Characterization of the Immune Infiltration Landscape and Identification of Prognostic Biomarkers for Esophageal Cancer.

Immunotherapy is an effective treatment for esophageal cancer (ESCA) patients. However, there are no dependable markers for predicting prognosis and immunotherapy responses in ESCA. Our study aims to explore immune gene prognostic models and markers in ESCA as well as predictors for immunotherapy. The expression profiles of ESCA were obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC) databases. Cox regression analysis was performed to construct an immune gene prognostic model. ESCA was grouped into three immune cell infiltration (ICI) clusters by CIBERSORT algorithm. The immunotherapy response of patients in different ICI score clusters was also compared. The copy number variations, somatic mutations, and single nucleotide polymorphisms were analyzed. Enrichment analyses were also performed. An immune gene prognostic model was successfully constructed. The ICI score may be used as a predictor independent of tumor mutation burden. Enrichment analyses showed that the differentially expressed genes were mostly enriched in microvillus and the KRAS and IL6/JAK/STAT3 pathways. The top eight genes with the highest mutation frequencies in ESCA were identified and all related to the prognosis of ESCA patients. Our study established an effective immune gene prognostic model and identified markers for predicting the prognosis and immunotherapy response of ESCA patients.

LIMD2 is a Prognostic and Predictive Marker in Patients With Esophageal Cancer Based on a ceRNA Network Analysis.

Globally, esophageal cancer (ECA) is the seventh most common cancer and sixth most common cause of cancer-associated mortality. However, there are no reliable prognostic and predictive molecular markers for ECA; in addition, the pathogenesis of ECA is not fully elucidated. The expressions of circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) of ECA and control groups were obtained from the RNA-sequencing (RNA-seq) data of our hospital, the Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) datasets. Analyses of differentially expressed genes, the circRNA-miRNA-mRNA-competing endogenous RNA (ceRNA) network, and functional/pathway enrichment were conducted. The key targets in the ceRNA network that showed significant results in survival Cox regression analyses were selected. Furthermore, analyses of immune infiltration and autophagy genes related to the key targets were performed. Seven circRNAs, 22 miRNAs, and 34 mRNAs were identified as vital genes in ECA; the nuclear factor-κ-gene binding (NF-κB) and phosphatidylinositol-3 kinase/protein kinase B (PI3K-Akt) signaling were identified as the most enriched pathways. In addition, the LIM domain containing 2 (LIMD2) was an independent predictor of prognosis in ECA patients and closely associated with immunity and autophagy. Moreover, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) revealed significant upregulation of LIMD2 expression in ECA tissues. ECA may be closely correlated with NF-κB and PI3K/Akt signaling. In addition, LIMD2 could be a potential prognostic and predictive marker of ECA.