ObjectiveTo analyze the clinicopathologic features and prognosis of breast cancer patients with low human epidermal growth factor receptor-2 (HER2) expression. MethodsThe breast cancer patients underwent initially surgical resection in the First Hospital of Shanxi Medical University from October 2015 to October 2017 and met the criterion of this study were retrospectively gathered. Based on the immunohistochemical / in situ hybridization detection results, the patients were divided into three subtypes of HER2 zero, low, and positive expressions, and the differences in the clinicopathologic characteristics, overall survival (OS) and disease-free survival (DFS) of the three subtypes of breast cancer patients were compared. At the same time, the risk factors affecting the OS and DFS of breast cancer patients with low HER2 expression were analyzed. ResultsA total of 315 eligible patients were gathered in this study, including 68 patients with HER2 zero expression, 121 patients with low HER2 expression, and 126 patients with positive HER2 expression. There were no statistic differences in the menstrual status, T stage, and histological classification between the breast cancer patients with low HER2 and positive HER2 expressions (P>0.05), but the proportions of the patients with lymph node metastasis, histological grade Ⅲ, negative hormone receptor (HR) and high Ki67 expression in the low HER2 expression patients were lower than those in the positive HER2 expression patients. And compared with HER2 zero expression breast cancer patients, the proportions of premenopausal / perimenopausal, T2–4, N1–3, histological grade Ⅱ, ductal carcinoma, negative HR, and low Ki67 expression patients in the breast cancer patients with low HER2 expression were higher (P<0.05). While the survival curves of OS and DFS by Kaplan-Meier method had no statistic differences among the three subtypes of the breast cancer patients (χ2=0.070, P=0.966; χ2=0.362, P=0.835). The multivariate analysis results by Cox proportional hazards regression found that the low HER2 expression breast cancer patients with histological grade Ⅲ and negative HR had the higher risks of OS and DFS shortening (P<0.05). In addition, the risk of DFS shortening in the patients with T stage 2–4 and N stage 1–3 was increased (P<0.05). ConclusionsFrom the results of this study, breast cancer patients with low HER2 expression is different from the other two subtypes of breast cancer in terms of clinicopathologic characteristics. However, there are no statistical significances in comparing the OS and DFS of three types of breast cancer patients, but it is found that histological grading and HR are related to the OS and DFS of breast cancer patients with low HER2 expression, and it is also found that T stage and N stage are related to the DFS of breast cancer patients with low HER2 expression, so more attentions should be paid to the treatment plans.
ObjectiveTo screen long non-coding RNAs (lncRNAs) relevant to programmed cell death (PCD) and construct a nomogram model predicting prognosis of hepatocellular carcinoma (HCC). MethodsThe HCC patients selected from The Cancer Genome Atlas (TCGA) were randomly divided into training set and validation set according to 1∶1 sampling. The lncRNAs relevant to PCD were screened by Pearson correlation analysis, and which associated with overall survival in the training set were screened by univariate Cox proportional hazards regression (abbreviation as “Cox regression”), and then multivariate Cox regression was further used to analyze the prognostic risk factors of HCC patients, and the risk score function model was constructed. According to the median risk score of HCC patients in the training set, the HCC patients in each set were assigned into a high-risk and low-risk, and then the Kaplan-Meier method was used to draw the overall survival curve, and the log-rank test was used to compare the survival between the HCC patients with high-risk and low-risk. At the same time, the area under receiver operating characteristic curve (AUC) was used to evaluate the value of the risk score function model in predicting the 1-, 3-, and 5-year overall survival rates of HCC patients in the training set, validation set, and integral set. Then the nomogram was constructed based on the risk score function model and factors validated in clinic, and its predictive ability for the prognosis of HCC patients was evaluated. ResultsA total of 374 patients with HCC were downloaded from the TCGA, of which 342 had complete clinicopathologic data, including 171 in the training set and 171 in the validation set. Finally, 8 lncRNAs genes relevant to prognosis (AC099850.3, LINC00942, AC040970.1, AC022613.1, AC009403.1, AL355974.2, AC015908.3, AC009283.1) were screened out, and the prognostic risk score function model was established as follows: prognostic risk score=exp1×β1+exp2×β2...+expi×βi (expi was the expression level of target lncRNA, βi was the coefficient of multivariate Cox regression analysis of target lncRNA). According to this prognostic risk score function model, the median risk score was 0.89 in the training set. The patients with low-risk and high-risk were 86 and 85, 86 and 85, 172 and 170 in the training set, validation set, and integral set, respectively. The overall survival curves of HCC patients with low-risk drawn by Kaplan-Meier method were better than those of the HCC patients with high-risk in the training set, validation set, and integral set (P<0.001). The AUCs of the prognostic risk score function model for predicting the 1-, 3-, and 5-year overall survival rates in the training set were 0.814, 0.768, and 0.811, respectively, in the validation set were 0.799, 0.684, and 0.748, respectively, and in the integral set were 0.807, 0.732, and 0.784, respectively. The multivariate Cox regression analysis showed that the prognostic risk score function model was a risk factor affecting the overall survival of patients with HCC [<0.89 points as a reference, RR=1.217, 95%CI (1.151, 1.286), P<0.001]. The AUC (95%CI) of the prognostic risk score function model for predicting the overall survival rate of HCC patients was 0.822 (0.796, 0.873). The AUCs of the nomogram constructed by the prognostic risk score function model in combination with clinicopathologic factors to predict the 1-, 3-, and 5-year overall survival rates were 0.843, 0.839, and 0.834. The calibration curves of the nomogram of 1-, 3-, and 5-year overall survival rates in the training set were close to ideal curve, suggesting that the predicted overall survival rate by the nomogram was more consistent with the actual overall survival rate. ConclusionThe prognostic risk score function model constructed by the lncRNAs relevant to PCD in this study may be a potential marker of prognosis of the patients with HCC, and the nomogram constructed by this model is more effective in predicting the prognosis (overall survival) of patients with HCC.
ObjectiveTo understand the single-cell RNA sequencing (scRNA-seq) and its research progress in the tumor microenvironment (TME) of breast cancer, in order to provide new ideas and directions for the research and treatment of breast cancer. MethodThe development of scRNA-seq technology and its related research literature in breast cancer TME at home and abroad in recent years was reviewed. ResultsThe scRNA-seq was a quantum technology in high-throughput sequencing of mRNA at the cellular level, and had become a powerful tool for studying cellular heterogeneity when tissue samples were fewer. While capturing rare cell types, it was expected to accurately describe the complex structure of the TME of breast cancer. ConclusionsAfter decades of development, scRNA-seq has been widely used in tumor research. Breast cancer is a malignant tumor with high heterogeneity. The application of scRNA-seq in breast cancer research can better understand its tumor heterogeneity and TME, and then promote development of personalized diagnosis and treatment.