Skin cutaneous melanoma (SKCM) is a skin cancer type characterized by a high degree of immune cell infiltration. The potential function of lactate, a main metabolic product in the tumor microenvironment (TME) of SKCM, remains unclear. In this study, we systemically analyzed the predictive value of lactate-related genes (LRGs) for prognosis and response to immune checkpoint inhibitors (ICIs) in SKCM patients included from The Cancer Genome Atlas (TCGA) database. Cluster 3, by consensus clustering for 61 LRGs, manifested a worse clinical outcome, attributed to the overexpression of malignancy marks. In addition, we created a prognostic prediction model for high- and low-risk patients and verified its performance in a validation cohort, GSE65904. Between TME and the risk model, we found a negative relation of the immunocyte infiltration levels with patients’ risk scores. The low-risk cases had higher ICI expression and could benefit better from ICIs relative to the high-risk cases. Thus, the lactate-related prognosis risk signature may comprehensively provide a basis for future investigations on immunotherapeutic treatment for SKCM.

Background Differentiation antagonizing non-protein-coding RNA (DANCR) is a novel long noncoding RNA. Recent studies have shown that DANCR is aberrantly expressed in several types of cancer and is associated with poor outcomes. However, the clinical diagnostic significance of DANCR in tumors is not completely understood. Methods We searched the PubMed, Medline, Web of Science, EMBASE, Cochrane Library, and Ovid databases (up to December 30, 2018) for relevant literature. A total of 11 studies with 945 cancer patients were included in the present meta-analysis. We further validated the results using The Cancer Genome Atlas (TCGA) dataset. Results High expression of DANCR significantly predicted poor overall survival (low expression group vs high expression group; HR =0.56, 95% CI=[0.43, 0.72], =0.000); this was validated using TCGA. Moreover, DANCR expression was associated with advanced tumor node metastasis stage (I+II:III+IV; OR=0.22, 95% CI=[0.14, 0.35], P =0.001) and lymph node metastasis (no:yes; OR=0.21, 95% CI=[0.13, 0.35], P =0.001). Conclusion Our results suggest that elevated DANCR is related to poor clinical outcomes and could serve as a potential prognostic biomarker of cancer.

Purpose. This study aimed to investigatie the feasibility of pretherapeutic CT radiomics-based nomograms to predict the overall survival (OS) of patients with nondistant metastatic Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) undergoing stereotactic body radiotherapy (SBRT). Methods. A retrospective review of 137 patients with nondistant metastatic BCLC-C HCC who underwent SBRT was made. Radiomics features distilled from pretherapeutic CT images were selected by the method of LASSO regression for radiomics signature construction. Then, the clinical model was constructed based on clinical characteristics. A radiomics nomogram was constructed using the radiomics score (Rad-score) and clinical characteristics to predict post-SBRT OS in BCLC-C HCC patients. An analysis of discriminatory ability and calibration was performed to confirm the efficacy of the radiomics nomogram. Results. In order to construct the radiomic signature, seven significant features were selected. Patients were divided into low-risk (Rad-score < −0.03) and high-risk (Rad-score ≥ −0.03) groups based on the best Rad-score cutoff value. There were statistically significant differences in OS both in the training set ( p < 0.0001 ) and the validation set ( p = 0.03 ) after stratification. The C-indexes of the radiomics nomogram were 0.77 (95% CI: 0.72–0.82) in the training set and 0.71 (95% CI: 0.61–0.81) in the validation set, which outperformed the clinical model and radiomics signature. An AUC of 0.76, 0.79, and 0.84 was reached for 6-, 12-, and 18-month survival predictions, respectively. Conclusions. The predictive nomogram that combines radiomic features with clinical characteristics has great prospects for application in the prediction of post-SBRT OS in nondistant metastatic BCLC-C HCC patients.

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