Corrigendum: Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma.

[This corrects the article DOI: 10.3389/fendo.2022.1056310.].

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma.

Background: Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM. Methods: In this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals. Results: 4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy. Conclusions: 4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies.

Natural killer cell-related prognosis signature characterizes immune landscape and predicts prognosis of HNSCC.

Background: Head and neck squamous cell carcinoma (HNSCC), the most common head and neck cancer, is highly aggressive and heterogeneous, resulting in variable prognoses and immunotherapeutic outcomes. Natural killer (NK) cells play essential roles in malignancies' development, diagnosis, and prognosis. The purpose of this study was to establish a reliable signature based on genes related to NK cells (NRGs), thus providing a new perspective for assessing immunotherapy response and prognosis of HNSCC patients. Methods: In this study, NRGs were used to classify HNSCC from the TCGA-HNSCC and GEO cohorts. The genes were evaluated using univariate cox regression analysis based on the differential analysis of normal and tumor samples in TCGA-HNSCC conducted using the "limma" R package. Thereafter, we built prognostic gene signatures using LASSO-COX analysis. External validation was carried out in the GSE41613 cohort. Immunity analysis based on NRGs was performed via several methods, such as CIBERSORT, and immunotherapy response was evaluated by TIP portal website. Results: With the TCGA-HNSCC data, we established a nomogram based on the 17-NRGs signature and a variety of clinicopathological characteristics. The low-risk group exhibited a better effect when it came to immunotherapy. Conclusions: 17-NRGs signature and nomograms demonstrate excellent predictive performance and offer new perspectives for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology research.

The prognostic value of MicroRNAs associated with fatty acid metabolism in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer in humans globally. In addition to smoking and drinking, genetic and epigenetic changes also play a big role in how HNSCC starts and grows. MicroRNAs are short, non-coding RNAs that control cell differentiation and apoptosis by interfering with gene expression. In addition, microRNAs in HNSCC have been shown to affect the clinical behaviors of HNSCC in amazing ways. Moreover, metabolic reprogramming is a key part of cancer and is needed for cancer to turn into a tumor and grow. But it is still not clear what effect microRNAs related to fatty acid metabolism have on the prognosis of HNSCC patients. We downloaded the data of HNSCC patients from the TCGA database and obtained the genes associated with fatty acid metabolism according to the GSEA database. Then, the microRNAs associated with fatty acid metabolism genes were matched. Finally, fatty acid metabolism gene-associated microRNAs for calculating risk scores and then building multifactorial Cox regression models in patients with HNSCC. Heatmap analysis showed that microRNAs involved in fatty acid metabolism were significantly different in HNSCC patients than in healthy controls. A total of 27 microRNAs associated with fatty acid metabolism were screened by univariate Cox analysis (p < 0.05). Using lasso regression, 18 microRNAs substantially linked with the prognosis of HNSCC patients were identified and included in risk scores. The ROC curves demonstrate that risk scores derived from microRNAs involved in fatty acid metabolism can accurately predict the prognosis of HNSCC patients at 1, 3, and 5 years. Moreover, we discovered that 11 microRNAs included in the risk score properly distinguished the prognosis of HNSCC patients. This paper indicated that microRNAs involved with fatty acid metabolism are strongly linked to the prognosis of HNSCC patients. It also indicated that reprogramming of fatty acid metabolism in tumor tissues may play an important role in HNSCC cancer.

Exosomes: A potential tool for immunotherapy of ovarian cancer.

Ovarian cancer is a malignant tumor of the female reproductive system, with a very poor prognosis and high mortality rates. Chemotherapy and radiotherapy are the most common treatments for ovarian cancer, with unsatisfactory results. Exosomes are a subpopulation of extracellular vesicles, which have a diameter of approximately 30-100 nm and are secreted by many different types of cells in various body fluids. Exosomes are highly stable and are effective carriers of immunotherapeutic drugs. Recent studies have shown that exosomes are involved in various cellular responses in the tumor microenvironment, influencing the development and therapeutic efficacy of ovarian cancer, and exhibiting dual roles in inhibiting and promoting tumor development. Exosomes also contain a variety of genes related to ovarian cancer immunotherapy that could be potential biomarkers for ovarian cancer diagnosis and prognosis. Undoubtedly, exosomes have great therapeutic potential in the field of ovarian cancer immunotherapy. However, translation of this idea to the clinic has not occurred. Therefore, it is important to understand how exosomes could be used in ovarian cancer immunotherapy to regulate tumor progression. In this review, we summarize the biomarkers of exosomes in different body fluids related to immunotherapy in ovarian cancer and the potential mechanisms by which exosomes influence immunotherapeutic response. We also discuss the prospects for clinical application of exosome-based immunotherapy in ovarian cancer.