The new 'coN' staging system combining lymph node metastasis and tumour deposit provides a more accurate prognosis for TNM stage III colon cancer.

OBJECTIVE: Despite controversy over its origin and definition, the significance of tumour deposit (TD) has been underestimated in the tumour node metastasis (TNM) staging system for colon cancer, especially in stage III patients. We aimed to further confirm the prognostic value of TD in stage III colon cancer and to establish a more accurate 'coN' staging system combining TD and lymph node metastasis (LNM). METHODS: Information on stage III colon cancer patients with a definite TD status was retrospectively collected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017. The effect of TD on prognosis was estimated using Cox regression analysis. Maximally selected rank statistics were used to select the optimal cut-off value of TD counts. The predictive power of conventional N staging and the new coN staging was evaluated and compared by Akaike's information criterion (AIC), Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves. Clinicopathological data of stage III colon cancer patients in the Xiangya database from 2014 to 2018 were collected to validate the coN staging system. RESULTS: A total of 39,185 patients with stage III colon cancer were included in our study: 38,446 in the SEER cohort and 739 in the Xiangya cohort. The incidence of TD in stage III colon cancer was approximately 30% (26% in SEER and 30% in the Xiangya database). TD was significantly associated with poorer overall survival (OS) (HR = 1.37, 95% CI 1.31-1.44, p < 0.001 in SEER). The optimal cut-off value of TD counts was 4, and the patients were classified into the TD0 (count = 0), TD1 (count = 1-3) and TD2 (count ≥ 4) groups accordingly. The estimated 5-year OS was significantly different among the three groups (69.4%, 95% CI 68.8%-70.0% in TD0; 60.5%, 95% CI 58.9%-62.2% in TD1 and 42.6%, 95% CI 39.2%-46.4% in TD2, respectively, p < 0.001). The coN system integrating LNM and TD was established, and patients with stage III colon cancer were reclassified into five subgroups (coN1a, coN1b, coN2a, coN2b and coN2c). Compared with conventional N staging, the coN staging Cox model had a smaller AIC (197097.581 vs. 197358.006) and a larger C-index (0.611 vs. 0.601). The AUCs of coN staging at 3, 5 and 7 years were also greater than those of conventional N staging (0.6305, 0.6326, 0.6314 vs. 0.6186, 0.6197, 0.6160). Concomitant with the SEER cohort results, the coN staging Cox model of the Xiangya cohort also had a smaller AIC (2883.856 vs. 2906.741) and a larger C-index (0.669 vs. 0.633). Greater AUCs at 3, 5 and 7 years for coN staging were also observed in the Xiangya cohort (0.6983, 0.6774, 0.6502 vs. 0.6512, 0.6368, 0.6199). CONCLUSIONS: Not only the presence but also the number of TDs is associated with poor prognosis in stage III colon cancer. A combined N staging system integrating LNM and TD provides more accurate prognostic prediction than the latest AJCC N staging in stage III colon cancer.

Prognostic Value of the N1c in Stage III and IV Colorectal Cancer: A Propensity Score Matching Study Based on the Surveillance, Epidemiology, and End Results (SEER) Database.

The prognostic role of the N1c remains unclear in colorectal cancer (CRC). Our study aimed to determine the prognostic value of N1c.Patients diagnosed in 2010-2015 were accessed from the Surveillance, Epidemiology, and End Results (SEER) database. COX univariate and multivariate regression analysis and the Kaplan-Meier method were used to assess the impact of the N1c stage on the cause-specific (CSS) and overall survival (OS). Propensity score matching (PSM) was used to construct a matched group with similar propensity scores.Kaplan-Meier analysis showed that the CSS and OS rates in N1a were significantly better than N1c in stage III and IV CRCs after reducing selection bias (CSS: P < 0.001 in stage III, P = 0.041 in stage IV; OS: P < 0.001 in stage III, P = 0.0079 in stage IV). There were no statistical differences in CSS and OS between N1b and N1c (CSS: P = 0.500 in stage III, P = 0.270 in stage IV; OS: P = 0.390 in stage III, P = 0.600 in stage IV). Further, the prognostic value of N1c with only one tumor deposit (TD) is equivalent to N1a based on the comparison of CSS and OS rates (CSS: P = 0.420; OS: P = 0.310). Whereas N1c with only one TD had significantly better CSS and OS than N1b (CSS: P = 0.039; OS: P = 0.037).The CSS and OS rates of N1c do not achieve a statistical difference with N1b in both stage III and IV CRCs. Significantly, higher CSS and OS rates were found in N1c with only one TD versus N1b stage in stage III CRC.

A Nomogram for Predicting Lymph Nodal Metastases in Patients with Appendiceal Cancers: An Analysis of SEER Database.

BACKGROUNDS: Appendiceal cancers are usually diagnosed after appendectomy accidentally. The need for subsequent right hemicolectomy in these patients was determined by the potential risk of regional lymph node (LN) metastasis. Establishing a nomogram to forecast the potential risk of lymph node metastasis of appendiceal cancer could help in the next step of treatment. METHODS: Patients with appendiceal cancer undergoing surgery was queried in the American cancer database of Surveillance, Epidemiology and End Results database from 2004 to 2016. A nomogram was established based on Logistic regression model. RESULTS: Finally, 3,075 patients were diagnosed with appendectomy cancer from 2004 to 2016. Among them, there were 2028 (65.9%) cases with negative lymph nodes, 1047 (34.1%) cases with positive lymph nodes. Risk factors associated with lymph node metastasis include age, histological type, tissue grade, T stage, distant metastasis, and tumor size. We drew the ROC curves of the training group（0.754, P < 0.001） and the validation group （0.775, P < 0.001） respectively. C-index values of predictions were 0.772 (95%CI, 0.750-0.793) and 0.776 (95%CI, 0.746-0.807), and Brier score were 0.178 and 0.172 in training and validation group respectively. All of them showed excellent performance of the nomogram in our study. CONCLUSION: A new nomogram was created to assess the potential risk of LN metastasis in patients of appendiceal cancer by utilizing age, tumor histology, tumor pathologic grade, tumor size, T-stage, and M-stage. The nomogram could provide a strong reference for the right hemicolectomy and facilitate clinic decision.

Effect of neoadjuvant radiotherapy on survival of non-metastatic pancreatic ductal adenocarcinoma: a SEER database analysis.

BACKGROUND: Neoadjuvant radiotherapy has been shown to improve marginal negative resection and local control of Pancreatic Ductal Adenocarcinoma (PDAC). However, whether it improves overall survival (OS) in patients with non-metastatic PDAC remains controversial. Therefore, the purpose of this study was to analyze the benefits of only surgery, neoadjuvant radiotherapy, adjuvant radiotherapy, and surgery plus chemotherapy for OS in patients with non-metastatic PDAC. METHODS: PDAC diagnosed by surgical histopathology in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016 was selected. Kaplan-Meier analysis was used to compare the prognosis of patients with different treatments. Cox proportional risk model was used to analyze independent predictors of OS. Propensity score matching (PSM) was used to analyze the tumor prognosis of different treatment methods. RESULTS: Before PSM analysis, the OS of surgery plus chemotherapy (HRs = 0.896, 95%CIs, 0.827-0.970; P = 0.007) were significantly better than the other three treatments for stage T1-3N0M0 PDAC patients. For stage T1-3N + M0 patients, adjuvant radiotherapy (HRs = 0.613, 95% CIs, 0.579-0.649; P < 0.001) had significantly better OS than surgery plus chemotherapy and neoadjuvant radiotherapy. For stage T4N0M0 patients, neoadjuvant radiotherapy (HRs = 0.482, 95% CIs, 0.347-0.670; P < 0.001) had significantly better OS than surgery plus chemotherapy and adjuvant radiotherapy. For stage T4N + M0 patients, neoadjuvant radiotherapy (HRs = 0.338, 95% CIs, 0.215-0.532; P < 0.001) had significantly longer OS than adjuvant radiotherapy and surgery plus chemotherapy. Even after PSM, Chemotherapy plus surgery was still the best treatment for T1-3N0M0 patients. Postoperative adjuvant radiotherapy had the best prognosis among T1-3N + M0 patients, and neoadjuvant radiotherapy was the best treatment for T4 patients. CONCLUSIONS: For patients with non-metastatic PDAC, neoadjuvant radiotherapy, adjuvant radiotherapy and surgery plus chemotherapy were superior to only surgery in OS. For patients with stage T4 non-metastatic PDAC, neoadjuvant radiotherapy had the potential to be strongly recommended over adjuvant radiotherapy and surgery plus chemotherapy. However, neoadjuvant radiotherapy failed to benefit the survival of T1-3N0M0 stage patients, and surgery plus chemotherapy was preferred. For T1-3N + M0, neoadjuvant radiotherapy had no obvious advantage over adjuvant radiotherapy or surgery plus chemotherapy in OS, and adjuvant radiotherapy was more recommended.

Matrix Stiffness and Colorectal Cancer.

In recent years, a growing consensus is emerging that the mechanical microenvironment of tumors is far more critical in the onset of tumor, tumor progression, invasion, and metastasis. Matrix stiffness, one of the sources of mechanical stimulation, affects tumor cells as well as non-tumor cells in multiple different molecular signaling pathways in solid tumors such as colorectal tumors, which lead to tumor invasion and metastasis, immune evasion and drug resistance. This review will illustrate the relationship between matrix stiffness and colorectal cancer from the following aspects. First, briefly introduce the mechanical microenvironment and colorectal cancer, then explain the origin of colorectal cancer extracellular matrix stiffness, and then synthesize the study of matrix stiffness of colorectal cancer in recent years to elaborate the effects of extracellular matrix stiffness in colorectal cancer's biological behavior and signaling pathways, and finally we will discuss the transformation treatment for the matrix stiffness of colorectal cancer. An in-depth understanding of matrix stiffness and colorectal cancer can help researchers conduct further experiments to find new targets for the treatment of colorectal cancer.