Natural killer cell-related prognostic risk model predicts prognosis and treatment outcomes in triple-negative breast cancer.

Background: Natural killer (NK) cells are crucial to the emergence, identification, and prognosis of cancers. The roles of NK cell-related genes in the tumor immune microenvironment (TIME) and immunotherapy treatment are unclear. Triple-negative breast cancer (TNBC) is a highly aggressive malignant tumor. Hence, this study was conducted to develop a reliable risk model related to NK cells and provide a novel system for predicting the prognosis of TNBC. Methods: NK cell-related genes were collected from previous studies. Based on TCGA and GEO database, univariate and LASSO cox regression analysis were used to establish the NK cell-related gene signature. The patients with TNBC were separated to high-risk and low-risk groups. After that, survival analysis was conducted and the responses to immunotherapies were evaluated on the basis of the signature. Moreover, the drug sensitivity of some traditional chemotherapeutic drugs was assessed by using the "oncoPredict" R package. In addition, the expression levels of the genes involved in the signature were validated by using qRT-PCR in TNBC cell lines. Results: The patients with TNBC were divided into high- and low-risk groups according to the median risk score of the 5-NK cell-related gene signature. The low-risk group was associated with a better clinical outcome. Besides, the differentially expressed genes between the different risk groups were enriched in the biological activities associated with immunity. The tumor immune cells were found to be highly infiltrated in the low-risk groups. In accordance with the TIDE score and immune checkpoint-related gene expression analysis, TNBC patients in the low-risk groups were suggested to have better responses to immunotherapies. Eventually, some classical anti-tumor drugs were shown to be less effective in high-risk groups than in low-risk groups. Conclusion: The 5-NK cell-related gene signature exhibit outstanding predictive performance and provide fresh viewpoints for evaluating the success of immunotherapy. It will provide new insights to achieve precision and integrated treatment for TNBC in the future.

An exosome-related long non-coding RNAs risk model could predict survival outcomes in patients with breast cancer.

Breast cancer (BC) is one of the most frequent malignancies among women worldwide. Accumulating evidence indicates that long non-coding RNA (lncRNA) may affect BC progression. Exosomes, a class of small membrane vesicles, have been reported to promote tumor progression through transporting proteins, mRNAs, lncRNAs and some other small molecules. However, the interaction between exosome-related lncRNAs and the microenvironment of malignancies is unclear. Hence, we proceeded to investigate the relationship between exosome-related lncRNAs and BC microenvironment. 121 exosome-associated genes were extracted from ExoBCD database. Then, the Pearson analysis was used to screened out the exosome-related lncRNAs. After that, 15 exosome-related differentially expressed lncRNAs were identified by the correlation with BC prognosis. According to the sum of the expression of these 15 lncRNAs, extracted from The Cancer Genome Atlas, and the regression coefficients, an exosome-related lncRNAs signature was developed by using Cox regression analysis. With the median risk score of the training set, the patients in training and validation sets were separated to low-risk group and high-risk group. Subsequently, the lncRNA-mRNA co-expression network was constructed. The distinct enrichment pathways were compared among the different risk groups by using the R package clusterProfiler. The ESTIMATE method and ssGESA database were adopted to study the ESTIMATE Score and immune cell infiltration. Eventually, the expression of immune checkpoint associated genes, microsatellite instable and the immunophenoscore were further analyzed between different risk groups. Different risk groups exhibited different prognosis, with lower survival rate in the high-risk group. The differentially expressed genes between the different risk groups were enriched in biological processes pathways as well as immune responses. BC patients in high-risk group were identified with lower scores of ESTIMATE scores. Subsequently, we noticed that the infiltrating levels of aDCs, B cells, CD8+ T cells, iDCs, DCs, Neutrophils, macrophages, NK cells, pDCs, Tfh, T helper cells, TIL and Tregs were obvious elevated with the decreased risk score in training and validation cohorts. And some immune signatures were significantly activated with the decreased risk score in both cohorts. Eventually, the exosome-associated lncRNAs risk model was demonstrated to accurately predict immunotherapy response in patients with BC. The results of our study suggest that exosome-related lncRNAs risk model has close relationship with prognosis and immune cells infiltration in BC patients. These findings could make a great contribution to improving BC immunotherapy.

Three-microRNA expression signature predicts survival in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a specific type of breast cancer with poor overall survival (OS) time. Previous studies revealed that microRNAs (miRNAs/miRs) serve important roles in the pathogenesis, progression and prognosis of TNBC. The present study analyzed the miRNA expression and clinical data of patients with TNBC downloaded from The Cancer Genome Atlas. A total of 194 differentially expressed miRNAs were identified between TNBC and matched normal tissues using the cut-off criteria of P<0.05 and |log2 fold change|>2. Of these miRNAs, 65 were downregulated and 129 were upregulated. Using Kaplan-Meier survival analysis, a total of 77 miRNAs that were closely associated with OS time were identified (P<0.05). The intersection of the 77 miRNAs and 194 differentially expressed miRNAs revealed six miRNAs. Log-rank tests based on survival curves were performed and two miRNAs were eliminated. The prognostic value of the remaining four miRNAs was evaluated with a Cox proportional hazards model using multiple logistic regression with forward stepwise selection of variables. Three miRNAs (miR-21-3p, miR-659-5p and miR-200b-5p) were subsequently identified as independent risk factors associated with OS time in the model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that the target genes of these three miRNAs were mainly involved in 'cell protein metabolism', 'RNA transcriptional regulation', 'cell migration', 'MAPK signaling pathway', 'ErbB signaling pathway', 'prolactin signaling pathway' and 'adherens junctions'. Taken together, the results obtained in the present study suggested that the three-miRNA signature may serve as a prognostic biomarker for patients with TNBC.

The clinicopathologic differences of central lymph node metastasis in predicting lateral lymph node metastasis and prognosis in papillary thyroid cancer associated with or without Hashimoto's thyroiditis.

This study aims to evaluate the difference of central lymph node metastases (LNM) in papillary thyroid carcinoma (PTC) associated with or without Hashimoto's thyroiditis (HT) in predicting lateral node metastasis. A retrospective case control study was performed. Patients (1276) with PTC who underwent a total or near-total thyroidectomy with at least one lymph node dissection in our institution were retrospectively reviewed. All patients were divided into two groups (HT-group and non-HT group) according to the pathological diagnosis. In HT-group, the incidence of both central and lateral LNM was lower compared with non-HT group. The average of central metastatic lymph node radio (LNR) was also lower than that in Non-HT group. The multivariate analysis showed that the number of metastatic central LNs (HT ≥ 4, Non-HT ≥ 2) and the central LNR (HT ≥ 0.4, Non-HT ≥ 0.6) were independently associated with lateral LNM. Patients with HT need larger primary tumor size, more positive central LN and higher LNR to predict the presence of lateral LNM. HT may protect against central and lateral LNM in PTC. The number of positive central LNs and central LNR in PTC could be used to determine the presence of lateral LNM and inform postoperative follow-up.