Exocyst complex component 3 like 1 (EXOC3L1) is widely present in various human tissues, which mainly regulates insulin secretion. However, its roles in tumors remain unclear. In the present study, we aimed to investigate the roles of EXOC3L1 in pan-cancer, and the data was downloaded from of the University of California Santa Cruz (UCSC) Xena and the Cancer Genome Atlas (TCGA). The expression status of EXOC3L1 was studied in the TCGA_GTEx samples, TCGA samples and paired samples in TCGA, respectively. Subsequently, Kaplan-Meier analysis was applied to 33 kinds of tumors in TCGA, among the cancers that EXOC3L1 can affect prognosis, clinical correlation analysis and univariate Cox regression analysis were performed. Furthermore, representative cancers kidney renal clear cell carcinoma (KIRC) and lung squamous cell carcinoma (LUSC) with a sample size larger than 500 were selected to construct nomogram models to confirm the prognostic value of EXOC3L1 in cancers. Additionally, the associations of EXOC3L1 with immune cell infiltrations were performed as well. Mechanistically, functional enrichment analysis was performed to explore potential signaling pathways that EXOC3L1 may involve in. Our study found that EXOC3L1 was differentially expressed in a variety of tumors and was associated with the clinical outcomes and immune microenvironment of several tumors, it may affect the occurrence and development of tumors through NOTCH signaling pathway, PI3K-AKT signaling pathway and immune-related pathways. In conclusion, we propose that EXOC3L1 may serve as a potential prognostic biomarker and a promising target for cancer immunotherapy in a variety of cancers.

Background Exocyst complex component 3-like 1 (EXOC3L1) is ubiquitously present in multiple organs. However, its role in esophageal squamous cell carcinoma (ESCC) remains unknown. The aim of this study was to explore the relationship between EXOC3L1 and ESCC. Material/Methods A total of 652 normal samples and 82 ESCC samples obtained from the University of California Santa Cruz (UCSC) Xena were applied to detect the expression difference of EXOC3L1. GSE53625 with 179 paired samples and GSE161533 with 28 paired samples were used for validation. The correlation between clinicopathological features and EXOC3L1 expression was calculated. Kaplan-Meier method was employed to assess the prognostic value of EXOC3L1 in ESCC. Univariate and multivariate Cox regression analyses were carried out to screen the factors contributing to the prognosis of ESCC. In addition, functional enrichment analysis, protein–protein interaction (PPI) network analysis, and immune infiltration analysis were conducted to identify the significantly involved functions of EXOC3L1. Results EXOC3L1 was significantly overexpressed in ESCC compared to normal samples. High expression of EXOC3L1 was associated with worse prognosis, and univariate and multivariate Cox regression analysis demonstrated that EXOC3L1 was an independent prognostic predictor of ESCC. Functional enrichment analysis and immune infiltration analysis disclosed that the expression of EXOC3L1 was correlated with the abundance of several types of immune cells. Conclusions EXOC3L1 plays a crucial role in the prognosis of ESCC, and it may serve as a reliable biomarker for predicting the survival and a potential therapeutic target for ESCC.

The phenotype of pyroptosis has been extensively studied in a variety of tumors, but the relationship between pyroptosis and esophageal squamous cell carcinoma (ESCC) remains unclear. Here, 22 pyroptosis genes were downloaded from the website of Gene Set Enrichment Analysis (GSEA), 79 esophageal squamous cell carcinoma samples and GSE53625 containing 179 pairs of esophageal squamous cell carcinoma samples were collected from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), respectively. Then, pyroptosis subtypes of esophageal squamous cell carcinoma were obtained by cluster analysis according to the expression difference of pyroptosis genes, and a pyroptosis scoring model was constructed by the pyroptosis-related genes screened from different pyroptosis subtypes. Time-dependent receiver operator characteristic (timeROC) curves and the area under the curve (AUC) values were used to evaluate the prognostic predictive accuracy of the pyroptosis scoring model. Kaplan-Meier method with log-rank test were conducted to analyze the impact of the pyroptosis scoring model on overall survival (OS) of patients with esophageal squamous cell carcinoma. Nomogram models and calibration curves were used to further confirm the effect of the pyroptosis scoring model on prognosis. Meanwhile, CIBERSORTx and ESTIMATE algorithm were applied to calculate the influence of the pyroptosis scoring model on esophageal squamous cell carcinoma immune microenvironment. Our findings revealed that the pyroptosis scoring model established by the pyroptosis-related genes was associated with the prognosis and immune microenvironment of esophageal squamous cell carcinoma, which can be used as a biomarker to predict the prognosis and act as a potential target for the treatment of esophageal squamous cell carcinoma.