RESUMO
OBJECTIVE: This study aimed to pinpoint independent predictors influencing overall survival (OS) and cancer-specific survival (CSS) in elderly patients with small cell lung cancer (SCLC) brain metastasis (BM), and to create and validate nomograms for OS and CSS prediction. METHODS: Data from elderly SCLC BM patients were extracted out of the SEER database, including 1200 patients identified from 2010 and 2015 who were randomly allocated into a training set and an internal validation set at a proportion of 7:3, and 666 patients diagnosed between 2018 and 2020 as a temporal external validation set. Independent predictors for OS and CSS were determined through univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis sequentially. Nomograms for OS and CSS were constructed, and validated by the internal and temporal external validation sets. RESULTS: Age, N stage, chemotherapy, and liver metastasis were determined as independent predictors of OS and CSS, while radiotherapy and surgery were not. Nomograms were constructed based on these independent predictors. The results of the receiver operator characteristic (ROC) curves, the areas under the curve (AUC) and calibration curve demonstrated that the nomograms exhibited commendable discriminative ability and calibration. Moreover, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) also suggested that the nomograms possessed superior clinical usefulness and predictive capability relative to the TNM system. CONCLUSIONS: Prognostic nomograms for elderly patients with SCLC BM have been developed, demonstrating good performance in terms of accuracy, reliability, and practicality.
RESUMO
BACKGROUND: The progestin and adipoQ receptors (PAQRs) family contains 11 genes involved in the regulation of metabolism and cancer development. However, a comprehensive understanding of the role of PAQRs in cancer remains largely scarce, and the associations between their expression levels and immune signatures also need to be researched. METHODS: Here, we applied pan-cancer analysis to explore the associations between PAQRs expression and survival, tumor microenvironment (TME), and drug sensitivity from the UCSC Xena and CellMiner databases. Besides, we further studied the expression, survival and somatic mutations of PAQRs in lung cancer (LC) from TCGA database. RESULTS: The results showed that PAQRs had significant heterogeneity with some upregulation and some downregulation in most tumors. Specifically, compared with PAQR3/5/6/9 and MMD2, ADIPOR1/2, PAQR4/7/8 and MMD had higher levels of average expression in all tumor types. PAQRs expression was greatly correlated with survival, immune subtypes, TME, and drug sensitivity. Furthermore, this research concentrated on analyzing the relationship of PAQRs expression with LC prognosis, and proved that ADIPOR2, PAQR4/9 and MMD were independent prognostic factors for LC patients. Finally, based on somatic mutation data, the genetic mutations in LC patients were majorly missense mutations, and TP53 and TTN had the top two highest mutation frequencies. CONCLUSION: Collectively, PAQRs may serve as robust biomarkers to predict the prognosis and guide immunotherapy of tumors, especially LC, which enables novel ways for improving cancer treatment.
