Jianhua Zhang

Background/Objectives: As the leading cause of global cancer mortality, lung cancer suffers from low survival rates and an incomplete understanding of its pathogenesis. Lung squamous cell carcinoma (LSCC) involves dynamic protein and metabolic dysregulation. To gain initial molecular insights into LSCC is therefore essential to explore coordinated molecular alterations and early mechanisms in primary LSCC tissues. Methods: In this pilot study, we integrated proteomic and non-targeted metabolomic profiling to analyze six paired human LSCC tissue specimens and matched adjacent non-cancerous tissues. Results: Preliminary analyses revealed extensive differences at both the proteomic and metabolomic levels. Specifically, proteomic profiling highlighted widespread dysregulation in cell adhesion dynamics, whereas non-targeted metabolomic profiling identified perturbations in several amino acid metabolic pathways. Integrated multi-omics analysis suggested potential co-variation patterns linking endoplasmic reticulum (ER) protein processing, cell adhesion, and amino acid metabolism. Conclusions: Collectively, these pilot findings provide an initial exploratory characterization of paired proteomic and non-targeted metabolomic alterations in LSCC. Given the small cohort size, these results serve as hypothesis-generating molecular signatures, and all findings require confirmation in larger, independent cohorts with orthogonal and functional validation.

Paraptosis plays a critical role in mediating anti-tumor effects by inducing cell death in cancer cells. However, its specific involvement in lung adenocarcinoma (LUAD) remains inadequately understood. This study aims to systematically investigate the prognostic significance and underlying mechanisms of paraptosis-related genes (PRGs) in LUAD. Differentially expressed genes were identified between LUAD and control samples from the training set and cross-referenced with PRGs to generate candidate genes (CGs). Prognostic genes were selected from CGs using regression analysis, leading to the development of a LUAD risk model, which was validated in an independent validation set. Clinical characteristics were analyzed to identify independent prognostic factors for constructing a nomogram. Functional and immune infiltration analyses were performed on high-/low-risk cohorts from the training set. Drug predictions related to prognostic genes were made and subsequently validated through molecular docking. Polymerase chain reaction was performed to validate the expression of prognostic genes. Four prognostic genes (CDKN3, PEBP1, TNFRSF19, and PHB) were identified from 27 CGs through regression analysis. The prognostic risk model demonstrated robust predictive capacity for LUAD prognosis and exhibited generalizability. Significant associations were observed between risk scores and clinical features, including age, TNM.stage, T-stage, and N-stage ( P < .05). These risk scores served as independent prognostic factors for the nomogram model, offering strong predictive power for LUAD. Vorinostat and raloxifene exhibited notable binding affinity for PEBP1. Elevated CDKN3 expression was observed in LUAD, while PEBP1 and TNFRSF19 expressions were reduced. This study highlights the prognostic value of PRGs, specifically CDKN3, PEBP1, TNFRSF19, and PHB. CDKN3 and PHB emerged as risk factors for LUAD prognosis, whereas PEBP1 and TNFRSF19 did not. In-depth analysis of the tumor microenvironment revealed the distribution and correlations of immune cell types influenced by PRGs and risk score. Furthermore, an independent prognostic model for LUAD was developed, enhancing our understanding of high-/low-risk cohorts’ functional pathways. Drug prediction results provided valuable insights into potential therapeutic strategies for LUAD, warranting further investigation.