Metastatic pancreatic adenocarcinomas could be classified into M1a and M1b category by the number of metastatic organs.

BACKGROUND: With the improvement of treatment and prognosis for patients with late malignant diseases, certain malignancies with distant metastasis (M1 category) have been further classified into M1a (single metastatic site) and M1b (multiple metastatic sites) category in the staging system. We aimed to assess the feasibility of sub-classifying metastatic pancreatic adenocarcinoma (mPA) into M1a and M1b category depending on the number of metastatic organs. METHODS: Patient records were collected from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015). Univariable and multivariable analyses were performed using the Cox regression model. Then survival analysis was determined using the Kaplan-Meier method. RESULTS: A total of 11,885 patients were included in this analysis, including 9425 patients with single metastasis and 2460 patients with multiple metastases. Multivariable analysis showed that gender, age, marital status, grade, surgery, chemotherapy, and radiotherapy were independent prognostic factors for patients with single metastasis; gender, age, marital status, grade, chemotherapy and radiotherapy were independent prognostic factors for patients with multiple metastases. Notably, surgery was an independent prognostic factor for patients with single metastasis (P < 0.001) but not for patients with multiple metastases (P = 0.134). Kaplan-Meier analysis showed that patients with single metastasis (M1a) had better survival outcomes than patients with multiple metastases (M1b) (P < 0.001). CONCLUSIONS: PA patients with M1 diseases could be divided into M1a (single metastasis) category and M1b (multiple metastases) category by the number of metastatic organs. The subclassification would facilitate individualized treatment for late PA patients. Surgery was associated with lower mortality in M1a patients but not significantly in M1b patients.

Industrial co-agglomeration, green technological innovation, and total factor energy efficiency.

The double-wheel driven of manufacturing and producer services industrial co-agglomeration is of great significance for transforming the economic growth mode driven by a single industry, integrating and extending regional resources, and improving energy efficiency. Based on panel data from 2004 to 2019, this paper uses the spatial Dubin model to analyze the impact of industrial co-agglomeration on total factor energy efficiency (TFEE) and its regional heterogeneity. Moreover, the mediating model is employed to examine the mediating effect of green technological innovation in the industrial co-agglomeration affects TFEE. Last but not least, the threshold panel regression model is conducted to verify the nonlinear relationship between industrial co-agglomeration and TFEE. The results show that there is a U-shaped curve relationship between industrial co-agglomeration and TFEE. Moreover, there are obvious regional heterogeneities in the impact of industrial co-agglomeration on TFEE and its spatial spillover effect. Meanwhile, industrial co-agglomeration has a significant indirect impact on TFEE through green technological innovation. In addition, there is a single threshold effect on the impact of industrial co-agglomeration on TFEE, only when the industrial co-agglomeration degree crosses the threshold value of 0.6329, can it positively promote the improvement of TFEE.

Increasing negative lymph node count predicts favorable OS and DSS in breast cancer with different lymph node-positive subgroups.

Adequate lymph node evaluation is recommended for optimal staging in patients with malignant neoplasms including breast cancer. However, the role of negative lymph nodes (LNs) remains unclear in breast cancer according to N substage (N1, N2, and N3). In this study, for the first time, we analyzed the prognostic significance of negative LNs in breast cancer patients. A critical relationship was observed between negative LN count and survival, independent of patient characteristics and other related molecular variables including estrogen receptor (PR) status, progesterone receptor (ER) status, human epidermal growth factor receptor 2 (HER2) status, depth of tumor invasion and degree of differentiation. This research is of great importance in providing more information about the prognosis of breast cancer by statistical analysis of negative lymph nodes and can serve as a useful supplement to the current pathological system.

Stage III should be subclassified into Stage IIIA and IIIB in the American Joint Committee on Cancer (8th Edition) staging system for pancreatic cancer.

AIM: To ascertain the prognostic role of the T4 and N2 category in stage III pancreatic cancer according to the 8th edition of the American Joint Committee on Cancer (AJCC) classification. METHODS: Patients were collected from the Surveillance Epidemiology and End Results (SEER) database (2004-2013) and were divided into three groups: T(1-3)N2, T4N(0-1), and T4N2. Overall survival (OS) and disease-specific survival (DSS) of patients were evaluated by the Kaplan-Meier method. RESULTS: For the first time, we found a significant difference in OS and DSS between T(1-3)N2/T4N(0-1) and T4N2 but not between T(1-3)N2 and T4N(0-1). A higher grading correlated with a worse prognosis in the T(1-3)N2 and T4N2 groups. CONCLUSION: Patients with stage T4N2 had a worse prognosis than those with stage T(1-3)N2/T4N(0-1) in the 8th edition AJCC staging system for pancreatic cancer. We recommend that stage III should be subclassified into stage IIIA [T(1-3)N2/T4N(0-1)] and stage IIIB (T4N2).

Lymph node status have a prognostic impact in breast cancer patients with distant metastasis.

BACKGROUND: The objective of this retrospective study was to determine whether lymph node metastasis has a prognostic impact on patients with stage IV breast cancer. PATIENTS AND METHODS: Seven thousand three hundred and seventy-nine patients with de novo stage IV breast cancer diagnosed from 2004 to 2013 were identified. Kaplan-Meier estimate method was fitted to measure overall survival and breast cancer-specific survival (BCSS). Cox proportional hazard analysis was used to evaluate the association between N stage and BCSS after controlling variables such as other patient/tumor characteristics. RESULTS: The primary site of M1 tumors was mainly upper-outer quadrant and overlapping lesion of the breast. Patients with N1 disease had better overall survival and BCSS than did those without lymph node metastasis. The overall survival and BCSS of M1 patients with N3 disease were significantly lower than that of those with N0, N1 and N2 disease, whereas patients with N2 and N0/N1 involvement showed no significant difference with survival. Multivariate analysis showed that lymph node metastasis was an important prognostic factor for M1 patients (N1 versus N0, hazard ratio [HR] = 0.902, 95% confidence interval [CI]: 0.825-0.986, p = 0.023; N3 versus N0, HR = 1.161, 95% CI: 1.055-1.276, p = 0.002). For M1 patients, age, race, marital status, primary site, ER, PR and HER2 were the independent prognostic factors. CONCLUSIONS: The cohort study provides an insight into de novo stage IV breast cancer with lymph node metastasis. Our results indicated that accurate lymph node evaluation for stage IV patients is still necessary to obtain important prognostic information.