Abstract
BACKGROUND: Small cell lung cancer (SCLC) represents an aggressive malignancy characterized by marked heterogeneity and neuroendocrine differentiation. Despite its clinical significance, the functional landscape of neuroendocrine function, while neuroactive-signaling-related genes (NRGs) in SCLC pathogenesis remains poorly characterized. Therefore, the aim of this study is to classify SCLC based on neuroactive signaling networks and to analyze the characteristics of these classifications in relation to the immune microenvironment. METHODS: Through integrated analysis of bulk transcriptomic profiling from 79 primary SCLC tumors and single-cell transcriptomic profiling from 11 SCLC tumors, we employed a consensus clustering algorithm to deconvolute transcriptional programs underlying neuroactive signaling networks. Molecular functions and tumor-infiltrated immune cells were estimated from bulk transcriptomes using bioinformatics methods. Single-cell transcriptomic analysis was implemented for cross-validation and cellular characterization. RESULTS: Bulk-seq analyses reported that the transcriptional variability of three major clusters of tumors were associated with different clinical outcomes and biological pathways. Clinical, genomic, and immunological characteristics were observed among three clusters. Furthermore, the key genes module of cluster with the worst survival were identified as neuroactive-signaling-related signature (NRS) and used to classify tumor samples into two distinct intra-tumoral subtypes (H-NRS and L-NRS) with single-cell transcriptomic data. At single-cell level, malignant cells in H-NRS tumor were in later cell state and had more frequent cellular communication. And NRS subsequently was identified as a biomarker correlated with better prognosis for patients receiving chemoimmunotherapy. It was found that Natriuretic Peptide C (NPPC), as one of the key genes in NRS, was overexpressed in SCLC tumor cells and correlated with poor prognosis. Treatment with C-type natriuretic peptide (CNP) facilitates cellular migration and metastatic potential. CONCLUSIONS: This study proposes a novel molecular taxonomy for SCLC grounded in neuroactive signaling networks, suggests a potential prognostic biomarker to aid in therapeutic stratification, and identifies NPPC as a candidate therapeutic target worthy of further investigation in metastatic SCLC. Our findings may help bridge gaps in understanding between neuroendocrine biology and tumor microenvironment (TME) dynamics during SCLC evolution.