-57. [Crossref] [PubMed]
29. Gao J, Zhuang L, He C, et al. Risk and prognostic factors in patients with colon cancer with liver metastasis. J Int Med Res 2023;51:3000605231191580. [Crossref] [PubMed]
30. Kahi CJ, Boland CR, Dominitz JA, et al. Colonoscopy Surveillance After Colorectal Cancer Resection: Recommendations of the US Multi-Society Task Force on Colorectal Cancer. Gastroenterology 2016;150:758-768.e11. [Crossref] [PubMed]
31. Altintas S, Bayrak M. Assessment of Factors Influencing Lymph Node Count in Colorectal Cancer. J Coll Physicians Surg Pak 2019;29:1173-8. [Crossref] [PubMed]
32. Scarinci A, Di Cesare T, Cavaniglia D, et al. The impact of log odds of positive lymph nodes (LODDS) in colon and rectal cancer patient stratification: a single-center analysis of 323 patients. Updates Surg 2018;70:23-31. [Crossref] [PubMed]
33. Pei JP, Zhang CD, Fan YC, et al. Comparison of Different Lymph Node Staging Systems in Patients With Resectable Colorectal Cancer. Front Oncol 2018;8:671. [Crossref] [PubMed]
34. Sun X, Guo Y, Zhang Y, et al. Colon Cancer-Related Genes Identification and Function Study Based on Single-Cell Multi-Omics Integration. Front Cell Dev Biol 2021;9:789587. [Crossref] [PubMed]