A Prognostic Nomogram of postoperative colon mucinous adenocarcinoma: A Study of the US SEER Database and a Chinese Cohort

doi:10.21203/rs.3.rs-2503818/v1

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A Prognostic Nomogram of postoperative colon mucinous adenocarcinoma: A Study of the US SEER Database and a Chinese Cohort

https://doi.org/10.21203/rs.3.rs-2503818/v1

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Background: Colon mucinous adenocarcinoma (MAC) is a special pathological type of colon cancer, but the prognosis model of overall survival (OS) of MAC is lacking. The purpose of this study is to construct a prediction model to predict the 1-year, 3-year, and 5-year OS of patients with MAC.

Methods: Patients with MAC from 2010 to 2019 were extracted from Surveillance, Epidemiology, and End Results (SEER) database in US. Patients with MAC from 2016 to 2021 were extracted from Sichuan Cancer Hospital in China and which worked as the external validation cohort. All patients from SEER database were randomly divided into a training cohort and an internal validation cohort at a ratio of 7:3. A prognostic model was established with Cox regression analysis in the training cohort, which was validated by validation cohorts. The predictive abilities of the constructed models were evaluated by the concordance indexes (C-indexes), calibration curves, and decision curve analysis (DCA).

Results: A total of 1539 patients from the SEER database and 116 patients from Sichuan Cancer Hospital in China were included in the study. Age, AJCC T status, lymph node ratio (LNR), AJCC M status, and grade were independently associated with the OS of MAC in the training set, which were used to establish a model. The C-indexes of 1-, 3- and 5-year were higher than 0.70 in the training cohort, internal validation cohort, and external validation cohort, indicating the powerful performance of the present model.

Conclusions: The prediction model constructed in present study could accurately predict the 1-, 3-, and 5-year OS prognosis of MAC. External and internal validation cohortsboth show that the model has excellent performance. The model can help clinicians make rational clinical decisions.

Colon cancer

mucinous adenocarcinoma

SEER

Nomogram

Prognosis

Colorectal cancer (CRC) is the second most common malignancy in women and the third most common malignancy in men and is the second leading cause of cancer death.(1) In the United States, the number of new cases and deaths is expected to rank third in 2022.(2) In China, an estimated 592,232 new CRC cases and 309,114 deaths are expected in 2022.(3) Thanks to the application of artificial intelligence in colonoscopy, the mortality rate of CRC has been reduced to varying degrees, but patients with CRC should not be ignored.(4) Colon and rectum are different in many ways, involving anatomy, embryological origin, function, and treatments, and 71% of CRC is colon cancer (CC), so this research only studies CC.(5, 6)

The pathological types of CC are mainly adenocarcinoma. In addition to the non-specific adenocarcinoma, there have 9 specific subtypes of adenocarcinoma, namely mucinous adenocarcinoma (MAC), signet ring cell adenocarcinoma, medullary carcinoma, serrated adenocarcinoma, micropapillary adenocarcinoma, adenoma-like adenocarcinoma, Adenosquamous carcinoma, carcinomas with sarcomatoid components and undifferentiated carcinomas.(7) MAC is a unique pathological type of CC, and its diagnostic criterion is that more than 50% of the components in the tumor tissue contain extracellular mucous pools. The proportion of MAC in different areas is different, from 10% to 20%.(8, 9) Research has shown that patients with CRC whose pathological type is MAC have a worse prognosis than those with non-specific adenocarcinoma(10, 11). Patients with MAC are more likely to develop peritoneal metastases.(12) So far, only patients with colorectal MAC have predictive models for OS, while patients with colon MAC have no predictive models for OS, and previous predictive models lack external validation.(13-15) In clinical practice guidelines, the tumor-node-metastasis (TNM) stage system of the American Joint Committee on Cancer (AJCC) has been widely used in the treatment and prognosis of patients. However, the prognostic value of TNM in patients with colon MAC has not been reported. Considering the increase of multiple variables, it is necessary to explore a more comprehensive prognosis in the age of precision medicine.(16)

The number of samples restricted most studies due to the rarity of the MAC diagnosis. The Surveillance, Epidemiology, and End Results (SEER) database which covers about 28% of the U.S. population provides a relatively large sample size for studying such MAC. The current research has established a targeted prediction model for MAC based on the SEER database, hoping to further clarify the prognosis of patients with MAC. Finally, we also included an external validation to further verify the model, which would provide treatment advice for patients with different risks and help to specify individualized clinical decisions.

Patient selection

The data of patients in the present research were downloaded from the SEER∗ Stat 8.4.0.1 software. Patients with histological diagnosis as CC from 2010 to 2019 were included. Individual data retrieved for each case included age record, sex, race, tumor site, carcinoembryonic antigen (CEA), AJCC T stage, AJCC N stage, AJCC M stage, regional nodes examined, regional nodes positive, grade, perineural invasion (PNI), tumor size, chemotherapy, histology, surgery classification, vital status, and survival months. Meanwhile, patients were excluded if: (1) histological type not 8480 according to the International Classification of Disease for Oncology third edition (ICD-O-3); (2)age record≤18 or survival time < 1 month;(3) the information of age record, sex, race, tumor site, CEA, AJCC T stage, AJCC N stage, AJCC M stage, regional nodes examined, regional nodes positive, grade, perineural invasion, tumor size, chemotherapy, surgery classification, vital status, and survival months is unknown. All included MAC patients were randomly divided into a training set (70%) and an internal validation set (30%). The training set was used to identify the independent prognostic factors for MAC patients and establish the prognostic nomogram, and we use the internal validation set to validate the model. The flow chart is shown in Figure 1.

In addition, to further test the predictive power of the present model, the data of patients with pathologically confirmed colon MAC in Sichuan Cancer Hospital from 2016 to 2021 are used to compose an external validation set. The inclusion and exclusion criteria were consistent with those described above. The last follow-up was on October 30, 2022. The study was approved by the Ethics Review Committees of Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China.

Variable Collection

The variables included in the present study were age, sex, race, tumor site, CEA, AJCC T stage, AJCC N stage, lymph node ratio (LNR), AJCC M stage, grade, PNI, tumor size, chemotherapy, vital status, and survival months. The tumor site was classified as the right colon (including the cecum, the ascending colon, the hepatic flexure, and the transverse colon) and the left colon (including the splenic flexure and the descending colons, sigmoid colons). Tumor differentiation includes well-differentiated, moderately differentiated, poorly differentiated, and undifferentiated. The researchers classified the age at diagnosis as ＜80 years and ≥80 years. Tumor size was regrouped as ＜6 cm and ≥6 cm. LNR was defined as positive regional lymph nodes divided by all regional lymph nodes examined surgically. LNR was regrouped as ＜0.36 and ≥0.36. The re-segmentation of age, tumor size, and LNR were performed by X-tile software, as shown in Figure 2. Overall survival (OS) was chosen as the endpoint and defined as the time from diagnosis to the date of death for any cause.

Statistical analysis

The statistical analysis in this study was performed in R software (Version 4.1.3). A p-value <0.05 was considered statistically significant. First, the data from SEER were divided into the training set and internal validation set according to the ratio of 7:3 through the "caTools” package in R software, and then the univariate Cox analysis was used to determine OS-related factors in the training set. Second, the variables statistically significant in the univariate Cox analysis were included in the multivariate Cox analysis to identify the independent prognostic factors of MAC patients by the "survival” package in R software. Then, a nomogram was established by the “rms” package in R software based on independent prognostic factors determined by multivariate Cox analysis. Meanwhile, the receiver operating characteristic (ROC) curves at 1-, 3-, and 5-years were plotted, and the corresponding area under the curve (AUC) values were used to evaluate the discrimination of the model. Besides, the corresponding calibration curves were established to show the calibration of the model, and decision curve analysis (DCA) was performed to show the clinical benefit of the model. During the validation of the model, the total scores of each patient in two validation sets were calculated according to the model developed in the training set, then Cox regression in this cohort was performed using the total scores as a factor, and finally, the C-index, calibration curve and DCA curve were derived based on the regression analysis. The Kaplan-Meier survival curves were stratified according to the high and low risk generated in the training set, internal validation set, and external validation set. Furthermore, to confirm that the performance of the nomogram was better than TNM, the ROC curves of the model and the TNM were generated.

Clinicopathologic Characteristics

Table 1 丨Clinicopathologic features of colon MAC in the train set and the internal validation set

Characteristic	train set, N = 1,079	validation set, N = 460	p-value
Age			0.609
＜80	772 (72%)	335 (73%)
≥80	307 (28%)	125 (27%)
Sex			0.899
Female	548 (51%)	232 (50%)
Male	531 (49%)	228 (50%)
Race			0.301
Black	87 (8.1%)	30 (6.5%)
Other	120 (11%)	43 (9.3%)
White	872 (81%)	387 (84%)
T			0.460
T1	38 (3.5%)	20 (4.3%)
T2	136 (13%)	47 (10%)
T3	611 (57%)	272 (59%)
T4	294 (27%)	121 (26%)
N			0.510
N0	590 (55%)	237 (52%)
N1	272 (25%)	122 (27%)
N2	217 (20%)	101 (22%)
M			0.702
M0	939 (87%)	397 (86%)
M1	140 (13%)	63 (14%)
LNR			0.640
＜0.36	968 (90%)	409 (89%)
≥0.36	111 (10%)	51 (11%)
Grade			0.968
G1	97 (9.0%)	42 (9.1%)
G2	723 (67%)	313 (68%)
G3	206 (19%)	83 (18%)
G4	53 (4.9%)	22 (4.8%)
PIN			0.472
Yes	109 (10%)	41 (8.9%)
No	970 (90%)	419 (91.1%)
CEA			0.923
Negative	553 (51%)	237 (52%)
Positive	526 (49%)	223 (48%)
Size			0.169
＜6cm	564 (52%)	258 (56%)
≥6cm	515 (48%)	202 (44%)
Chemotherapy			0.353
Yes	430 (40%)	195 (42%)
No	649 (60%)	265 (58%)
Site			0.989
Left	249 (23%)	106 (23%)
Right	830 (77%)	354 (77%)

Table 2 丨Clinicopathologic features of colon MAC in the external validation set.

Characteristic	external validation set, N = 116
Age
＜80	107 (92.2%)
≥80	9 (7.8%)
T
T1	2 (1.7%)
T2	4 (3.4%)
T3	75 (64.7%)
T4	35 (30.2%)
M
M0	84 (72.4%)
M1	32 (27.6%)
LNR
＜0.36	107 (92.2%)
≥0.36	9 (7.8%)
Grade
G1	14 (12.1%)
G2	39 (33.6%)
G3 G4	63 (54.3%) 0 (0.0%)

A total of 1539 patients from the SEER database and 116 patients from a single center in China were eventually enrolled in this study according to the criteria of inclusion and exclusion. Patients from SEER were randomly divided into a training set (n=1079) and an internal validation set (n=460) at a ratio of 7 to 3. As shown in Table 1, there was no significant difference between the training set and the internal validation set through the Chi-square test. In the training set, 72% were younger than 80 years old, and 28% were older than 80 years old. Males accounted for 49% and females 51%. Caucasians have the largest share, accounting for 81%. The right colon which is about 3 times larger than the left colon accounted for 77%. The proportion of CEA negative and positive was almost the same, 51% and 49% respectively. T stage was mainly T3, accounting for 57%. N0, N1 and N2 accounted for 55%, 25%, and 20% respectively. The M stage was mainly M0, accounting for 87%. LNR < 0.36 dominated, accounting for 90%. G1, G2, G3, and G4 accounted for 9%, 67%, 19%, and 4.9% respectively. The patients with PNI accounted for 10.0%. <6cm and ≥6cm accounted for 52% and 48% respectively. 40% of the patients had experienced chemotherapy. The clinicopathological features of the external validation set are shown in Table 2.

Determining Prognostic Factors of MAC Patients in the Training Set

To determine OS-related variables, age, sex, race, tumor site, CEA, T, N, LNR, M, grade, PNI, tumor size, and chemotherapy were included in the univariate Cox analysis. The results are shown in Table 3, which showed that age, tumor site, CEA, T, N, LNR, M, grade, PNI, and tumor size was identified as OS-related variables. Subsequently, variables with statistical differences in univariate COX regression analysis were included in multivariate COX regression analysis, and the result of multivariate COX regression analysis indicated that age, T, N, LNR, M, and grade were independently associated with OS of MAC patients.

Development and Validation of the Prognostic Nomogram

To predict the OS of MCA, a nomogram was established based on all independent prognostic factors from the training set, which is shown in Figure 3. To test the performance of the model, a receiver operating curve is plotted and showed that the AUC values in 1-, 3-, and 5-years were 0.786, 0.795, and 0.788 respectively, which suggested the favorable discrimination of the nomogram. Then, the AUC values in 1-, 3- and 5-years were 0.730, 0.748, and 0.744 in the internal validation set and 0.859, 0.782, and 0.819 in the external validation set respectively. The above ROC curve is shown in Figure 4. Besides, the calibration curves indicated that the nomogram has a strong calibration, which is shown in Figure 5. Meanwhile, DCA was performed, and the results indicated that the nomogram has high clinical application value and could serve as an effective tool for clinical practice, which is shown in Figure 6.

Risk Stratification for MAC Patients

MAC patients can be divided into high-, and low-risk groups via the prognostic nomogram established in this study. As shown in Figure 7, the results of the Kaplan-Meier survival analysis with log-rank test suggested that significantly different survival patterns among patients existed in the two risk groups. Besides, patients both in the internal and external validation sets were also divided into two risk groups. The prognosis of patients in the low-risk group was better than that in the high-risk group. The above results suggested that the model can divide MAC patients into two groups with different prognoses to provide clinicians with a reference for decision-making.

Comparison of Predictive Accuracy

As shown in Figure 8, the AUC values of TNM in the training set and the validation sets were higher than 0.5. By comparing the predictive power between the nomogram and TNM, the AUC value of the nomogram constructed in this study was higher than TNM in 1-, 3- and 5-years both in the training set and the internal validation sets, suggesting the excellent performance of the nomogram.

MAC is a special pathological type of CC. Compared with non-specific adenocarcinoma, the prognosis of MAC is poor. Bacterial biofilms may have caused the formation of MAC.(17) The unique feature of MAC is mucin production. The higher the MUC1/MUC2 ratio, the worse the prognosis.(18) MAC has more mucin gene mutations and non-mucin gene mutations than non-specific adenocarcinoma.(19, 20) Abnormal activation of the SCF/c-KIT signaling pathway promotes mucin secretion.(21) This may be one of the reasons for the high secretion of mucin. MAC is more prone to mismatch repair protein deletion and KRAS and BRAF mutations.(22) MAC was inferior to AC in terms of TNM staging, differentiation, and sensitivity to chemotherapy.(23) In various clinical practice guidelines, the treatment of MAC currently adopts a comprehensive treatment mode mainly consisting of surgery, chemotherapy, and targeted therapy, and immunotherapy is also recommended for MSI-H. Consensus on the need for chemotherapy for MAC is not established. Some studies have identified chemotherapy as a protective factor, others as a risk factor.(24, 25) Therefore, this study analyzed the effects of chemotherapy on OS in patients with MAC, to guide the determination of treatment options. In addition, common clinicopathologic variables from the perspectives of basic data and pathological data were analyzed, and OS-related variables that were significant for multivariate analysis were included in the model to predict the prognosis of patients with MAC. Considering the low incidence of MAC, the combined method of the SEER database and Chinese single-center data was used to study the OS of patients. Univariate and multivariate COX regression analysis showed that age, T, N, LNR, M, and grade were independent prognostic factors for the OS of patients with MAC. When the model was applied to the validation cohort, it showed strong discrimination, calibration, and clinical application value, which provided powerful data support for further clinical application.

Advanced age is often an independent risk factor for OS in cancer patients. This study reveals for the first time that advanced age is independently associated with OS of colon MAC. The possible reasons are as follows. First, with the increase of age, chronic obstructive pulmonary disease, diabetes, cardiovascular disease, and cerebrovascular disease become more common. These diseases increase non-tumor specific mortality in patients with MAC and also can lead to a dysfunctional immune system, which in turn accelerates the progression of a patient's tumor.(26) In addition, younger patients are more likely to tolerate more systematic treatment, providing more possibilities for radical tumor resection.(27)

The TNM staging system is widely accepted and used in clinical staging systems. Its prognostic and therapeutic value has been widely confirmed in many cancer species. Generally speaking, if the stage increases, the patient's prognosis will be worse. This study demonstrates the value and limitations of the TNM staging system. The prognostic value of the LRN for MAC was analyzed because the prognostic value of LNR has been reported in other literature.(28) The results showed that patients with MAC had a worse prognosis as the LRN increased. This may be because unlike the N stage, which only analyzes lymph nodes from the single perspective of the number of positive lymph nodes, LRN analyzes the two dimensions of the number of lymph nodes detected during surgery and the number of positive lymph nodes, which analyzes lymph nodes more comprehensively. However, until now, there has been no consensus on the optimal cut-off value of LNR, which needs to be explored in future multi-center randomized controlled trials. Distant metastasis is a common cause of death in patients with colon cancer, among which liver metastasis is the most common.(29) This study found that the hazard ratio of distant metastasis was relatively high. Therefore, comprehensive treatment should be used to actively treat distant metastatic lesions if the patient's body allows it. Since mucinous adenocarcinoma is more prone to distant metastasis, a close review may be warranted.

Differentiation is one of the common prognostic factors of malignant tumors but is not of prognostic value in all malignancies.(30, 31) In addition, the degree of differentiation affects the sensitivity of radiotherapy and chemotherapy.(32) The study strongly confirmed that the degree of differentiation is an independent risk factor for the prognosis of patients with MAC. Notably, patients with moderately differentiated mucinous colon adenocarcinoma had a better prognosis than those with highly differentiated mucinous colon adenocarcinoma. The results showed that the prognosis and differentiation of mucinous adenocarcinoma patients were not linear. The mechanism of differentiation on prognosis needs further investigation.

Gender, race, site, CEA, PIN, and size are common prognostic factors in CC patients.(33-37) This study demonstrated that all the factors mentioned above were not independent prognostic factors for OS in patients with MAC. Different studies have different results, and there is no consensus on the effect of the above factors on the prognosis of CC patients. This may be because different studies have different populations and different subgroup analyses, but the effect of these factors on the prognosis of patients with colonic MAC has been elucidated for the first time.

Chemotherapy is one of the important treatment methods for patients with CC. According to clinical guidelines, chemotherapy can effectively improve the survival prognosis of stage 2 high-risk patients, stage 3 patients, and stage 4 patients. In this study, chemotherapy was not associated with OS in patients with MAC. The reason may relate to somatic mutations in genes associated with resistance to 5-FU, oxaliplatin, and irinotecan.(38) Meanwhile, the anti-cell death effect of mucin should not be ignored.(39) Notably, MAC is more prone to microsatellite instability, suggesting that MAC patients are more likely to benefit from immunotherapy.(40)

Compared with the existing prognosis models of MAC, the model established in this study still has certain advantages.(13-15) First of all, the study for the first time isolates colonic MAC from CRC MAC and validates the established model using real-world data. This lays a certain foundation for the further study of colonic MAC. Secondly, many clinicopathological features should be included in the study to make the study as comprehensive as possible. In this study, LNR was included in the prognosis of MCA for the first time and the prognostic value of LNR in patients with MAC was validated for the first time. At the same time, X-tile software is used to classify continuous variables, which determines the best cutoff values of the continuous variable more scientifically and reasonably. Thirdly, every patient in this study underwent surgical treatment for the primary lesion, which reduced heterogeneity to some extent.

In this study, there are also some limitations. First, variable information in the SEER database is limited. As gene information, microsatellite status, lymphatic and vascular invasion, and other pathologic information related to prognosis are not available, which may lead to the absence of more comprehensive prognostic factors. In addition, specific chemotherapy regimens could not be obtained, and only a general analysis of the effects of chemotherapy on patients with MAC could be made. Second, the sample size of the external verification set is not large enough, and it is only single-center data, which provides a possibility for the generation of bias. Third, this study is a retrospective study, so the inherent defects of a retrospective study are inevitable. To overcome the above deficiencies, in the future, a multi-center, multi-observation factor, large sample randomized controlled experiment is worth looking forward to.

In summary, we found that patients with advanced age, higher T stage, larger LNR, distant metastasis, and poor differentiation had a worse prognosis. The prognostic model containing the above 5 independent prognostic factors can accurately predict the OS of MAC patients.

MAC: mucinous adenocarcinoma; OS: overall survival; SEER: Surveillance, Epidemiology, and End Results; C-indexes: concordance indexes; DCA: decision curve analysis; LNR: lymph node ratio; CRC: Colorectal cancer; CC: colon cancer; TNM: tumor-node-metastasis; AJCC: American Joint Committee on Cancer; CEA: carcinoembryonic antigen; PNI: perineural invasion; ICD-O-3:International Classification of Disease for Oncology third edition; ROC: receiver operating characteristic; AUC: area under the curve.

Ethics approval and consent to participate

The SEER database used in this study is a public database, so there is no need for an ethical approval. The data from Sichuan Cancer Hospital has been approvaled by the Ethics Committee of Sichuan Cancer Hospital.

Consent for publication

Not applicable

Availability of data and materials

Publicly available datasets were analyzed in this study. This data can be found here: Surveillance, Epidemiology, and End Results (SEER) database (https://seer.cancer.gov/). China single center data from Sichuan Cancer Hospital.

Competing interests

The authors declare that they have no competing interests.

Funding

There is no funding for this research.

Authors' contributions

Rui Liu and Yinjie Zhang designed the study. Rui Liu conducted statistical analysis of the data, and explained the results, and wrote the manuscript. Jiahui Zhang, Yinjie Zhang collected and organized the data, and made figures and tables. Jin Yan revised and improved the manuscript. All authors reviewed the manuscript.

Acknowledgements

Not applicable.

Authors' information

Rui Liu: [email protected]

Yinjie Zhang: [email protected]

Jiahui Zhang: [email protected]

Jin Yan: [email protected].

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Table 3 is available in the Supplementary Files section.

No competing interests reported.

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A Prognostic Nomogram of postoperative colon mucinous adenocarcinoma: A Study of the US SEER Database and a Chinese Cohort

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Table 3

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