Long noncoding RNA (lncRNA) plays a critical role in the development of tumors. The aim of our study was construction of a lncRNA signature model to predict breast cancer (BRCA) patient survival. We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. Corresponding protein-coding genes were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. Finally, 521 differentially expression lncRNA were obtained. We constructed a ten-lncRNA signature model (LINC01208, RP5-1011O1.3, LINC01234, LINC00989, RP11-696F12.1, RP11-909N17.2, CTC-297N7.9, CTA-384D8.34, CTC-276P9.4, and MAPT-IT1) to predict BRCA patient survival using the multivariate Cox proportional hazard regression model. The C-index was 0.712, and AUC scores of training, test, and entire sets were 0.746, 0.717, and 0.732, respectively. Univariate Cox regression analysis indicated that age, tumor status, N status, M status, and risk score were significantly related to overall survival in patients with BRCA. Further, the multivariate analysis showed that risk score and M status had outstanding independent prognostic values, both with p<0.001. The Gene Ontology (GO) function and KEEG pathway analysis was primarily enriched in immune response, receptor binding, external surface of plasma membrane, signal transduction, cytokine-cytokine receptor interaction, and cell adhesion molecules (CAMs). Finally, we constructed a ten-lncRNA signature model that can serve as an independent prognostic model to predict BRCA patient survival.

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Background: Immune checkpoint inhibitors are a promising therapeutic strategy for breast cancer (BRCA) patients. The tumor microenvironment (TME) can downregulate the immune response to cancer therapy. Our study aimed to find a TME-related biomarker to identify patients who might respond to immunotherapy. Method: We downloaded raw data from several databases including TCGA and MDACC to identify TME hub genes associated with overall survival (OS) and the progression-free interval (PFI) by WGCNA. Correlations between hub genes and either tumor-infiltrating immune cells or immune checkpoints were conducted by ssGSEA.Result: TME-related green and black modules were selected by WGCNA to further screen hub genes. Random forest, univariate and multivariate Cox regressions were applied to screen hub genes (MYO1G, TBC1D10C, SELPLG, and LRRC15) and construct a nomogram to predict survival of BRCA patients. The C-index for the nomogram was 0.713. A DCA of the predictive model revealed that the net benefit of nomogram was significantly higher than others and the calibration curve demonstrated a good performance by the nomogram. Only TBC1D10C was correlated with both OS and the PFI (both p-values<0.05). TBC1D10C also had a high positive association with tumor-infiltrating immune cells and common immune checkpoints (PD-1, CTLA-4, and TIGIT).Conclusion: We constructed a TME-related gene signature model to predict the survival probability of BRCA patients. We also identified a hub gene, TBC1D10C, which was correlated with both OS and the PFI and had a high positive association with tumor-infiltrating immune cells and common immune checkpoints. TBC1D10C may be a new biomarker to select patients who may benefit from immunotherapy.

Background. Immune checkpoint inhibitors are a promising therapeutic strategy for breast cancer (BRCA) patients. The tumor microenvironment (TME) can downregulate the immune response to cancer therapy. Our study is aimed at finding a TME-related biomarker to identify patients who might respond to immunotherapy. Method. We downloaded raw data from several databases including TCGA and MDACC to identify TME hub genes associated with overall survival (OS) and the progression-free interval (PFI) by WGCNA. Correlations between hub genes and either tumor-infiltrating immune cells or immune checkpoints were conducted by ssGSEA. Result. TME-related green and black modules were selected by WGCNA to further screen hub genes. Random forest and univariate and multivariate Cox regressions were applied to screen hub genes (MYO1G, TBC1D10C, SELPLG, and LRRC15) and construct a nomogram to predict the survival of BRCA patients. The C -index for the nomogram was 0.713. A DCA of the predictive model revealed that the net benefit of the nomogram was significantly higher than others and the calibration curve demonstrated a good performance by the nomogram. Only TBC1D10C was correlated with both OS and the PFI (both p values < 0.05). TBC1D10C also had a high positive association with tumor-infiltrating immune cells and common immune checkpoints (PD-1, CTLA-4, and TIGIT). Conclusion. We constructed a TME-related gene signature model to predict the survival probability of BRCA patients. We also identified a hub gene, TBC1D10C, which was correlated with both OS and the PFI and had a high positive association with tumor-infiltrating immune cells and common immune checkpoints. TBC1D10C may be a new biomarker to select patients who may benefit from immunotherapy.