Background Lung adenocarcinoma (LUAD) presents significant challenges in prognosis and treatment efficacy evaluation. While post-translational modifications are known to influence tumor progression, their prognostic value in LUAD remains largely unexplored. Methods We developed a post-translational modification learning signature (PTMLS) using machine learning techniques, analyzing data from 1231 LUAD patients across seven global cohorts. The signature’s efficacy in predicting immunotherapy response was evaluated using 12 immunotherapy cohorts spanning multiple cancer types (n=1201). An in-house LUAD tissue cohort (n=171) was used to validate beta-1,4-galactosyltransferase 2’s (B4GALT2’s) prognostic significance. The role of B4GALT2 in immune exclusion was investigated through in vivo and in vitro experiments. Results The established PTMLS exhibited exceptional predictive capabilities in LUAD patient outcomes, surpassing the efficacy of 98 existing LUAD prognostic indicators. The system’s predictive value was validated across diverse malignancy categories for immunotherapeutic response assessment. From a biological perspective, significant correlations were observed between PTMLS and immunological parameters, whereby elevated PTMLS levels were characterized by attenuated immune responses and immunologically cold neoplastic features. Within the PTMLS framework, B4GALT2 was identified as a crucial molecular component (r=0.82, p<0.05), and its heightened expression was linked to unfavorable clinical outcomes in LUAD cases, particularly in specimens exhibiting CD8-depleted phenotypes. The spatial distribution patterns between B4GALT2 and immune cell populations, specifically CD8+ T lymphocytes and CD20+ B lymphocytes, were elucidated through multiplexed immunofluorescence analysis. Laboratory investigations subsequently established B4GALT2’s regulatory influence on LUAD cellular expansion in both laboratory cultures and animal models. Significantly, suppression of B4GALT2 was found to enhance CD8+ T lymphocyte populations and their functional status, thereby potentiating anti-programmed cell death protein 1 immunotherapeutic efficacy in animal studies. This phenomenon was characterized by reduced CD62L+CD8 T lymphocyte levels alongside elevated GZMB+/CD44+/CD69+CD8 T cell populations. Conclusion The developed PTMLS system represents an effective instrument for individualized prognostic evaluation and immunotherapy stratification in both LUAD and diverse cancer populations. The identification of B4GALT2 as a previously unrecognized oncogenic factor involved in immune exclusion presents a novel therapeutic avenue for LUAD treatment and immunotherapy optimization.

Background Lung adenocarcinoma (LUAD) is a highly heterogeneous disease, posing significant challenges to accurate prognosis prediction. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence programmed cell death (PCD) mechanisms, which are critical in tumorigenesis and cancer progression. However, the prognostic significance of the interplay between mitochondrial function and PCD in LUAD requires further investigation. Methods We analyzed data from 1231 LUAD patients across seven global cohorts to develop a mitochondrial-related PCD signature (MPCDS) using machine learning. Validation was done using six immunotherapy cohorts (LUAD, melanoma, clear cell renal cell carcinoma; n=935) and a pan-cancer cohort of 21 tumor types. An in-house LUAD tissue cohort (n=100) confirmed the prognostic significance of nucleoside diphosphate kinase 4 (NME4). In vivo and in vitro experiments explored NME4’s role in immune exclusion. Results The MPCDS demonstrated strong predictive performance for prognosis in LUAD patients, surpassing 114 previously published LUAD signatures. Additionally, MPCDS effectively predicted outcomes in immunotherapy patients (including those with LUAD, melanoma, and clear cell renal cell carcinoma). Biologically, MPCDS was significantly associated with immune features, with the high MPCDS group exhibiting reduced immune activity and a tendency towards cold tumors. NME4, a key gene within the MPCDS (correlation=0.55, p<0.05), was associated with poorer prognosis in LUAD patients with high expression, particularly in CD8 desert phenotypes, as validated by our in-house cohort. Multiplex immunofluorescence confirmed the spatial colocalization and exclusion relationship between NME4 and immune cells such as CD3+ T cells and CD20+ B cells. Further experiments revealed that NME4 regulated the proliferation and invasion of LUAD cells both in vitro and in vivo. Importantly, inhibiting NME4 increased the abundance and activity of CD8+ T cells and enhanced the antitumor immunity of anti-programmed cell death protein-1 therapy in vivo. Conclusion The MPCDS provides personalized risk assessment and immunotherapy interventions for individual LUAD patients. NME4, a key gene within the MPCDS, has been identified as a novel oncogene associated with immune exclusion and may serve as a new target for LUAD intervention and immunotherapy.

Immunotherapy has brought significant advancements in the treatment of lung adenocarcinoma (LUAD), but identifying suitable candidates remains challenging. In this study, we investigated tumour cell heterogeneity using extensive single‐cell data and explored the impact of different tumour cell cluster abundances on immunotherapy in the POPLAR and OAK immunotherapy cohorts. Notably, we found a significant correlation between CKS1B+ tumour cell abundance and treatment response, as well as stemness potential. Leveraging marker genes from the CKS1B+ tumour cell cluster, we employed machine learning algorithms to establish a prognostic and immunotherapeutic signature (PIS) for LUAD. In multiple cohorts, PIS outperformed 144 previously published signatures in predicting LUAD prognosis. Importantly, PIS reliably predicted genomic alterations, chemotherapy sensitivity and immunotherapy responses. Immunohistochemistry validated lower expression of immune markers in the low‐PIS group, while in vitro experiments underscored the role of the key gene PSMB7 in LUAD progression. In conclusion, PIS represents a novel biomarker facilitating the selection of suitable LUAD patients for immunotherapy, ultimately improving prognosis and guiding clinical decisions.

Background: There is currently a lack of effective biomarkers to evaluate efficacy of neoadjuvant therapy (NAT) for resectable non-small cell lung cancer (NSCLC) patients. Circulating tumor DNA (ctDNA) has been investigated as a non-invasive tool for the assessment of tumor burden and minimal residual disease (MRD). The utility of ctDNA profiling in reflecting NAT efficacy, however, has not been confirmed. This study explored the association of ctDNA change with treatment response to NAT and recurrence-free survival (RFS) after surgery. Methods: Eligible patients with stage IB–IIIA NSCLC were retrospectively included if they had received neoadjuvant immunotherapy combined with chemotherapy (IO+Chemo), dual immunotherapy (IO+IO), or chemotherapy alone (Chemo). We conducted ctDNA profiling before and after NAT, after surgery, and during follow-ups using an ultra-deep lung cancer-specific MRD (LC-MRD) sequencing panel. Results: A total of 22 patients who received NAT followed by surgery between August 2018 and July 2019 were included in this study. The major pathological response (MPR) rates were 58.33% (7/12) in the IO+Chemo group, 25.00% (1/4) in the IO+IO group, and 16.67% (1/6) in the Chemo group. The ctDNA dynamics during NAT were highly concordant with pathologic response, demonstrating 100% sensitivity and 83.33% specificity, for an overall accuracy of 91.67%. Pre-surgery detectable ctDNA (after NAT) trended to correlate with inferior RFS [hazard ratio (HR), 7.41; 95% confidence interval (CI): 0.91–60.22, log-rank P=0.03]. At 3-8 days after surgery, ctDNA was detectable in 31.8% of patients and was an independent risk factor for recurrence (HR, 5.37; 95% CI: 1.27–22.67; log-rank P=0.01). The presence of ctDNA at 3 months after surgery showed 83% sensitivity and 90% specificity for predicting relapse (C-index, 0.79; 95% CI: 0.62–0.95). During disease monitoring after surgery, molecular recurrence by means of ctDNA preceded radiographic relapse, with a median time of 6.83 months. Conclusions: This study investigated the potential of ctDNA in evaluating NAT efficacy in NSCLC, implying the high concordance between ctDNA and pathological response. We also set out the prognostic value of perioperative ctDNA in predicting recurrence.

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Although immune checkpoint inhibitors (ICIs) have brought new treatment options for advanced patients, a considerable proportion still shows limited response. Mitochondrial dysfunction plays a crucial role in tumor development and immune evasion, but its regulatory mechanisms in LUAD immune microenvironment remain unclear. Methods: We integrated 149 mitochondria-related pathways (1,136 coding proteins) to develop and validate the Mitochondrial Pathway Signature (MitoPS) using machine learning approaches across seven independent LUAD cohorts (n=1,231). The system was systematically compared with 129 published LUAD prognostic signatures and validated in seven immunotherapy cohorts (n=451). Multiomics analysis, immunofluorescence staining, and experimental validation were performed to investigate its molecular mechanism. Results: MitoPS demonstrated consistent predictive performance across validation cohorts, with high scores indicating poor prognosis, outperforming 129 existing prognostic models. In immunotherapy cohorts, MitoPS reliably predicted treatment response and prognosis. Immune microenvironment analysis revealed that low MitoPS scores correlated with higher immune cell infiltration and active immune function. Mechanistic studies identified mitochondria-related gene NDUFB10 as a core gene of MitoPS (r=0.38, p<0.05), where its high expression was significantly associated with immune desert phenotype and worse prognosis. Functional experiments confirmed that NDUFB10 knockdown significantly enhanced ICIs therapy and increased GZMB+CD8+T cell infiltration, indicating NDUFB10’s crucial role in regulating tumor immune microenvironment and immunotherapy response. Conclusion: The MitoPS scoring system reliably predicts prognosis and immunotherapy response in patients with LUAD, providing a novel reference for clinical decision-making. Furthermore, its core gene NDUFB10 regulates tumor immune microenvironment, offering a potential therapeutic target for improving immunotherapy outcomes.

We employed single‐cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between ‘cold’ and ‘hot’ tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.