Triple-negative breast cancer (TNBC) is characterized by a high rate of systemic metastasis, insensitivity to conventional treatment and susceptibility to drug resistance, resulting in a poor patient prognosis. The immune checkpoint inhibitors (ICIs) represented by antibodies of programmed death receptor 1 (PD-1) and programmed death receptor ligand 1 (PD-L1) have provided new therapeutic options for TNBC. However, the efficacy of PD-1/PD-L1 blockade monotherapy is suboptimal immune response, which may be caused by reduced antigen presentation, immunosuppressive tumor microenvironment, interplay with other immune checkpoints and aberrant activation of oncological signaling in tumor cells. Therefore, to improve the sensitivity of TNBC to ICIs, suitable patients are selected based on reliable predictive markers and treated with a combination of ICIs with other therapies such as chemotherapy, radiotherapy, targeted therapy, oncologic virus and neoantigen-based therapies. This review discusses the current mechanisms underlying the resistance of TNBC to PD-1/PD-L1 inhibitors, the potential biomarkers for predicting the efficacy of anti-PD-1/PD-L1 immunotherapy and recent advances in the combination therapies to increase response rates, the depth of remission and the durability of the benefit of TNBC to ICIs.
Background. Oxidative stress (OS) reactions are closely related to the development and progression of bladder cancer (BCa). This project aimed to identify new potential biomarkers to predict the prognosis of BCa and improve immunotherapy. Methods. We downloaded transcriptomic information and clinical data on BCa from The Cancer Genome Atlas (TCGA). Screening for OS genes was statistically different between tumor and adjacent normal tissue. A coexpression analysis between lncRNAs and differentially expressed OS genes was performed to identify OS-related lncRNAs. Then, differentially expressed oxidative stress lncRNAs (DEOSlncRNAs) between tumors and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select the lncRNAs for risk assessment. LASSO analysis was conducted to establish a prognostic model. The prognostic risk model could accurately predict BCa patient prognosis and reveal a close correlation with clinicopathological features. We analyzed the principal component analysis (PCA), immune microenvironment, and half-maximal inhibitory concentration (IC50) in the risk groups. Results. We constructed a model containing eight DEOSlncRNAs (AC021321.1, AC068196.1, AC008750.1, SETBP1-DT, AL590617.2, THUMPD3-AS1, AC112721.1, and NR4A1AS). The prognostic risk model showed better results in predicting the prognosis of BCa patients and was strongly correlated with clinicopathological characteristics. We found great agreement between the calibration plots and prognostic predictions in this model. The areas under the receiver operating characteristic (ROC) curve (AUCs) at 1, 3, and 5 years were 0.792, 0.804, and 0.843, respectively. This model also showed good predictive ability regarding the tumor microenvironment and tumor mutation burden. In addition, the high-risk group was more sensitive to eight therapeutic agents, and the low-risk group was more responsive to five therapeutic agents. Sixteen immune checkpoints were significantly different between the two risk groups. Conclusion. Our eight DEOSlncRNA risk models provide new insights into predicting prognosis and clinical progression in BCa patients.
Based on the importance of basement membrane (BM) in cancer invasion and metastasis, we constructed a BM-associated lncRNA risk model to group bladder cancer (BCa) patients. Transcriptional and clinical data of BCa patients were downloaded from The Cancer Genome Atlas (TCGA), and the expressed genes of BM-related proteins were obtained from the BM-BASE database. We download the GSE133624 chip data from the GEO database as an external validation dataset. We screened for statistically different BM genes between tumors and adjacent normal tissues. Co-expression analysis of lncRNAs and differentially expressed BM genes was performed to identify BM-related lncRNAs. Then, differentially expressed BM-related lncRNAs (DEBMlncRNAs) between tumor and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select lncRNAs for risk assessment. LASSO analysis was performed to build a prognostic model. We constructed a model containing 8 DEBMlncRNAs (AC004034.1, AL662797.1, NR2F1-AS1, SETBP1-DT, AC011503.2, AC093010.2, LINC00649 and LINC02321). The prognostic risk model accurately predicted the prognosis of BCa patients and revealed that tumor aggressiveness and distant metastasis were associated with higher risk scores. In this model, we constructed a nomogram to assist clinical decision-making based on clinicopathological characteristics such as age, T, and N. The model also showed good predictive power for the tumor microenvironment and mutational burden. We validated the expression of eight lncRNAs using the dataset GSE133624 and two human bladder cancer cell lines (5637, BIU-87) and examined the expression and cellular localization of LINC00649 and AC011503.2 using a human bladder cancer tissue chip. We found that knockdown of LINC00649 expression in 5637 cells promoted the proliferation of 5637 cells.Our eight DEBMlncRNA risk models provide new insights into predicting prognosis, tumor invasion, and metastasis in BCa patients.
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