Copper metabolism-related risk score identifies hepatocellular carcinoma subtypes and SLC27A5 as a potential regulator of cuproptosis.

AIMS: Dysregulated copper metabolism has been noticed in many types of cancer including hepatocellular carcinoma (HCC); however, a comprehensive understanding about this dysregulation still remains unclear in HCC. METHODS: A set of bioinformatic tools was integrated to analyze the expression and prognostic significance of copper metabolism-related genes. A related risk score, termed as CMscore, was developed via univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression. Pathway enrichment analyses and tumor immune cell infiltration were further investigated in CMscore stratified HCC patients. Weighted correlation network analysis (WGCNA) was used to identify potential regulator of cuproptosis. RESULTS: Copper metabolism was dysregulated in HCC. HCC patients in the high-CMscore group showed a significantly lower overall survival (OS) and enriched in most cancer-related pathways. Besides, HCC patients with high CMscore had higher expression of pro-tumor immune infiltrates and immune checkpoints. Moreover, cancer patients with high CMscore from two large cohorts exhibited significantly prolonged survival time after immunotherapy. WGCNA and subsequently correlation analysis revealed that SLC27A5 might be a potential regulator of cuproptosis in HCC. In vitro experiments revealed that SLC27A5 inhibited cell proliferation and migration of HCC cells and could upregulate FDX1, the key regulator of cuproptosis. SIGNIFICANCE: The CMscore is helpful in clustering HCC patients with distinct prognosis, gene mutation signatures, and sensitivity to immunotherapy. SLC27A5 might serve as a potential target in the induction of cuproptosis in HCC.

Abnormal low expression of SFTPC promotes the proliferation of lung adenocarcinoma by enhancing PI3K/AKT/mTOR signaling transduction.

The abnormality of surfactant protein C (SFTPC) has been linked to the development of a number of interstitial lung diseases, according to mounting evidence. Nonetheless, the function and mechanism of SFTPC in the biological progression of lung adenocarcinoma (LUAD) remain unclear. Analysis of public datasets and testing of clinical samples suggested that SFTPC expression was abnormally low in LUAD, which was associated with the onset and poor prognosis of LUAD. The SFTPC-related risk score was derived using least absolute shrinkage and selection operator Cox regression as well as multivariate Cox regression. The risk score was highly correlated with tumor purity and tumor mutation burden, and it could serve as an independent prognostic indicator for LUAD. Low-risk LUAD patients may benefit more from CTLA-4 or/and PD-1 inhibitors. Overall, the risk score is useful for LUAD patient prognostication and treatment guidance. Moreover, in vitro and in vivo experiments demonstrated that SFTPC inhibits the proliferation of LUAD by inhibiting PI3K/AKT/mTOR signaling transduction. These results reveal the molecular mechanism by which SFTPC inhibits the proliferation of LUAD and suggest that SFTPC could be a new therapeutic target for LUAD.

Identification and validation of immunogenic cell death-related score in uveal melanoma to improve prediction of prognosis and response to immunotherapy.

BACKGROUND: Immunogenic cell death (ICD) could activate innate and adaptive immune response. In this work, we aimed to develop an ICD-related signature in uveal melanoma (UVM) patients and facilitate assessment of their prognosis and immunotherapy. METHODS: A set of machine learning methods, including non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithms were used to evaluate the infiltration of immune cells. The Genomics of Drug Sensitivity in Cancer (GDSC), cellMiner and tumor immune dysfunction and exclusion (TIDE) databases were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures was also compared. RESULTS: The ICDscore could predict the prognosis of UVM patients in both the training and four validating cohorts. The ICDscore outperformed 19 previously published signatures. Patients with high ICDscore exhibited a substantial increase in immune cell infiltration and expression of immune checkpoint inhibitor-related genes, leading to a higher response rate to immunotherapy. Furthermore, the downregulation of poly (ADP-ribose) polymerase family member 8 (PARP8), a critical gene involved in the development of the ICDscore, resulted in decreased cell proliferation and slower migration of UVM cells. CONCLUSION: In conclusion, we developed a robust and powerful ICD-related signature for evaluating the prognosis and benefits of immunotherapy that could serve as a promising tool to guide decision-making and surveillance for UVM patients.

Machine learning-based construction of immunogenic cell death-related score for improving prognosis and response to immunotherapy in melanoma.

BACKGROUND: Immunogenic cell death (ICD) is a form of regulated cell death (RCD) which could drive the activation of the innate and adaptive immune responses. In this work, we aimed to develop an ICD-related signature to facilitate the assessment of prognosis and immunotherapy response for melanoma patients. METHODS: A set of machine learning methods, including consensus clustering, non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithm were used to evaluate the infiltration of immune cells. The 'pRRophetic' package in R and 6 cohorts of melanoma patients receiving immunotherapy were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures were also compared. RESULTS: The ICDscore could predict prognosis and immunotherapy response in multiple cohorts, and displayed superior performance than other forms of cell death-related signatures or 52 published signatures. The melanoma patients with low ICDscore were marked with high infiltration of immune cells, high expression of immune checkpoint inhibitor-related genes, and increased tumor mutation burden. CONCLUSIONS: In conclusion, we constructed a stable and robust ICD-related signature for evaluating the prognosis and benefits of immunotherapy, and it could serve as a promising tool to guide decision-making and surveillance for individual melanoma patients.