Metabolic reprogramming-driven homologous recombination and TCA cycle dysregulation contribute to poor prognoses in lung adenocarcinoma.

Increasing evidence has shown that homologous recombination (HR) and metabolic reprogramming are essential for cellular homeostasis. These two processes are independent as well as closely intertwined. Nevertheless, they have rarely been reported in lung adenocarcinoma (LUAD). We analysed the genomic, immune microenvironment and metabolic microenvironment features under different HR activity states. Using cell cycle, EDU and cell invasion assays, we determined the impacts of si-SHFM1 on the LUAD cell cycle, proliferation and invasion. The levels of isocitrate dehydrogenase (IDH) and α-ketoglutarate dehydrogenase (α-KGDH) were determined by ELISA in the NC and si-SHFM1 groups of A549 cells. Finally, cell samples were used to extract metabolites for HPIC-MS/MS to analyse central carbon metabolism. We found that high HR activity was associated with a poor prognosis in LUAD, and HR was an independent prognostic factor for TCGA-LUAD patients. Moreover, LUAD samples with a high HR activity presented low immune infiltration levels, a high degree of genomic instability, a good response status to immune checkpoint blockade therapy and a high degree of drug sensitivity. The si-SHFM1 group presented a significantly higher proportion of cells in the G0/G1 phase, lower levels of DNA replication, and significantly lower levels of cell migration and both TCA enzymes. Our current results indicated that there is a strong correlation between HR and the TCA cycle in LUAD. The TCA cycle can promote SHFM1-mediated HR in LUAD, raising their activities, which can finally result in a poor prognosis and impair immunotherapeutic efficacy.

IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.

Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD. IBPGNET can integrate different omics data efficiently and provide global interpretability. In addition, our experimental results show that IBPGNET outperforms other classification methods in 5-fold cross-validation. IBPGNET identified PSMC1 and PSMD11 as genes associated with LUAD recurrence, and their expression levels were significantly higher in LUAD cells than in normal cells. The knockdown of PSMC1 and PSMD11 in LUAD cells increased their sensitivity to afatinib and decreased cell migration, invasion and proliferation. In addition, the cells showed significantly lower EGFR expression, indicating that PSMC1 and PSMD11 may mediate therapeutic sensitivity through EGFR expression.

Construction of 11 metabolic-related lncRNAs to predict the prognosis in lung adenocarcinoma.

OBJECTIVE: To explore the metabolism-related lncRNAs in the tumorigenesis of lung adenocarcinoma. METHODS: The transcriptome data and clinical information about lung adenocarcinoma patients were acquired in TCGA (The Cancer Genome Atlas). Metabolism-related genes were from the GSEA (Gene Set Enrichment Analysis) database. Through differential expression analysis and Pearson correlation analysis, lncRNAs about lung adenocarcinoma metabolism were identified. The samples were separated into the training and validation sets in the proportion of 2:1. The prognostic lncRNAs were determined by univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) regression. A risk model was built using Multivariate Cox regression analysis, evaluated by the internal validation data. The model prediction ability was assessed by subgroup analysis. The Nomogram was constructed by combining clinical indicators with independent prognostic significance and risk scores. C-index, calibration curve, DCA (Decision Curve Analysis) clinical decision and ROC (Receiver Operating Characteristic Curve) curves were obtained to assess the prediction ability of the model. Based on the CIBERSORT analysis, the correlation between lncRNAs and tumor infiltrating lymphocytes was obtained. RESULTS: From 497 lung adenocarcinoma and 54 paracancerous samples, 233 metabolic-related and 11 prognostic-related lncRNAs were further screened. According to the findings of the survival study, the low-risk group had a greater OS (Overall survival) than the high-risk group. ROC analysis indicated AUC (Area Under Curve) value was 0.726. Then, a nomogram with T, N stage and risk ratings was developed according to COX regression analysis. The C-index was 0.743, and the AUC values of 3- and 5-year survival were 0.741 and 0.775, respectively. The above results suggested the nomogram had a good prediction ability. The results based on the CIBERSORT algorithm demonstrated the lncRNAs used to construct the model had a strong correlation with the polarization of immune cells. CONCLUSIONS: The study identified 11 metabolic-related lncRNAs for lung adenocarcinoma prognosis, on which basis a prognostic risk scoring model was created. This model may have a good predictive potential for lung adenocarcinoma.

TAGAP expression influences CD4+ T cell differentiation, immune infiltration, and cytotoxicity in LUAD through the STAT pathway: implications for immunotherapy.

Background: T-cell Activation GTPase Activating Protein (TAGAP) plays a role in immune cell regulation. This study aimed to investigate TAGAP's expression and its potential impact on CD4+ T cell function and prognosis in lung adenocarcinoma (LUAD). Methods: We analyzed TAGAP expression and its correlation with immune infiltration and clinical data in LUAD patients using multiple datasets, including The Cancer Genome Atlas (TCGA-LUAD), Gene Expression Omnibus (GEO), and scRNA-seq datasets. In vitro and in vivo experiments were conducted to explore the role of TAGAP in CD4+ T cell function, chemotaxis, and cytotoxicity. Results: TAGAP expression was significantly lower in LUAD tissues compared to normal tissues, and high TAGAP expression correlated with better prognosis in LUAD patients. TAGAP was positively correlated with immune/stromal/ESTIMATE scores and immune cell infiltration in LUAD. Single-cell RNA sequencing revealed that TAGAP was primarily distributed in CD4+/CD8+ T cells. In vitro experiments showed that TAGAP overexpression enhanced CD4+ T cell cytotoxicity, proliferation, and chemotaxis. Gene Set Enrichment Analysis (GSEA) indicated that TAGAP was enriched in the JAK-STAT signaling pathway. In vivo experiments in a xenograft tumor model demonstrated that TAGAP overexpression suppressed tumor growth and promoted CD4+ T cell cytotoxicity. Conclusions: TAGAP influences CD4+ T cell differentiation and function in LUAD through the STAT pathway, promoting immune infiltration and cytotoxicity. This study provides a scientific basis for developing novel LUAD immunotherapy strategies and exploring new therapeutic targets.

Rational cyclization-based minimization of entropy penalty upon the binding of Nrf2-derived linear peptides to Keap1: A new strategy to improve therapeutic peptide activity against sepsis.

Nrf2 is a critical regulator of innate immune response and survival during sepsis, which is constitutively degraded through binding to the Keap1 adapter protein of E3 ubiquitin ligase. Two linear peptides DLG and ETG derived from, respectively, the low-affinity and high-affinity motifs of Nrf2 binding site exhibit self-binding affinity to Keap1 central hole (active pocket); they can be exploited as therapeutic self-inhibitory peptides to disrupt the Nrf2-Keap1 interaction. Molecular dynamics simulation and binding energetics decomposition reveal that the two peptides possess large flexibility and intrinsic disorder in unbound free state, and thus would incur a considerable entropy penalty upon binding to Keap1. In order to improve Keap1-peptide binding affinity (or free energy ΔG), instead of traditionally increasing favorable enthalpy contribution (ΔH) we herein describe a rational peptide cyclization strategy to minimize unfavorable entropy penalty (ΔS) upon the binding of Nrf2-derived linear peptides to Keap1. Crystal structure analysis impart that the native active conformations of DLG and ETG peptides bound with Keap1 are folded into U-shape and hairpin configurations, respectively, and adopt their turning head to insert into the central hole of Keap1. Here, cyclization is designed by adding a disulfide bond across the two arms of DLG U-shape or ETG hairpin, which would not influence the direct intermolecular interaction between Keap1 and peptide as well as desolvation effect involved in the interaction, but can effectively constrain the conformational flexibility and disorder of the two peptides in free state, thus largely minimizing entropy penalty upon the binding. Both free energy calculation and binding affinity assay substantiate that the cyclization, as might be expected, can moderately or considerably enhance peptide binding potency to Keap1, with affinity (dissociation constant Kd) increase by 1.4-7.5-fold for designed cyclic peptides relative to their linear counterparts.

Association of epidermal growth factor receptor (EGFR) gene polymorphism with lung cancer risk: a systematic review.

Epidermal growth factor receptor (EGFR) is a member of the tyrosine kinase receptor family, which is thought to be involved in the development of cancer, as the EGFR gene is often amplified, and/or mutated in cancer cells. Lung cancer remains one of the most major causes of morbidity and mortality worldwide, accounting for more deaths than any other cancer cause. Gene polymorphism factor has been reported to be an important factor which increases the susceptibility of lung cancer. There lacks a well-documented diagnostic approach for the lung cancer risk, and the etiology of lung cancer is not clear. The current systematic review was performed to explore the association of EGFR gene polymorphism with lung cancer risk. In this review, association of EGFR 181946C > T, 8227G > A gene polymorphism with lung cancer was found, and EGFR Short genotype of cytosine adenine repeat number polymorphism was significantly associated with an increased risk of lung cancer.