Search

ObjectiveBreast cancer is highly heterogeneous, presenting challenges in prognostic assessment. Developing a universally applicable prognostic model could simplify clinical decision-making. This study aims to develop and validate a novel breast cancer prognosis model using coagulation-related genes with broad clinical applicability.MethodsA total of 203 genes related to coagulation were obtained from the KEGG database, and the mRNA data of 1,099 tumor tissue samples and 572 samples of normal tissue were retrieved from the TCGA-BRCA cohort and GTEx databases. The R package “limma” was utilized to detect variations in gene expression related to coagulation between the malignancies and normal tissue. A model was constructed in the TCGA cohort through a multivariable Cox regression analysis, followed by validation using the GSE42568 dataset as the testing set. Constructing a nomogram incorporating clinical factors to enhance the predictive capacity of the model. Utilizing the ESTIMATE algorithm to investigate the immune infiltration levels in groups with deferent risk. Performing drug sensitivity analysis using the “oncoPredict” package.ResultsA risk model consisting of six coagulation-associated genes (SERPINA1, SERPINF2, C1S, CFB, RASGRP1, and TLN2) was created and successfully tested for validation. Identified were 6 genes that serve as protective factors in the model’s development. Kaplan-Meier curves revealed a worse prognosis in the high-risk group compared to the low-risk group. The ROC analysis showed that the model accurately forecasted the overall survival (OS) of breast cancer patients at 1, 3, and 5 years. Nomogram accompanied by calibration curves can also provide better guidance for clinical decision-making. The low-risk group is more likely to respond well to immunotherapy, whereas the high-risk group may show improved responses to Gemcitabine treatment. Furthermore, individuals in distinct risk categories displayed different responses to various medications within the identical therapeutic category.ConclusionWe established a breast cancer prognostic model incorporating six coagulation-associated genes and explored its clinical utility. This model offers valuable insights for clinical decision-making and drug selection in breast cancer patients, contributing to personalized and precise treatment advancements.

Abstract Background Due to the rarity of PBL and the lack of large-scale studies, the prognostic value of IPI in PBL was controversial. Especially in the rituximab era, the ability of IPI to stratify prognosis in patients receiving immunochemotherapy was severely reduced. Then revised IPI (R-IPI) and National Comprehensive Cancer Network IPI (NCCN-IPI) were introduced. The present study aimed to evaluate the prognostic value of IPI and the other IPIs in patients with PBL in a Chinese population. Methods We performed a multicenter retrospective study of 71 patients with PBL from 3 institutions in China. The Kaplan–Meier method and log-rank tests were used for the survival analysis. Cox regression analysis was performed to evaluate the prognostic factors. Subgroup analysis was performed to assess the prognostic significance of IPI scores, R-IPI scores, and NCCN-IPI scores. Results The median follow-up was 4.7 years (0.7–21.8 years). The 5-year progression-free survival (PFS) and overall survival (OS) rates were 90.2% and 96.3%. In the multivariate analysis, only IPI scores and radiotherapy were significantly associated with OS and PFS (P 0.05). Conclusions Our study confirms the prognostic value of IPI in patients with PBL, but the predictive value of IPI proved to be relatively low with the addition of the rituximab. The R-IPI and NCCN-IPI can accurately assess the high and low-risk groups of PBL patients but were insufficient to evaluate the intermediate risk group.

AGR2 is a secreted protein widely existing in breast. In precancerous lesions, primary tumors and metastatic tumors, the expression of AGR2 is increased, which has aroused our interest. This review introduces the gene and protein structure of AGR2. Its endoplasmic reticulum retention sequence, protein disulfide isomerase active site and multiple protein binding sequences endow AGR2 with diverse functions inside and outside breast cancer cells. This review also enumerates the role of AGR2 in the progress and prognosis of breast cancer, and emphasizes that AGR2 can be a promising biomarker and a target for immunotherapy of breast cancer, providing new ideas for early diagnosis and treatment of breast cancer.

Objective: To develop a nomogram for predicting the possibility of four or more positive nodes in breast cancer patients with 1–3 positive sentinel lymph nodes (SLN). Materials and methods: Retrospective analysis of data of patients from two institutions was conducted. The inclusion criteria were: invasive breast cancer; clinically node negative; received lumpectomy or mastectomy plus SLN biopsy followed by axillary lymph node dissection (ALND); and pathologically confirmed T1-2 tumor, with 1–3 positive SLNs. Patients from one institution formed the training group and patients from the other the validation group. Univariate and multivariate analyses were performed to identify the predictors of four or more positive nodes. These predictors were used to build the nomogram. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model. Results: Of the 1480 patients (966 patients in the training group, 514 in the validation group), 306 (20.7%) had four or more positive nodes. Multivariate stepwise logistic regression showed number of positive (p

Autoimmune diseases and malignant tumors are the two hotspots and difficulties that are currently being studied and concerned by the medical field. The use of PD-1/PD-L1 inhibitors improves the prognosis of advanced tumors, but excessive immune responses can also induce immune-related adverse events (irAEs). Due to this concern, many clinical trials exclude cancer patients with preexisting autoimmune disease (AID). This review outlines the possible mechanisms of irAE, discusses the safety and efficacy of PD-1/PD-L1 inhibitors in cancer patients with preexisting AID, and emphasizes the importance of early recognition, continuous monitoring, and multidisciplinary cooperation in the prevention and management of cancer patients with preexisting AID.

Abstract Background Stage at diagnosis and molecular subtype are important clinical factors associated with breast cancer patient survival. However, subgroup survival data from a large study sample are limited in China. To estimate the survival differences among patients with different stages and various subtypes of breast cancer, we conducted a hospital-based multi-center study on breast cancer in Beijing, China. Methods All resident patients diagnosed with primary, invasive breast cancer between January 1, 2006 and December 31, 2010 from four selected hospitals in Beijing were included and followed up until December 31, 2015. Hospital-based data of stage at diagnosis, hormone receptor status, and selected clinical characteristics, including body mass index (BMI), menopausal status, histological grade, and histological type, were collected from the medical records of the study subjects. Overall survival (OS) and cancer-specific survival (CSS) were estimated. Cox proportional hazards models were employed to evaluate the associations of stage at diagnosis and molecular subtype with patient survival. Results The 5-year OS and CSS rates for all patients were 89.4% and 90.3%. Survival varied by stage and molecular subtype. The 5-year OS rates for patients with stage I, II, III, and IV diseases were 96.5%, 91.6%, 74.8%, and 40.7%, respectively, and the corresponding estimates of 5-year CSS rates were 97.1%, 92.6%, 75.6%, and 42.7%, respectively. The 5-year OS rates for patients with luminal A, luminal B, HER2, and triple-negative subtypes of breast cancer were 92.6%, 88.4%, 83.6%, and 82.9%, respectively, and the corresponding estimates of 5-year CSS rates were 93.2%, 89.1%, 85.4%, and 83.5%, respectively. Multivariate analysis showed that stage at diagnosis and molecular subtype were important prognostic factors for breast cancer. Conclusions Survival of breast cancer patients varied significantly by stage and molecular subtype. Cancer screening is encouraged for the early detection and early diagnosis of breast cancer. More advanced therapies and health care policies are needed on HER2 and triple-negative subtypes.