Long-read sequencing and proteomics reveal blood transcriptome and protein expression profiles in multiple primary lung cancers

Multiple primary cancers (MPC) refer to the occurrence of two or more independent primary malignant tumors in the same patient, either simultaneously or metachronously. Their clinical diagnosis and differential diagnosis are challenging, treatment strategies are complex, and the incidence has been increasing in recent years. However, there is still a lack of potential biomarkers that can be used for early identification and prognosis assessment, which has become a key bottleneck in current research. This study employed Oxford Nanopore Technologies (ONT) long-read transcriptome sequencing and data-independent acquisition (DIA) proteomics to perform integrated multi-omics analysis of blood samples from patients with only primary lung cancer (OPLC), lung cancer with MPC, and healthy controls (HC), systematically characterizing the molecular features of MPC at both transcript and protein levels. The results showed that MPC patients exhibited significantly increased transcript complexity, with the numbers of differentially expressed genes (DEGs), differentially expressed transcripts (DETs), and differential transcript usage (DTU) events being substantially higher than those in OPLC and HC groups. Multiple genes displayed rich isoform diversity. Functional enrichment analysis indicated that MPC-specific molecules were significantly enriched in immune- and inflammation-related pathways such as NF-κB, NOD-like receptor, Toll-like receptor, and TNF signaling. By integrating gene, transcript, and protein expression profiles, several core molecules (e.g., ATP6AP2, APMAP, CIAO2A, and ITGB2) with consistent expression across multiple regulatory levels were identified, all showing significant and consistent alterations in MPC. This study reveals the molecular characteristics of transcript isoform complexity in the blood samples of MPC patients through long-read sequencing combined with proteomics, providing important theoretical insights for understanding the mechanisms of MPC and identifying potential targets.