Development of a nomogram for prediction of central lymph node metastasis of papillary thyroid microcarcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most frequent malignant tumor in thyroid carcinoma. The aim of this study was to explore the risk factors associated with central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) and establish a nomogram model that can assess the probability of central lymph node metastasis (CLNM). METHODS: The clinicopathological data of 377 patients with cN0 PTMC were collected and analyzed from The Second Affiliated Hospital of Fujian Medical University from July 1st, 2019 to December 30th, 2021. All patients were examined by underwent ultrasound (US), found without metastasis to central lymph nodes, and diagnosed with PTMC through pathologic examination. All patients received thyroid lobectomy or total thyroidectomy with therapeutic or prophylactic central lymph node dissection (CLND). R software (Version 4.1.0) was employed to conduct a series of statistical analyses and establish the nomogram. RESULTS: A total of 119 patients with PTMC had central lymph node metastases (31.56%). After that, age (P < 0.05), gender (P < 0.05), tumor size (P < 0.05), tumor multifocality (P < 0.05), and ultrasound imaging-suggested tumor boundaries (P < 0.05) were identified as the risk factors associated with CLNM. Subsequently, multivariate logistic regression analysis indicated that the area under the receiver operating characteristic (ROC) curve (AUC) of the training cohort was 0.703 and that of the validation cohort was 0.656, demonstrating that the prediction ability of this model is relatively good compared to existing models. The calibration curves indicated a good fit for the nomogram model. Finally, the decision curve analysis (DCA) showed that a probability threshold of 0.15-0.50 could benefit patients clinically. The probability threshold used in DCA captures the relative value the patient places on receiving treatment for the disease, if present, compared to the value of avoiding treatment if the disease is not present. CONCLUSION: CLNM is associated with many risk factors, including age, gender, tumor size, tumor multifocality, and ultrasound imaging-suggested tumor boundaries. The nomogram established in our study has moderate predictive ability for CLNM and can be applied to the clinical management of patients with PTMC. Our findings will provide a better preoperative assessment and treatment strategies for patients with PTMC whether to undergo central lymph node dissection.

Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer.

Cancer stem cells (CSCs) have the potential to self-renew and induce cancer, which may contribute to a poor prognosis by enabling metastasis, recurrence, and therapy resistance. Hence, this study was performed to identify the association between CSC-related genes and triple-negative breast cancer (TNBC) development. Stemness gene sets were downloaded from StemChecker. Based on the online databases, a consensus clustering algorithm was conducted for unsupervised classification of TNBC samples. The variations between subtypes were assessed with regard to prognosis, tumor immune microenvironment (TIME), and chemotherapeutic sensitivity. The stemness-related gene signature was established and random survival forest analysis was employed to identify the core gene for validation experiments and tumor sphere formation assays. 499 patients with TNBC were classified into three subgroups and the Cluster 1 had a better OS than others. After that, WGCNA study was performed to identify genes important for Cluster 1 subtype. Out of all 8 modules, the subtype of Cluster 1 and the yellow module with 103 genes demonstrated the largest positive association. After that, a four-gene stemness-related signature was established. Based on the yellow module, the 39 potential pivotal genes were subjected to the random forest survival analysis to find out the gene that was relatively important for OS. KIF15 was confirmed as the targeted gene by LASSO and random survival forest analyses. In vitro experiments, the downregulation of KIF15 promoted the stemness of TNBC cells. The expression levels of stem cell markers Nanog, SOX2, and OCT4 were found to be elevated in TNBC cell lines after KIF15 inhibition. A stemness-associated risk model was constructed to forecast the clinical outcomes of TNBC patients. The downregulation of KIF15 expression in a subpopulation of TNBC stem cells may promote stemness and possibly TNBC progression.

Identification of an exosome-related signature associated with prognosis and immune infiltration in breast cancer.

Exosomes, nanosized vesicles, play a vital role in breast cancer (BC) occurrence, development, and drug resistance. Hence, we proceeded to study the potential prognostic value of exosome-related genes and their relationship to the immune microenvironment in BC. 121 exosome-related genes were provided by the ExoBCD database, and 7 final genes were selected to construct the prognostic signature. Besides, the expression levels of the 7 exosome-related genes were validated by the experiment in BC cell lines. Based on the signature, BC patients from the training and validation cohorts were separated into low- and high-risk groups. Subsequently, the R clusterProfiler package was applied to identify the distinct enrichment pathways between high-risk groups and low-risk groups. The relevance of the tumor immune microenvironment and exosome-related gene risk score were analyzed in BC. Eventually, the different expression levels of immune checkpoint-related genes were compared between the two risk groups. Based on the risk model, the low-risk groups were identified with a higher survival rate both in the training and validation cohorts. A better overall survival was revealed in patients with higher scores evaluated by the estimation of stromal and immune cells in malignant tumor tissues using expression (ESTIMATE) algorithm. Subsequently, BC patients with lower risk scores were indicated by higher expression levels of some immune checkpoint-related genes and immune cell infiltration. Exosomes are closely associated with the prognosis and immune cell infiltration of BC. These findings may contribute to improving immunotherapy and provide a new vision for BC treatment strategies.

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.

Construction and validation of a transient receptor potential-related long noncoding RNA signature for prognosis prediction in breast cancer patients.

Breast cancer (BC) is the most commonly diagnosed malignancy in women around the world. Accumulating evidence suggests that transient receptor potential (TRP) channels play a significant role in tumor progression and immune cell infiltration. Hence, we conducted the study to investigate the correlation between TRP-associated lncRNAs and the prognosis of breast carcinoma. In the current study, 33 TRP-associated genes were selected from a review published by Amrita Samanta et al, and the TRP-related lncRNAs were identified by Pearson analysis. Based on the sum of the expression levels of 12 lncRNAs provided by the Cancer Genome Atlas (TCGA), a TRP-associated lncRNA signature was established by using Cox regression analysis. According to the median value of the risk score in the training set, BC patients were separated into high- and low-risk groups. Subsequently, functional enrichment analysis was conducted on the differential expression genes (DEGs) between different risk groups. The Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression (ESTIMATE) Score was calculated by ESTIMATE, and the immune cell infiltration was evaluated by ssGSEA. Finally, the immune checkpoint gene expression levels, microsatellite instability (MSI), and immunophenoscore (IPS) were further assessed. The high-risk groups exhibited lower survival rates, while the low-risk groups showed higher survival rates. As a result, the DEGs between different risk groups were highly enriched in immune cell activation and immunoregulation. Besides, the ESTIMATE scores of patients in low-risk groups were higher than those in high-risk groups. The infiltration levels of several immune cells were remarkably elevated in low-risk groups, and various immune signatures were activated with a decreased risk score. Eventually, the TRP-associated lncRNA signature was confirmed with a highly potential ability to evaluate the immunotherapy response in breast carcinoma patients. The outcomes of the current study indicated that the 12-TRP-associated-lncRNA risk model was an independent prognostic risk factor for BC patients. This risk model could be closely related to the tumor immune microenvironment in BC. Our findings will provide new insights for future immunotherapy for BC treatment.

Comprehensive analysis of cuproptosis-related long non-coding RNA signature and personalized therapeutic strategy of breast cancer patients.

Background: Breast cancer (BC) is considered to be one of the primary causes of cancer deaths in women. Cuproptosis was suggested to play an important role in tumor proliferation and tumor immune microenvironment. Therefore, an investigation was conducted to identify the relationship between cuproptosis-related long non-coding RNAs (lncRNAs) and BC prognosis. Method: Based on The Cancer Genome Atlas (TCGA), nine cuproptosis-related lncRNAs were identified by Pearson's analysis and Cox regression analysis to create a cuproptosis-related lncRNA signature. Subsequently, patients with BC were divided into high-risk and low-risk groups. The Kaplan-Meier curves and a time-dependent receiver operating characteristic (ROC) analysis were employed to elucidate the predictive capability of the signature. After that, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted by Gene Set Enrichment Analysis (GSEA), and the lncRNA-mRNA co-expression network was established by Cytoscape software. Furthermore, the ESTIMATE score was calculated, and the immune cell type component analysis was conducted. Eventually, immunotherapy response analysis was applied to identify the predictive power of cuproptosis-related lncRNAs to tumor immunotherapy response, including immune checkpoint gene expression levels, tumor mutational burden (TMB), and microsatellite instability (MSI). Results: Patients with BC in the low-risk groups showed better clinical outcomes. The KEGG pathways in the high-risk groups were mainly enriched in immune response and immune cell activation. Furthermore, the ESTIMATE scores were higher in the low-risk groups, and their immune cell infiltrations were dramatically different from those of the high-risk groups. The low-risk groups were shown to have higher infiltration levels of CD8+ T cells and TMB-high status, resulting in better response to immunotherapies. Conclusion: The findings of this study revealed that the nine-cuproptosis-related lncRNA risk score was an independent prognostic factor for BC. This signature was a potential predictor for BC immunotherapy response. What we found will provide novel insight into immunotherapeutic treatment strategies in BC.

A pyroptosis-associated gene risk model for predicting the prognosis of triple-negative breast cancer.

Background: Pyroptosis is a novel identified form of inflammatory cell death that is important in the development and progression of various diseases, including malignancies. However, the relationship between pyroptosis and triple-negative breast cancer (TNBC) is still unclear. Therefore, we started to investigate the potential prognostic value of pyroptosis-associated genes in TNBC. Methods: Thirty-three genes associated with pyroptosis were extracted from previous publications, 30 of which were identified in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. On the basis of the 30 pyroptosis-related genes, patients with TNBC were divided into three subtypes through unsupervised cluster analysis. The prognostic value of each pyroptosis-associated gene was assessed, and six genes were selected by univariate and LASSO Cox regression analysis to establish a multigene signature. According to the median value of risk score, patients with TNBC in the training and validation cohorts were separated to high- and low-risk sets. The enrichment analysis was conducted on the differentially expressed genes (DEGs) of the two risk sets using R clusterProfiler package. Moreover, the ESTIMATE score and immune cell infiltration were calculated by the ESTIMATE and CIBERSORT methods. After that, the correlation among pyroptosis-associated risk score and the expression of immune checkpoint-associated genes as well as anti-cancer drugs sensitivities were further analyzed. Results: In the training and validation cohorts, patients with TNBC in the high-risk set were found in a lower survival rate than those in the low-risk set. Combined with the clinical characteristics, the pyroptosis-related risk score was identified as an independent risk factor for the prognosis of patients with TNBC. The enrichment analysis indicated that the DEGs between the two risk groups were mainly enriched by immune responses and activities. In addition, patients with TNBC in the low-risk set were found to have a higher value of ESTIMATE score and a higher rate of immune cell infiltration. Finally, the expression levels of five genes [programmed cell death protein 1 (PD-1); cytotoxic t-lymphocyte antigen-4 (CTLA4); lymphocyte activation gene 3 (LAG3); T cell immunoreceptor with Ig and ITIM domains (TIGIT)] associated with immune checkpoint inhibitors were identified to be higher in the low-risk sets. The sensitivities of some anti-cancer drugs commonly used in breast cancer were found closely related to the pyroptosis-associated risk model. Conclusion: The pyproptosis-associated risk model plays a vital role in the tumor immunity of TNBC and can be applied to be a prognostic predictor of patients with TNBC. Our discovery will provide novel insight for TNBC immunotherapies.

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.