Colorectal cancer (CRC) is the 3rd and 2nd most common cancer in men and women worldwide, respectively, and accounts for about 10% of deaths from all cancers (1). In China, CRC is also a prevalent malignant tumor detrimental to the digestive system. According to recently released data from GLOBOCAN, an estimated 555,477 newly diagnosed CRC cases and 286,162 CRC-related deaths occurred in China in 2020 (2). With its high morbidity and mortality rates, CRC places a heavy burden on both society and individuals (3).
At present, CRC is primarily treated with integrated therapies focused on surgery. However, the rates of survival among CRC patients vary significantly depending on the cancer staging at the time of diagnosis. For example, the 5-year survival rate of patients reaches 90% if the tumors are locally controlled, but plummets to only 13% in cases of remote metastasis (4). This is because the early symptoms of CRC remain hidden, and CRC only tends to be detected at later stages. Consequently, the optimal timing for surgery is often missed.
Generally speaking, unlike a sudden pathological change, CRC progresses in the order of normal mucous glandular epithelium, low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, and adenocarcinoma (5,6). Thus, early CRC detection and treatment are crucial if patient prognosis is to be improved. Currently, early CRC screening is mainly performed through a fecal occult blood test (FOBT) and enteroscopy. However, enteroscopy is an invasive procedure that patients often are reluctant to undergo. Additionally, due to the limited availability related to the complexity, costliness, and time-consuming process of the procedure, it would be unrealistic to conduct mass enteroscopies in China (7). Conversely, FOBT can be easily performed, but has poor reliability as a single indicator, and thus tends to produce unsatisfactory results (8).
Considerable research has been conducted using tumor markers for examination as an non-invasive diagnostic method (9). However, issues arise in the use of tumor markers for CRC diagnosis, as the levels of tumor markers may be increased in patients with both benign and malignant tumors and inflammatory diseases in other systems. As CRC is highly heterogeneous, a single tumor marker is not usually as sensitive or specific as an independent diagnostic indicator (10). Thus, this study intends to construct an easy-to-implement method for predicting CRC by combining several clinical indicators to provide a basis for early clinical detection and diagnosis of CRC. We present the following article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-22-917/rc).
The CRC group comprised 65 patients who had undergone a radical operation after receiving a diagnosis of colon adenocarcinoma at Tianjin Hospital in Tianjin, China from January 2017 to March 2022. To be eligible for inclusion in this study, patients had to meet the following inclusion criteria: (I) have received a pathological diagnosis as per the guidelines of the World Health Organization (WHO) and have undergone a properly performed post-operative pathological examination and review by two pathologists; and (II) have complete clinical and follow-up data. Patients were excluded from the study if they met any of the following exclusion criteria: (I) had dual or multiple primary malignancies; (II) were prone to genetic malignant transformations, such as inflammatory enteropathy and familial polyposis; (III) had undergone radiochemotherapy; and/or (IV) had contracted any serious infectious disease in the past 3 months. The control (CON) group comprised the serum specimens of 162 randomly selected healthy adult patients who had received a checkup in the above period. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Tianjin Hospital (No. 2022-150) and informed consent was taken from all the patients.
Indicators for clinical observation
Data on gender, age, body mass index (BMI), FOBT, smoking, appendectomy, and cholecystectomy were collected from patients’ past medical histories. The testing data collected included hemoglobin, albumin, bile acid, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA199), serum ferritin, and Helicobacter pylori (H. pylori) infection data.
The FOBT was conducted using the gold-standard method; reagent was purchased from Siemens. Hemoglobin and NLR were tested with a Sysmex hematology analyzer; reagent was purchased from Sysmex. The levels of albumin and bile acid were tested with a fully automated Siemens chemistry analyzer; reagent was purchased from Siemens. CRP was tested with a Siemens POCT immunometer; reagent was purchased from Siemens. CEA and CA199 were tested by electrochemiluminescence; reagent was purchased from Roche. The carbon-14 breath test was performed for H. pylori; reagent was purchased from Shanghai Biohub International. Quality control was ensured across all the tests.
SPSS 26.0 software was employed for the statistical analysis of the data. The quantitative data are expressed as the . A normality test or homogeneity of variance test was performed first, and the normally distributed data were compared between the groups by means of an independent samples t-test. The non-normally distributed data are expressed as the median (interquartile range), and the rank-sum test was used for the intergroup comparisons. The enumeration data are expressed as the frequency, and the χ2 test was used for the intergroup comparisons.
A joint model was built for CRC prediction by including the statistically significant indicators from the univariate analysis in the multivariate logistic regression analysis and screening the independent risk factors for CRC. Next, the model’s prediction performance was assessed using the receiver operating characteristic (ROC) curve. R software (V4.1.2) and the rms package were used to create the nomogram model. Bootstrap re-sampling was performed for internal verification, and a calibration curve was drawn. A P value <0.05 was considered statistically significant.
The CRC group comprised 65 patients (35 men and 30 women) with a median age of 68 years. Among the 65 patients, 23 were well differentiated, 34 were moderately differentiated, and 8 were poorly differentiated. Further, 15 had lymph node metastasis, and 50 did not. In relation to the TNM (Tumor Node Metastasis) staging, 11 were at stage I, 38 were at stage II, 14 were at stage III, and 2 were at stage IV (with liver metastasis). The CON group comprised 162 patients (95 men and 67 women) with a median age of 69 years. An intergroup comparison of gender and age revealed no statistically significant differences between the two groups (P>0.05).
The univariate analysis of the available data revealed statistically significant differences between the two groups in terms of smoking, fecal occult blood, H. pylori infection, a history of appendectomy, serum total bile acid levels, serum CEA levels, serum CA199 levels, and serum ferritin levels (P<0.05; see Table 1).
|Clinical indicators||CON group||CRC group||Statistic||P value|
|H. pylori infection (P/N)||20/142||30/35||30.87||<0.001|
|Serum CEA (ng/mL)||1.13±0.20||8.58±2.49||37.82||<0.001|
|Total bile acid (μmol/L)||2.87±0.33||3.96±0.46||19.80||<0.001|
|Serum CA199 (U/mL)||22.52±19.47||56.94±56.59||6.82||0.001|
Data are presented as No. and mean ± standard deviation. CRC, colorectal cancer; CON, control; Y/N, yes/no; P/N, positive/negative; FOBT, fecal occult blood test; BMI, body mass index; SF, serum ferritin; NLR, neutrophil-to-lymphocyte ratio; CRP, C-reactive protein.
Multivariate logistic regression analysis
The statistically significant indicators in the univariate analysis were then included in the multivariate logistic regression analysis. The results revealed that smoking, fecal occult blood, H. pylori infection, a history of appendectomy, serum CEA levels, and serum CA199 levels were independent risk factors for CRC (see Table 2).
|Clinical indicators||Regression coefficient b||Wald-statistic||OR (95% CI)||P value|
|H. pylori infection||1.487||3.62||6.26 (2.41–17.75)||0.0011|
|Serum CEA||1.234||3.21||23.57 (8.45–80.37)||0.0001|
|Serum CA199||2.236||4.45||14.65 (4.68–53.99)||0.0001|
CRC, colorectal cancer; FOBT, fecal occult blood test; CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 19-9; OR, odds ratio.
Joint prediction model construction and assessment
On the basis of the results of the multivariate logistic regression analysis, a joint prediction model was built. The following formula was used:
where X1 refers to smoking, X2 refers to H. pylori infection, X3 refers to a history of appendectomy, X4 refers to fecal occult blood, X5 refers to serum CEA levels, and X6 refers to serum CA199 levels. Next the equation P=1/(1 + exp(–Y)) was used to calculate the predictive value of the model for each patient. Afterwards, the model was assessed based on the ROC curve. The results showed that the model’s area the under curve (AUC) of ROC was 0.842, indicating that its diagnostic performance was superior to that of each of the other single indicators (see Table 3 and Figure 1).
|Clinical indicators||AUC||95% CI||P value|
|H. pylori infection||0.632||0.549–0.716||0.0173|
|Fecal occult blood||0.597||0.513–0.680||0.0231|
|Serum CEA levels||0.680||0.599–0.761||0.0001|
ROC, receiver operating characteristic; CRC, colorectal cancer; CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 19-9; AUC, area the under curve.
Nomogram and calibration curve preparation
According to the results of the multivariate logistic regression analysis, the nomogram function in the rms package in R was used to draw a nomogram, and the calibration function for a calibration curve. The analysis of the calibration curve indicated that the nomogram was suitable for predicting the onset of CRC (see Figures 2,3).
Following improvements in surgical techniques, the 5-year survival rate of CRC patients after surgery has been rising steadily and has now reached 50–60%; however, the overall survival rate of CRC patients remains <60% (11). In addition to the biological features of CRC, surgical therapy plus early detection and diagnosis are crucial to increasing patients’ survival rates. In this study, we examined multiple factors related to the onset and progression of CRC, including smoking, fecal occult blood, H. pylori infection, a history of appendectomy, serum CEA levels, and serum CA199 levels and then developed a joint prediction model to offer an optimal plan for the screening of CRC.
It has long been thought that smoking significantly increases the risk of malignant tumors in many systems, including the lung, oral cavity, throat, esophagus, bladder, kidney, and pancreas, but its role in the morbidity of CRC patients was only recently established (12). The results of our multivariate analysis showed that smoking is an independent risk factor for the onset of CRC. Similarly, Botteri et al. (13) analyzed the effects of smoking on the risk of CRC, and found that the risk of CRC increases in a dose-dependent manner with rises in the duration and intensity of smoking. In relation to the mechanism of the carcinogenicity of smoking, the 69 different carcinogenic compounds in tobacco smoke affect the onset and progression of tumors through various channels, such as tumor-related genes (14). Thus, quitting smoking does reduce the risk of CRC.
The FOBT, a regular checkup item in CRC screening, is primarily conducted in chemical and immunological ways. The chemical testing method is prone to interference from dietary and living factors, and thus can produce false positive results. The immunological testing method causes the bloody feces, which stay too long in the intestine, to loose antigenicity, and thus can produce false negative results (15). Thus, the simultaneous application of both methods may help to enhance the accuracy of testing. Notwithstanding the above, as a single diagnostic indicator, the FOBT has poor sensitivity (16).
Based on the finding that fecal occult blood remains an independent factor for CRC prediction, this study included it in the joint prediction model and attained a higher diagnostic value. This study showed that serum CEA level, like fecal occult blood, is an independent factor for CRC prediction, but it cannot be used as a single prediction indicator due to its low positive rate (of approximately 25%) in early CRC (17). However, it plays a significant role in predicting post-operative relapses of CRC.
Studies (18,19) recently reported that H. pylori infection, which is mostly considered a risk factor for gastric cancer (20), is associated with the onset of CRC. In relation to its mechanism, Holmes et al. (21) observed a significant increase in the serum gastrin levels of CRC patients compared to normal patients. The hypergastrinemia may be attributed to the colonization of H. pylori in the stomach. Assuming that H. pylori enlarges the risk of CRC by increasing serum gastrin levels, we hypothesized that eradicating H. pylori infection could be a major approach for CRC prevention. To clarify the connection between H. pylori infection and CRC, we included it in this study and proved that it is an independent risk factor for CRC. However, the mechanism behind the increased risk of CRC as a result of H. pylori infection requires further research and our conclusion needs to be proven in large multi-center studies.
There has been reported that appendectomy increases the risk of CRC (22). One theory of its possible mechanism is that the appendix is essential to the development of lymphatic tissues in the intestinal tract and a lack of such tissues hinders inflammation and reduces disruptive self-reactions in the large intestine. Another theory suggests that appendectomy alters the structure and function of the intestinal florae (23). The appendix hosts a multitude of intestinal florae, which differ from those in the large intestine in terms of structure and may re-enter the large intestine in the event of appendicitis. The microscopic florae in the appendix exist as biological membrane, contact the epithelial cells in the large intestine, and may promote the onset and progression of CRC through the process of an adenoma (24). However, further evidence is required to ascertain the details of the mechanism. According to the present study in which appendectomy was included as an indicator, more cases a history of appendectomy were found among the CRC patients than the normal patients (P<0.05), and appendectomy was later proven to be an independent risk factor for the onset of CRC.
Following the continuous development in molecular biology and immunological technology, serum tumor markers have gradually been identified based on the knowledge that malignant tumors in different systems have relatively specific markers. As our understandings deepen, research has shown that a single marker tends to be extremely insensitive and unspecific in the diagnosis of malignant tumors of certain systems due to the polymorphism of tumors and the interference by some non-tumor diseases (25).
Tumor markers, such as CEA and CA199, have been shown to be highly expressed in the malignant tumors of multiple systems, including CRC tissues. The rate of CEA positive expression is only 40% or so among CRC patients, which indicates that it has low sensitivity and specificity (26). CA199 is mainly secreted by tumors in the digestive system, especially pancreatic cancer and CRC. Each of these markers, when used as a single indicator, has been shown to have low sensitivity and specificity (27). However, a previous study (28) suggested that the combined use of CA199 with other factors greatly enhances the accuracy of CRC diagnosis. However, it is not yet known whether the diagnostic accuracy can be improved by the simultaneous usage of multiple tumor markers. Our multivariate logistic analysis suggested that the levels of serum CEA and CA199 are independent risk factors for CRC. We included them in a prediction model, and found that the diagnostic performance of the model was superior to that of each single indicator alone, which also shows that the combination of multiple indicators increased the diagnostic accuracy of the model. The specificity and accuracy were significantly improved compared to the related study by Ding et al. (29).
In this study, we built a joint model for CRC prediction based on a multivariate logistic regression analysis, and achieved satisfactory values through internal verification. Further, a basic assessment showed that the CRC risk nomogram based on the model had good predictive value. Thus, we established a feasible and simple approach for clinical CRC screening. However, as the study involved a small number of cases from a single center, the model needs to be optimized by increasing the sample count and including data from multiple centers.
Funding: This research was supported by grants from the National Nature Science Foundation of China (Grant No. 81101870), the Natural Science Foundation of China (No. 81801240), the Project of Science and Technology Department of Chinese Anti-Cancer Association (Grant No. CORP-258:2021001045), the CAMS Innovative Fund for Medical Science (Grant No. 2018-I2M-AI-012), and the Science and Technology Research Development Plan of Tangshan City in 2019 (Grant No. 19150207E).
Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-22-917/rc
Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-22-917/dss
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-22-917/coif). All authors report that this research was supported by grants from the National Nature Science Foundation of China (Grant No. 81101870), the Natural Science Foundation of China (No. 81801240), the Project of Science and Technology Department of Chinese Anti-Cancer Association (Grant No. CORP-258:2021001045), the CAMS Innovative Fund for Medical Science (Grant No. 2018-I2M-AI-012), and the Science and Technology Research Development Plan of Tangshan City in 2019 (Grant No. 19150207E). The authors have no other 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). This study was approved by the Ethics Committee of Tianjin Hospital (No. 2022-150). Each patient provided written informed consent.
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/.
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(English Language Editor: L. Huleatt)