CASTOR1 phosphorylation predicts poor survival in male patients with KRAS-mutated lung adenocarcinoma.

BACKGROUND: Lung cancer, a leading global cause of cancer-related mortality, necessitates enhanced prognostic markers for improved treatment outcomes. We have previously shown a tumor suppressive role of cytosolic arginine sensor for mTORC1 subunit 1 (CASTOR1), which is targeted for degradation upon phosphorylation at S14 (pCASTOR1) in multiple types of cancer. This study focuses on the predictive value of pCASTOR1 in lung adenocarcinoma (LUAD) patients with KRAS mutations. RESULTS: Employing a newly developed pCASTOR1 specific antibody, we found that tumor cells exhibited significantly elevated pCASTOR1 scores compared to non-tumor cells (P < 0.05). Higher pCASTOR1 scores predicted poorer overall survival (OS) (HR = 3.3, P = 0.0008) and relapse-free survival (RFS) (HR = 3.0, P = 0.0035) in male patients with KRAS mutations. pCASTOR1 remained an independent predictor for OS (HR = 4.1, P = 0.0047) and RFS (HR = 3.5, P = 0.0342) after controlling for other factors. Notably, in early-stage LUAD, elevated pCASTOR1 scores were associated with significantly worse OS (HR = 3.3, P = 0.0176) and RFS (HR = 3.1, P = 0.0277) in male patients with KRAS mutations, akin to late-stage patients. CONCLUSION: Elevated pCASTOR1 scores serve as biomarkers predicting poorer OS and RFS in male LUAD patients with KRAS mutations, offering potential clinical utility in optimizing treatment strategies for this subgroup.

Comprehensive analysis identifies YKT6 as a potential prognostic and diagnostic biomarker in lung adenocarcinoma.

BACKGROUND: Lung cancer is the most common cause of cancer-related death worldwide. The most prevalent histological subtype of lung cancer is lung adenocarcinoma (LUAD), with incidence rising each year. Treating LUAD remains a significant issue due to a lack of early diagnosis and poor therapy outcomes. YKT6 is a member of the SNARE protein family, whose clinical value and biological function in LUAD has yet to be established. METHODS: TCGA, HPA and UALCAN were used to analyze YKT6 mRNA and protein levels, the correlation between YKT6 expression and clinicopathological features and prognosis. YKT6 mRNA and protein expression were verified by qRT-PCR, immunohistochemistry (IHC) and tissue microarrays (TMA). Additionally, lung cancer cell lines were chosen for YKT6 silencing to explore the effects on cell proliferation and migration. The cBioPortal was used to select YKT6-related genes. Protein-protein interaction (PPI) network was created based on STRING database and hub genes were screened, with their expression levels and prognosis values in LUAD analyzed accordingly. YKT6-related genes were enriched by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses. RESULTS: In LUAD, YKT6 was distinctly highly expressed with relation to clinical features of staging, smoking, lymph node metastasis, and TP53 mutation. Elevated YKT6 expression was linked to adverse prognosis, serving as an independent unfavorable prognostic factor. Moreover, YKT6 presented high diagnostic value in LUAD patients (AUC = 0.856). Experimental validation indicated that freshly collected LUAD tissues showed significantly high mRNA expression of YKT6. IHC and TMA verified increased YKT6 protein level in LUAD. Knockdown of YKT6 inhibited cell proliferation and promoted apoptosis, with mitigated capability of migration and invasion. The top ten hub genes screened by PPI network were highly expressed in LUAD, and significantly associated with poor prognosis. GO and KEGG analyses showed that YKT6-related genes were mainly involved in cell cycle. CONCLUSION: Elevated YKT6 expression is related to poor prognosis of LUAD patients. YKT6 can serve as a novel biomarker for LUAD diagnosis and prognosis. Cell proliferation, migration and invasion was impaired with increased apoptosis upon YKT6 silencing in lung cancer cells. In summary, this study comprehensively uncovered that YKT6 could be identified as a potential prognostic and diagnostic biomarker in LUAD.

Mitochondrial Pyruvate Carrier 1 as a Novel Prognostic Biomarker in Non-Small Cell Lung Cancer.

BACKGROUND: Abnormal mitochondrial pyruvate carrier 1 (MPC1) expression plays a key role in tumor metabolic reprogramming and progression. Understanding its significance in non-small cell lung cancer (NSCLC) is crucial for identifying therapeutic targets. METHODS: TIMER 2.0 was utilized to assess the expression of MPC1 in both normal and cancer tissues in pan-cancer. Overall survival (OS) differences between high and low MPC1 expression were analyzed in NSCLC using the Cancer Genome Atlas (TCGA) datasets. We also examined the expression of MPC1 in NSCLC cell lines using western blotting and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). In addition, the tissue samples and clinical information of 80 patients with NSCLC from our hospital were collected. Immunohistochemistry (IHC) was used to assess MPC1 expression, and OS was evaluated using Kaplan-Meier curves and the log-rank test. Univariate and multivariate Cox regression analyses were conducted to evaluate the prognostic values of the clinical characteristics and MPC1expression. RESULTS: Analysis of public databases suggested that MPC1 was downregulated in NSCLC compared to that in normal lung tissue and predicted poor prognosis. In addition, the expression of MPC1 in NSCLC cell lines was lower than that in human bronchial epithelial (HBE) cells at both protein and mRNA levels. Further clinical analysis suggested that MPC1 expression was correlated with age, tumor T stage, and TNM stage. Kaplan-Meier analysis revealed that NSCLC patients with high MPC1 expression had a better prognosis, particularly in lung adenocarcinoma (LUAD), whereas no survival benefit was observed in lung squamous cell carcinoma (LUSC). Univariate and multivariate analyses suggested that MPC1 was an independent prognostic factor for patients with NSCLC. CONCLUSIONS: MPC1 is poorly expressed in NSCLC, particularly in LUAD, which predicts a poor prognosis and may serve as an independent prognostic factor. Further studies on MPC1 may reveal new targets for the treatment of NSCLC.

Bioinformatic prediction of miR-320a as a potential negative regulator of CDGSH iron-sulfur domain 2 (CISD2), involved in lung adenocarcinoma bone metastasis via MYC activation, and associated with tumor immune infiltration.

Background: Ferroptosis, a form of regulated cell death associated with iron-dependent lipid peroxidation, plays a role in cancer progression. However, the specific mechanisms of ferroptosis in lung adenocarcinoma (LUAD) bone metastasis (BM) remain unclear. Using bioinformatics analysis, this study sought to identify the ferroptosis-associated genes involved in BM in LUAD, thus providing potential novel targets for the treatment of BM in LUAD. Methods: The RNA expression dataset GSE10799 was acquired from the Gene Expression Omnibus (GEO) database, and intersected with the ferroptosis dataset to identify ferroptosis-related differentially expressed genes (DEGs). The expression of candidate genes and their correlation with the prognosis of LUAD patients were validated in The Cancer Genome Atlas (TCGA) database. A protein gene interaction network was constructed using GeneMania and Retrieval of Interacting Genes/Proteins (STRING) databases. The association between the candidate genes and immune cells was assessed via TCGA and Tumor IMmune Estimation Resource (TIMER) databases. The potential mechanisms were elucidated by a gene set enrichment analysis (GSEA). The relevant microRNAs (miRNAs or miRs) that bind to the 3'untranslated region (3'UTR) end of candidate genes' mRNA was explored using the TargetScan database. The expression of these candidate miRNAs in LUAD was validated and the correlation between candidate miRNAs and candidate mRNAs was tested using the TCGA database. Finally, the clinical data of 40 LUAD patients were retrospectively analyzed to evaluate the clinical value of candidate gene expression for LUAD BM patients. Results: In this research, 15 ferroptosis-related DEGs in LUAD BM were identified. TCGA database analysis indicated that patients with low levels of CDGSH iron-sulfur domain 2 (CISD2) in LUAD had better disease-specific survival (DSS), overall survival (OS), and a better progression-free interval (PFI) than those with high levels of CISD2. The TIMER database results show that the expression of CISD2 is correlated with the infiltration levels of various immune cells. The GSEA indicated that CISD2 might influence biological activity in LUAD by participating in cell-cycle regulation, mitochondrial translation, DNA damage repair, c-Myc (MYC) activation, and the P53 signaling pathway. Through the combined analysis of the TargetScan and TCGA databases, hsa-miR-320a was identified as the optimal upstream regulatory miRNA. The immunohistochemistry data indicated that the positive CISD2 expression rates and immunohistochemistry scores of the patients with BM were significantly higher than those of the patients without BM (P<0.05). The high expression of CISD2 is a significant risk factor for BM in LUAD. Conclusions: The downregulation of CISD2 expression may extend DSS, OS, and the PFI of LUAD patients. Thus, CISD2 could serve as a novel predictive biomarker for LUAD patients. Further, miR-320a might negatively regulate CISD2 and participate in LUAD BM by activating MYC. These data provide a potential perspective for developing anticancer therapies for LUAD-BM patients.

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. Methods: We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. Results: First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. Conclusions: We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.

Intratumoral and peritumoral radiomics combined with computed tomography features for predicting the invasiveness of lung adenocarcinoma presenting as a subpleural ground-glass nodule with a consolidation-to-tumor ratio ≤50.

Background: Preoperative accurate judgment of the degree of invasiveness in subpleural ground-glass lung adenocarcinoma (LUAD) with a consolidation-to-tumor ratio (CTR) ≤50% is very important for the choice of surgical timing and planning. This study aims to investigate the performance of intratumoral and peritumoral radiomics combined with computed tomography (CT) features for predicting the invasiveness of LUAD presenting as a subpleural ground-glass nodule (GGN) with a CTR ≤50%. Methods: A total of 247 patients with LUAD from our hospital were randomly divided into two groups, i.e., the training cohort (n=173) and the internal validation cohort (n=74) (7:3 ratio). Furthermore, 47 patients from three other hospitals were collected as the external validation cohort. In the training cohort, the differences in clinical-radiological features were compared using univariate and multivariate analyses. The gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV5, GPTV10, and GPTV15) radiomics models were constructed based on intratumoral and peritumoral (5, 10, and 15 mm) radiomics features. Additionally, the radscore of the best radiomics model and clinical risk factors were used to construct a combined model and the predictive efficacy of the model was evaluated in the validation cohorts. Finally, the receiver operating characteristics (ROC) curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. Results: Tumor size and CTR were independent risk factors for predicting the invasiveness of LUAD. The GPTV10 model outperformed the other radiomics models, with AUC values of 0.910, 0.870, and 0.887 in the three cohorts. The AUC values of the combined model were 0.912, 0.874, and 0.892. Conclusions: A nomogram based on GPTV10-radscore, tumor size, and CTR exhibited high predictive efficiency for predicting the invasiveness of LUAD.

Comprehensive Analysis of Characteristics of Cuproptosis-Related LncRNAs Associated with Prognosis of Lung Adenocarcinoma and Tumor Immune Microenvironment.

As a novel discovered mechanism of cell death, cuproptosis is copper-dependent and induces protein toxicity related to advanced tumors, disease prognosis, and human innate and adaptive immune response. However, it has not yet been fully established how the prognosis of lung adenocarcinoma (LUAD) is related to the immune microenvironment of cuproptosis-related lncRNAs using several bioinformatic techniques. In the study, 19 genes related to cuproptosis were collected. Subsequently, 783 lncRNAs related to the co-expression of cuproptosis were obtained. Moreover, the Cox model revealed and constructed four lncRNA (AC012020.1, AC114763.1, AL161431.1, AC010260.1) prognostic markers related to cuproptosis. Based on the median risk score (RS) values, patients were categorized into two groups: high risk and low risk. The Kaplan-Meier (KM) survival curve depicted a statistically significant overall survival (OS) rate among two groups. Principal component analysis (PCA) and receiver operator characteristic curve (ROC) proved that the model had promising ability in prognosis. The analysis of univariate and multivariate Cox regression revealed that RS served as an independent prognostic factor. Moreover, multivariate Cox regression was employed for the establishment of a nomogram of prognostic indicators. The tumor mutational burden (TMB) depicted a considerable difference between the two risk groups. The immunotherapy response of LUAD patients with high risk was improved compared to low risk patients. The study also revealed that drug sensitivity associated with LUAD was significantly linked to RS. The findings could be helpful to establish a good diagnosis, prognosis, and management regime for patients with LUAD.

Bioinformatics analysis of BTK expression in lung adenocarcinoma: implications for immune infiltration, prognostic biomarkers, and therapeutic targeting.

In recent years, as more and more lung-cancer patients have been treated with immunotherapeutic agents, their survival has been prolonged compared to before. It is well known that BTK (Bruton's tyrosine kinase) is predominantly found in cells of the hematopoietic system. However, there is a distinct lack of literature on BTK expression in lung adenocarcinoma (LUAD) patients and its effect on the immune microenvironment. Consequently, the main goal of this investigation was to analyze how BTK expression in lung adenocarcinoma affects its progression, along with its prognostic significance, through the utilization of bioinformatics online resources and publicly available databases. Data on the sequencing results and clinical records of lung adenocarcinoma patients were gathered from The Cancer Genome Atlas (TCGA) database. Based on the expression level of BKT, TCGA categorized lung adenocarcinoma patients into BTK high-expression and low-expression groups. We investigated the effects of BKT on clinicopathologic, genomic, and immunologic characteristics of lung adenocarcinoma patients. We analyzed BTK mRNA expression in tumors and normal tissues using two key resources: Tumor Immuno Estimation Resource 2.0 (TIMER 2.0) and Gene Expression Profiling Interactive Analysis 2 (GEPIA 2). We analyzed the prognosis of the patients using GEPIA2 and validated the results using univariate and multivariate analyses. In addition, we assessed BTK protein expression by Human Protein Atlas (HPA). We sought to elucidate the clinical prognostic significance of BTK in The TCGA using the online tool GEPIA 2. Furthermore, to clarify the biologic roles and pathways linked to BTK, we conducted a genomic enrichment analysis of the information. To predict the proportion of various immune cell infiltrations in the immune microenvironment of lung adenocarcinoma patients diagnosed in the TCGA database, we performed an analysis using the TIMER online tool. Using TIMER and CIBERSORT, the correlation between genes co-expressed with BTK and the corresponding tumor-infiltrating immune cells was explored; finally, the relationship between BTK expression and immune infiltration and immune checkpoints in the TMB group and the high and low groups was analyzed by R language analysis using the TCGA database. The expression of BTK provides some hints about the prognosis of the patients. The high expression of BTK is involved in immune response regulation signaling pathways, leukocyte-mediated immunity, leukocyte intercellular adhesion, graft rejection, and complement. Analysis of the GEPIA 2 database showed that BTK was co-expressed with the genes FGD2, SASH3, NCKAP1L, CD53, ARHGAP30 and LPXN. Increased expression of the above-mentioned genes resulted in increased proportions of CD8 + T cells, memory CD4 + T cells, B cells, macrophages, and dendritic cells, and decreased proportions of Treg cells and TH2 cells. In addition, our study revealed a strong positive correlation between various key immune checkpoints (e.g., PDCD1, CD274, PDCD1LG2, CTLA4, HAVCR2, LAG3, TIGIT, and SIGLEC15) and BTK expression. In conclusion, increased BTK expression in lung adenocarcinoma is closely associated with prolonged survival of lung-cancer patients. Moreover, the genes classified under the BTK high-expression group exhibit significant enrichment in immune-related pathways, suggesting a potential impact on the tumor microenvironment. We investigated the potential of BTK as a tumor suppressor gene in predicting prolonged patient survival. In addition, we further investigated the possibility that BTK further affects the immunotherapeutic response of patients by influencing the microenvironment of tumor immune infiltration, but the relevant mechanisms remain to be further studied.

Exploring the prognostic and therapeutic value of HIF1A in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) remains a challenge within the realm of non-small cell lung cancer (NSCLC), demanding innovative diagnostic and therapeutic solutions. In this study, we systematically detected the correlation between the expression of hypoxia-induced factor 1A (HIF1A) and the clinical characteristics of LUAD, alongside lung squamous cell carcinoma (LUSC). Our bioinformatic analysis reveals that HIF1A mRNA expression is significantly upregulated in both LUAD and LUSC samples compared to non-tumorous lung tissues. The overexpression is positively correlated with increased copy number variation and negatively associated with promoter methylation. However, meta-analysis and survival analyses revealed a pronounced association between elevated HIF1A expression and poor clinical outcome specifically within the LUAD subset, with no such correlation evident in LUSC. Additionally, we explored the interplay between HIF1A expression, leukocyte infiltration, and the presence of immunosuppressive markers, revealing HIF1A's suppressive role in cytotoxicity against cancer cells. Furthermore, we performed in silico prediction to explore the correlations between HIF1A and its interacting proteins, associated pathways, glycolysis, and m6A modification, and the feasibility of targeting HIF1A with specific drugs. In summary, our study revealed the prognostic significance and therapeutic potential of HIF1A in LUAD.

Construction of lncRNA prognostic model related to disulfidptosis in lung adenocarcinoma.

Background: Lung cancer is one of the malignant tumors with the highest rates of morbidity and mortality worldwide. One of the most common histological types of lung cancer is lung adenocarcinoma (LUAD). Despite the fact that development in medicine has significantly improved some patients' prognoses, the overall survival (OS) rate is still very low. In glucose-deficient SLC7A11-overexpressed cancer cells, the accumulation of disulfide molecules leads to abnormal disulfide bonding between actin cytoskeletal proteins, interferes with their tissues, and eventually leads to actin network collapse and cell death. This mode of cell death is called disulfidptosis. Studies have shown that disulfidptosis may be a new target for cancer treatment. However, the role of disulfidptosis in LUAD is still unknown. Methods: LUAD transcriptome and clinical information from The Cancer Genome Atlas (TCGA) was downloaded. The co-expression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Cox regression analysis was performed to screen the disulfidptosis-related lncRNAs (DRLs) and build the prognostic model. Kaplan-Meier curve, Cox regression analysis, and receiver operating characteristic (ROC) curve was used to validate the model. Then a nomogram is made to predict the prognosis of LUAD patients. Finally, fresh-collected clinical samples were used to verify the expression of DRLs in LUAD. Results: The prognostic model with six DRLs was developed to predict the prognosis of LUAD, with superior prognosis value compared to other clinical variables. The Cox regression analysis revealed that T stage, N stage and the risk score were identified as independent variables that affected LUAD prognosis. ROC curve revealed that the model has a moderate diagnostic value, with an AUC of 1-year 0.684, 3-year 0.664, and 5-year 0.588. Moreover, nine medications connected to LUAD treatment were acquired through drug sensitivity analysis. LUAD tissue validation showed that AC012073.1, AC012615.1, EMSLR, and SNHG12 were highly expressed, while AL606834.1 and AL365181.2 with low expression. Conclusion: Six DRLs were screened and verified to construct the prognostic model, which can accurately predict the LUAD prognosis. It establishes a basis for further exploration into the molecular mechanisms underlying LUAD and identification of potential biomarkers for diagnosis, prognosis, and therapeutic targets.

Circ_BBS9 as an early diagnostic biomarker for lung adenocarcinoma: direct interaction with IFIT3 in the modulation of tumor immune microenvironment.

Background: Introduction: Circular RNAs (circRNAs) have been identified as significant contributors to the development and advancement of cancer. The objective of this study was to examine the expression and clinical implications of circRNA circ_BBS9 in lung adenocarcinoma (LUAD), as well as its potential modes of action. Methods: The expression of Circ_BBS9 was examined in tissues and cell lines of LUAD through the utilization of microarray profiling, quantitative real-time polymerase chain reaction (qRT-PCR), and western blot analysis. In this study, we assessed the impact of circ_BBS9 on the proliferation of LUAD cells, as well as its influence on ferroptosis and tumor formation. To analyze these effects, we employed CCK-8 assays and ferroptosis assays. The identification of proteins that interact with Circ_BBS9 was achieved through the utilization of RNA pull-down and mass spectrometry techniques. A putative regulatory network comprising circ_BBS9, miR-7150, and IFIT3 was established using bioinformatics study. The investigation also encompassed the examination of the correlation between the expression of IFIT3 and the invasion of immune cells. Results: Circ_BBS9 was significantly downregulated in LUAD tissues and cell lines. Low circ_BBS9 expression correlated with poor prognosis. Functional experiments showed that circ_BBS9 overexpression inhibited LUAD cell proliferation and promoted ferroptosis in vitro and suppressed tumor growth in vivo. Mechanistically, circ_BBS9 was found to directly interact with IFIT3 and regulate its expression by acting as a sponge for miR-7150. Additionally, IFIT3 expression correlated positively with immune infiltration in LUAD. Conclusion: Circ_BBS9 has been identified as a tumor suppressor in lung adenocarcinoma (LUAD) and holds promise as a diagnostic biomarker. The potential mechanism of action involves the modulation of ferroptosis and the immunological microenvironment through direct interaction with IFIT3 and competitive binding to miR-7150. The aforementioned findings offer new perspectives on the pathophysiology of LUAD and highlight circ_BBS9 as a potentially valuable target for therapeutic interventions.

Machine learning-based integration of CD8 T cell-related gene signatures for comprehensive prognostic assessment in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response. Methods: By leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts. Results: RiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups. Conclusions: In summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.

LncRNA MALAT-1 modulates EGFR-TKI resistance in lung adenocarcinoma cells by downregulating miR-125.

Molecular targeted therapy resistance remains a major challenge in treating lung adenocarcinoma (LUAD). The resistance of Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs, epidermal growth factor receptor-tyrosine kinase inhibitor) plays a dominant role in molecular targeted therapy. Our previous research demonstrated the role of MALAT-1 (Metastasis-associated lung adenocarcinoma transcript 1) in the formation of Erlotinib-resistant LUAD cells. This study aims to uncover the mechanism of MALAT-1 overexpression in Erlotinib-resistant LUAD cells. The RT2 LncRNA PCR array system was used to explore MALAT-1 regulation in Erlotinib-resistant LUAD cells through patient serum analysis. Dual luciferase reporter experiments confirmed the binding between MALAT-1 and miR-125, leading to regulation of miR-125 expression. Functional assays were performed to elucidate the impact of MALAT1 on modulating drug resistance, growth, and Epithelial-mesenchymal transition (EMT, Epithelial-mesenchymal transition) in both parental and Erlotinib-resistant LUAD cells. The investigation unveiled the mechanism underlying the competing endogenous RNA (ceRNA, competing endogenouse RNA) pathway. MALAT1 exerted its regulatory effect on miR-125 as a competing endogenous RNA (ceRNA). Moreover, MALAT1 played a role in modulating the sensitivity of LUAD cells to Erlotinib. Rab25 was identified as the direct target of miR-125 and mediated the functional effects of MALAT1 in Erlotinib-resistant LUAD cells. In conclusion, our study reveals overexpress MALAT-1 cause the drug resistance of EGFR-TKIs in non-small cell lung cancer (NSCLC) through the MALAT-1/miR-125/Rab25 axis. These findings present a potential novel therapeutic target and perspective for the treatment of LUAD.

Silencing of the long non-coding RNA LINC00265 triggers autophagy and apoptosis in lung cancer by reducing protein stability of SIN3A oncogene.

Background: Long non-coding RNAs are important regulators in cancer biology and function either as tumor suppressors or as oncogenes. Their dysregulation has been closely associated with tumorigenesis. LINC00265 is upregulated in lung adenocarcinoma and is a prognostic biomarker of this cancer. However, the mechanism underlying its function in cancer progression remains poorly understood. Methods: Here, the regulatory role of LINC00265 in lung adenocarcinoma was examined using lung cancer cell lines, clinical samples, and xenografts. Results: We found that high levels of LINC00265 expression were associated with shorter overall survival rate of patients, whereas knockdown of LINC00265 inhibited proliferation of cancer cell lines and tumor growth in xenografts. Western blot and flow cytometry analyses indicated that silencing of LINC00265 induced autophagy and apoptosis. Moreover, we showed that LINC00265 interacted with and stabilized the transcriptional co-repressor Switch-independent 3a (SIN3A), which is a scaffold protein functioning either as a tumor repressor or as an oncogene in a context-dependent manner. Silencing of SIN3A also reduced proliferation of lung cancer cells, which was correlated with the induction of autophagy. These observations raise the possibility that LINC00265 functions to promote the oncogenic activity of SIN3A in lung adenocarcinoma. Conclusions: Our findings thus identify SIN3A as a LINC00265-associated protein and should help to understand the mechanism underlying LINC00265-mediated oncogenesis.

Single cell transcriptomic analysis reveals tumor immune infiltration by NK cells gene signature in lung adenocarcinoma.

Background: Natural Killer (NK) cells are vital components of the innate immune system, crucial for combating infections and tumor growth, making them pivotal in cancer prognosis and immunotherapy. We sought to understand the diverse characteristics of NK cells within lung adenocarcinoma (LUAD) by conducting single-cell RNA sequencing analyses. Methods: Using the scRNA-seq dataset for multiple primary lung cancers (MPLCs), we examined two major NK cell groups, NK1 and NK2, comparing the expression profiles of 422 differentially expressed NK signature genes. We identified eight genes (SPON2, PLEKHG3, CAMK2N1, RAB27B, CTBP2, EFHD2, GOLM1, and PLOD1) that distinguish NK1 from NK2 cells. A prognostic signature, the NK gene signature (NKGS) score, was established through LASSO Cox regression. High NKGS scores were linked to poorer overall survival in TCGA-LUAD patients and consistently validated in other datasets (GSE31210 and GSE14814). Results: Functional analysis revealed an enrichment of genes related to the TGF-ß signaling pathway in the high NKGS score group. Moreover, a high NKGS score correlated with an immunosuppressive tumor microenvironment (TME) driven by immune evasion mechanisms. We also observed reduced T-cell receptor (TCR) repertoire diversity in the high-risk NKGS group, indicating a negative association between inflammation and risk score. Conclusion: This study introduced the innovative NKGS score, differentiating NK1 from NK2 cells. High NKGS scores were associated with the TGF-ß pathway and provided insights into LUAD prognosis and immune activities.

Knockdown of GNL3 inhibits LUAD cell growth by regulating Wnt-ß-catenin pathway.

BACKGROUND: Lung adenocarcinoma (LUAD) is a leading cause of tumor-associated mortality, and it is needed to find new target to combat this disease. Guanine nucleotide-binding -protein-like 3 (GNL3) mediates cell proliferation and apoptosis in several cancers, but its role in LUAD remains unclear. OBJECTIVE: To explore the expression and function of Guanine nucleotide-binding protein-like 3 (GNL3) in lung adenocarcinoma (LUAD) and its potential mechanism in inhibiting the growth of LUAD cells. METHODS: We evaluated the expression of GNL3 in LUAD tissues and its association with patient prognosis using databases and immunohistochemistry. Cell proliferation was assessed by CCK-8 assay as well as colony formation, while apoptosis was evaluated by FCM. The effect of GNL3 knockdown on the Wnt/ß-catenin axis was investigated by Immunoblot analysis. RESULTS: GNL3 is overexpressed in LUAD tissues and is correlated with poor prognosis. Knockdown of GNL3 significantly inhibited the growth as well as induced apoptosis in A549 as well as H1299 cells. Furthermore, we found that the inhibitory effect of GNL3 knockdown on LUAD cell growth is associated with the downregulation of the Wnt/ß-catenin axis. CONCLUSION: GNL3 is key in the progression of LUAD by metiating Wnt/ß-catenin axis. Targeting GNL3 may represent a novel therapeutic method for LUAD treatment.

Peptidyl-prolyl isomerase F as a prognostic biomarker associated with immune infiltrates and mitophagy in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is among the most prevalent malignancies worldwide, with unfavorable treatment outcomes. Peptidyl-prolyl isomerase F (PPIF) is known to influence the malignancy traits of tumor progression by modulating the bioenergetics and mitochondrial permeability in cancer cells; however, its role in LUAD remains unclear. Our study seeks to investigate the clinical significance, tumor proliferation, and immune regulatory functions of PPIF in LUAD. Methods: The expression of PPIF in LUAD tissues and cells was assessed using bioinformatics analysis, immunohistochemistry (IHC), and Western blotting. Survival curve analysis was conducted to examine the prognostic association between PPIF expression and LUAD. The immunomodulatory role of PPIF in LUAD was assessed through the analysis of PPIF expression and immune cell infiltration. A series of gain- and loss-of-function experiments were conducted on PPIF to investigate its biological functions in LUAD both in vitro and in vivo. The mechanisms underlying PPIF's effects on LUAD were delineated through functional enrichment analysis and Western blotting assays. Results: PPIF exhibited overexpression in LUAD tissues compared to normal controls. Survival curve analysis revealed that patients with LUAD exhibiting higher PPIF expression demonstrated decreased overall survival and a shorter progression-free interval. PPIF was implicated in modulating immune cell infiltration, particularly in regulating the T helper 1-T helper 2 cell balance. Functionally, PPIF was discovered to promote tumor cell proliferation and advance cell-cycle progression. Furthermore, PPIF could impede mitophagy by targeting the FOXO3a/PINK1-Parkin signaling pathway. Conclusions: The findings of this study indicate that the prognosis-related gene PPIF may have a significant role in the regulation of LUAD cell proliferation, tumor-associated immune cell infiltration, and mitophagy, and thus PPIF may be a promising therapeutic target of LUAD.

PSME3 promotes lung adenocarcinoma development by regulating the TGF-ß/SMAD signaling pathway.

Background: Lung adenocarcinoma (LUAD) is one of the most common types of cancer worldwide. Proteasome activator subunit 3 (PSME3) is a subunit of a proteasome activator, and changes in PSME3 can lead to the development of many diseases in organisms. However, the specific mechanism of PSME3 in LUAD has not yet been elucidated. This study initially revealed the mechanism of PSME3 promoting the progression of lung adenocarcinoma, which provided a potential molecular target for clinical treatment. Methods: PSME3 expression in LUAD cells and tissues was assessed by bioinformatics analysis, immunohistochemistry (IHC), Western blotting (WB), and quantitative real time polymerase chain reaction (qRT-PCR). A series of functional experiments were used to evaluate the effects of PSME3 knockdown and overexpression on LUAD cell proliferation, migration, and apoptosis. The potential mechanism of PSME3 was explored by transcriptome sequencing and WB experiments. Results: In this study, our initial findings indicated that PSME3 expression was abnormally high in LUAD and was associated with poor patient prognosis. Further, we found that the downregulation of PSME3 significantly inhibited LUAD cell proliferation, an effect that was verified by subcutaneous tumor formation experiments in nude mice. Similarly, the rate of invasion and migration of LUAD cells significantly decreased after the downregulation of PSME3. Using flow cytometry, we found that the knockdown of PSME3 caused cell cycle arrest at the G1/S phase. Through transcriptome sequencing, we found that the transforming growth factor-beta (TGF-ß)/SMAD signaling pathway was closely related to LUAD, and we then validated the pathway using WB assays. Conclusions: We demonstrated that PSME3 was abnormally highly expressed in LUAD and related to poor patient prognosis; therefore, targeting PSME3 in the treatment of LUAD may represent a novel therapeutic approach.

Bioinformatics analysis of an immunotherapy responsiveness-related gene signature in predicting lung adenocarcinoma prognosis.

Background: Immune therapy has become first-line treatment option for patients with lung cancer, but some patients respond poorly to immune therapy, especially among patients with lung adenocarcinoma (LUAD). Novel tools are needed to screen potential responders to immune therapy in LUAD patients, to better predict the prognosis and guide clinical decision-making. Although many efforts have been made to predict the responsiveness of LUAD patients, the results were limited. During the era of immunotherapy, this study attempts to construct a novel prognostic model for LUAD by utilizing differentially expressed genes (DEGs) among patients with differential immune therapy responses. Methods: Transcriptome data of 598 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) database, which included 539 tumor samples and 59 normal control samples, with a mean follow-up time of 29.69 months (63.1% of patients remained alive by the end of follow-up). Other data sources including three datasets from the Gene Expression Omnibus (GEO) database were analyzed, and the DEGs between immunotherapy responders and nonresponders were identified and screened. Univariate Cox regression analysis was applied with the TCGA cohort as the training set and GSE72094 cohort as the validation set, and least absolute shrinkage and selection operator (LASSO) Cox regression were applied in the prognostic-related genes which fulfilled the filter criteria to establish a prognostic formula, which was then tested with time-dependent receiver operating characteristic (ROC) analysis. Enriched pathways of the prognostic-related genes were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and tumor immune microenvironment (TIME), tumor mutational burden, and drug sensitivity tests were completed with appropriate packages in R (The R Foundation of Statistical Computing). Finally, a nomogram incorporating the prognostic formula was established. Results: A total of 1,636 DEGs were identified, 1,163 prognostic-related DEGs were extracted, and 34 DEGs were selected and incorporated into the immunotherapy responsiveness-related risk score (IRRS) formula. The IRRS formula had good performance in predicting the overall prognoses in patients with LUAD and had excellent performance in prognosis prediction in all LUAD subgroups. Moreover, the IRRS formula could predict anticancer drug sensitivity and immunotherapy responsiveness in patients with LUAD. Mechanistically, immune microenvironments varied profoundly between the two IRRS groups; the most significantly varied pathway between the high-IRRS and low-IRRS groups was ribonucleoprotein complex biogenesis, which correlated closely with the TP53 and TTN mutation burdens. In addition, we established a nomogram incorporating the IRRS, age, sex, clinical stage, T-stage, N-stage, and M-stage as predictors that could predict the prognoses of 1-year, 3-year, and 5-year survival in patients with LUAD, with an area under curve (AUC) of 0.718, 0.702, and 0.68, respectively. Conclusions: The model we established in the present study could predict the prognosis of LUAD patients, help to identify patients with good responses to anticancer drugs and immunotherapy, and serve as a valuable tool to guide clinical decision-making.

Correlation analysis between driver gene mutation and clinicopathological features in lung adenocarcinoma based on real-world cumulative clinical data.

Background: Driver genes are essential predictors of targeted therapeutic efficacy. Detecting driver gene mutations in lung adenocarcinoma (LUAD) patients can help to screen for targeted drugs and improve patient survival benefits. This study aims to investigate the mutation characterization of driver genes and their correlation with clinicopathological features in LUAD. Methods: A total of 440 LUAD patients were selected from Sir Run Run Shaw Hospital between July 2019 and September 2022. Postoperative tissue specimens were analyzed for gene mutations using next-generation sequencing technology, focusing, including epidermal growth factor receptor EGFR, ALK, ROS1, RET, KRAS, MET, BRAF, HER2, PIK3CA and NRAS. At the same time, clinicopathological data were collected and organized for multidimensional correlation analysis. Results: Of 440 LUAD patients, driver gene mutations were not detected in 48 patients. The proportion of patients with driver gene mutations was as high as 89.09%. The top three driver genetic mutations were EGFR, KRAS, and MET. Sixty-nine types of EGFR mutations were detected and distributed in the protein tyrosine kinase catalytic domain (56, 81.16%), Furin-like cysteine-rich region (9, 13.04%), receptor binding domain (3, 4.35%), and EGFR transmembrane domain (1, 1.45%). Single gene locus mutation occurred in 343 LUAD patients, but the mutation gene types covered all tested genes. Our findings showed that EGFR mutations were more commonly observed in non-smoking and female patients (P<0.01), KRAS mutations were more prevalent in male patients and smokers (P<0.01), ROS1 mutations had larger tumor diameters (P<0.01) and RET mutations were more prevalent in smokers (P<0.05). Conclusions: LUAD patients exhibit diverse genetic mutations, which may co-occur simultaneously. Integrated analysis of multiple mutations is essential for accurate diagnosis and effective treatment of the disease. The use of NGS can significantly expand our understanding of gene mutations and facilitate integrated analysis of multiple gene mutations, providing critical evidence for targeted treatment methods.