Prognosis Of Lung Adenocarcinoma Research Articles

Role and mechanism of LINC02390 and its potential target genes, CLECL1 and CD69, in immune microenvironment of lung adenocarcinoma.

Targeted therapy and immunotherapy has brought new hope to patients with lung adenocarcinoma (LUAD) with their applications. However, the prognosis of LUAD patients is still unpromising. It is particularly important to find the biomarkers that can predict the prognosis of LUAD. In our previous study, we found that patients with high expression of LINC02390 had a better prognosis. The clinical significance of LINC02390 and its potential target genes, CLECL1 and CD69, in the prognosis of LUAD and its role in the immune microenvironment were explored. Through the survival analysis, LINC02390 and its potential target genes, CLECL1 and CD69, were identified as good prognostic factors for LUAD. According to GO and KEGG analyses, LINC02390-related genes were identified potentially involved in immune-related signaling pathways. Gene mutations and their relationship with immune cell infiltration were verified through the online cbioportal and TIMER database. CD69 was found to positively associate with CD8 + T cells and CLECL1 was also positively associated with CD4 + T cells. A high expression of CD69 in CD8 + T cells was identified through the single-cell sequencing dataset GSE111894. Finally, CLECL1 and CD69 were lowly expressed in clinical tissue samples with LUAD by immunohistochemical staining. LINC02390 and its possible target genes, CLECL1 and CD69, may be potential targets for the immunotherapy in LUAD patients.

Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism

ObjectiveThis study aimed to predict the level of stemness index (mRNAsi) and survival prognosis of lung adenocarcinoma (LUAD) using pathomics model. MethodsFrom The Cancer Genome Atlas (TCGA) database, 327 LUAD patients were randomly assigned to a training set (n = 229) and a validation set (n = 98) for pathomics model development and evaluation. PyRadiomics was used to extract pathomics features, followed by feature selection using the mRMR-RFE algorithm. In the training set, Gradient Boosting Machine (GBM) was utilized to establish a model for predicting mRNAsi in LUAD. The model's predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Prognostic analysis was conducted using Kaplan-Meier curves and cox regression. Additionally, gene enrichment analysis, tumor microenvironment analysis, and tumor mutational burden (TMB) analysis were performed to explore the biological mechanisms underlying the pathomics prediction model. ResultsMultivariable cox analysis (HR = 1.488, 95 % CI 1.012–2.187, P = 0.043) identified mRNAsi as a prognostic risk factor for LUAD. A total of 465 pathomics features were extracted from TCGA-LUAD histopathological images, and ultimately, the most representative 8 features were selected to construct the predictive model. ROC curves demonstrated the significant predictive value of the model for mRNAsi in both the training set (AUC = 0.769) and the validation set (AUC = 0.757). Calibration curves and Hosmer-Lemeshow goodness-of-fit test showed good consistency between the model's prediction of mRNAsi levels and the actual values. DCA indicated a good net benefit of the model. The prediction of mRNAsi levels by the pathomics model is represented using the pathomics score (PS). PS was strongly associated with the prognosis of LUAD (HR = 1.496, 95 % CI 1.008–2.222, P = 0.046). Signaling pathways related to DNA replication and damage repair were significantly enriched in the high PS group. Prediction of immune therapy response indicated significantly reduced Dysfunction in the high PS group (P < 0.001). The high PS group exhibited higher TMB values (P < 0.001). ConclusionsThe predictive model constructed based on pathomics features can forecast the mRNAsi and survival risk of LUAD. This model holds promise to aid clinical practitioners in identifying high-risk patients and devising more optimized treatment plans for patients by jointly employing therapeutic strategies targeting cancer stem cells (CSCs).

Exploring the function and prognostic value of RPLP0, RPLP1 and RPLP2 expression in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and is characterized by its heterogeneity and poor prognosis. The role of ribosomal proteins RPLP0, RPLP1 and RPLP2 in multiple cancers has been implicated. However, their function in LUAD and their correlation with the poor prognosis of LUAD remains elusive. In this study, we performed a comprehensive bioinformatic analysis of the impact of these ribosomal proteins on LUAD. Our findings reveal that RPLP0, RPLP1 and RPLP2 are overexpressed in LUAD, which are likely attributed to abnormal copy number variations and decreased methylation levels of their promoters. LUAD patients with high expression of RPLP0, RPLP1 or RPLP2 have worse clinical outcomes in terms of overall survival (OS), first progression (FP) and post-progression survival (PPS), indicating poor prognosis. Moreover, the expression of RPLP0, RPLP1 and RPLP2 affects immune cell infiltration in LUAD tissues. Finally, we identified multiple existing drugs that may inhibit the expression of RPLP1 and RPLP2. Collectively, our data implicate the oncogenic role of RPLP0, RPLP1 and RPLP2 in LUAD and underscore their prognostic value in LUAD patients.

RPL22L1 is a novel biomarker for prognosis and immune infiltration in lung adenocarcinoma, promoting the growth and metastasis of LUAD cells by inhibiting the MDM2/P53 signaling pathway.

The ribosomal protein L22-like1 (RPL22L1) is a constituent of the 60 S ribosomal subunit whose function in lung adenocarcinoma (LUAD) remains ambiguous. This study aims to elucidate the role of RPL22L1 in LUAD through a thorough analysis and experimental validation. Our findings indicate that RPL22L1 exhibits abnormal expression patterns in various cancer types, including LUAD. Moreover, a statistically significant association was observed between elevated levels of RPL22L1 expression in LUAD patients and several clinical parameters, such as pathological stage (p = 0.0083) and gender (p = 0.0038). The high expression of RPL22L1 in LUAD demonstrated a significant association with poorer overall survival (OS) (p = 0.005), progression-free survival (PFS) (p = 0.027), and disease-specific survival (p = 0.015). The expression of RPL22L1 in LUAD (p = 0.005) was identified as an independent prognostic factor. Additionally, RPL22L1 expression in LUAD was found to be correlated with immune infiltration, immune checkpoint genes, TMB/MSI, and mRNAsi. Notably, the expression of RPL22L1 exhibited significant negative correlations with 1-BET-762, Trametinib, and WZ3105 in LUAD. The RPL22L1 gene exhibited up-regulation in multiple individual cells of LUAD, leading to a comparatively shorter PFS in the RPL22L1 variant group as opposed to the RPL22L1 variant-free group in LUAD. Significantly increased expression of RPL22L1 was noted in LUAD cell lines, where it was found to enhance the growth and metastasis of LUAD cells by suppressing the MDM2/P53 signaling pathway. Therefore, RPL22L1 may serve as a promising prognostic biomarker and therapeutic target for patients with LUAD.

Comprehensive Analysis and Experimental Validation of TLL2 as a Potential New Prognostic Biomarker Associated with Immune Infiltration in Lung Adenocarcinoma.

The precise function of Tolloid Like 2 (TLL2) remains uncertain within the context of Lung Adenocarcinoma (LUAD). The primary objective of this investigation was to conduct a thorough analysis. To assess its diagnostic utility, data from The Cancer Genome Atlas (TCGA) database were used to assess TLL2 expression in pan-cancer and LUAD. The study has also investigated the correlation between TLL2 expression levels and LUAD symptoms and prognosis. Furthermore, the study has explored possible regulatory networks involving TLL2, including its association with immune infiltration, tumor stemness index (mRNAsi), and drug sensitivity in LUAD. We have explored TLL2 expression in single-cell sequencing of LUAD and the genomic variation and clinical significance of TLL2 in LUAD. The expression of TLL2 has been validated in GSE87340 and cell lines by quantitative Real-time PCR (qRT-PCR). An abnormal expression of TLL2 has been found in pan-cancer and LUAD. In LUAD patients, elevated levels of TLL2 were significantly related to the T stage (p = 0.046) and the pathological stage (p = 0.016). The expression of TLL2 in patients with LUAD was significantly associated with poorer Overall Survival (OS) (p < 0.001). The expression of TLL2 was determined to be an independent predictor of poorer OS (p = 0.042). TLL2 was associated with ribosome, neuroactive ligand-receptor interaction, allograft rejection, ECM receptor interaction, asthma, porphyrin and chlorophyll metabolism, focal adhesion, pentose and glucuronate inter-conversions, and ascorbate and aldarate metabolism. The expression of TLL2 in LUAD was correlated with immune infiltration and mRNAsi. The expression of TLL2 was significantly and negatively correlated with TAK-715, XMD13-2, STF-62247, OSI-930, and EHT-1864 in LUAD. The TLL2 gene was up- -regulated in multiple individual LUAD cells. LUAD patients with altered TLL2 had a shorter PFS as opposed to those with unaltered TLL2. The expression of TLL2 was significantly increased in LUAD cells. For patients with LUAD, TLL2 may serve as an immunotherapeutic target and a useful prognosis biomarker.

PLCG2, A Regulator of Lung Adenocarcinoma Proliferation and Migration Associated with Immune Infiltration.

Results from the TCGA database showed that phosphatidylinositol-specific phospholipase Cγ2 (PLCG2) expression level in Lung Adenocarcinoma (LUAD) was notably decreased compared to adjacent tissues, so we unveiled its role of LUAD. This study aims to explore the expression and clinical significance of Phosphatidyl-inositol-specific phospholipase Cγ2 (PLCG2) in lung adenocarcinoma (LUAD) cells and its role in cell proliferation and metastasis. Differential PLCG2 mRNA and protein levels between LUAD tissues and adjacent tissues were analyzed from the TCGA database, TIMER, and UALCAN database. Differentially expressed genes were screened for patients in the high and low PLCG2 mRNA expression groups by the R package as well as GSEA. The expression level of PLCG2 in LUAD cells was detected using qRT-PCR and CCK8, clone formation, Transwell, and Western blot assays. PLCG2 was lowly expressed in LUAD and did not significantly correlate with the prognosis of LUAD. PLCG2 expression levels varied significantly in terms of patients' gender, age, T, N, and pathological stage. GO/KEGG enrichment analysis showed that co-expression of PLCG2 was mainly associated with the immune response- regulating cell-surface receptors, and so on. GSEA analysis showed enrichment pathways of PLCG2-related differential gees were primarily associated with the olfactory transduction pathway, ribosome, etc. R software analysis revealed a significant correlation between PLCG2 expression and six types of immune-infiltrat-ing cells, positively correlated with immune checkpoint-related genes and negatively regulated by tumor mutational load. Overexpressing PLCG2 showed reduced LUAD cell proliferation, clone formation, cell migration and invasion, and epithelial-mesenchymal transition-associated proteins, compared with the control group. PLCG2 is lowly expressed in LUAD tissues and is involved in immune infiltration of LUAD, inhibiting LUAD cell proliferation and metastasis.

Combination of transcriptome and Mendelian inheritance reveals novel prognostic biomarker of CTLA-4-related lncRNAs and protective role of nitrogen metabolism pathway in lung adenocarcinoma development.

Since in the cancer setting, tumor cells may use cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) to evade the immune system. This study aimed to identify CTLA-4-related long non-coding RNAs (lncRNAs) and assess their roles in lung adenocarcinoma (LUAD) development. Clinical and genomic data were obtained from The Cancer Genome Atlas (TCGA), MSigDB and Gene Weaver. CTLA-4-related lncRNA-based gene signatures (CTLA4LncSigs) were identified using Cox regression, establishing a risk score model and an independent prognostic model. Enrichment analysis (GO/KEGG) was performed. Mendelian randomization (MR) analysis investigated the nitrogen metabolism and lung cancer relationship, with Bayesian weighted MR (BWMR) addressing uncertainties. Correlations with tumor microenvironment and drug sensitivity were explored. Nineteen CTLA4LncSigs significantly influenced LUAD prognosis. The risk score demonstrated independence as a prognostic factor. Functional analysis revealed lncRNAs' impact on nitrogen metabolism. MR and BWMR confirmed the protective role of the nitrogen metabolism pathway in lung cancer. Our study identifies CTLA-4-related lncRNAs associated with LUAD prognosis and uncovers a previously undiscovered protective role of the nitrogen metabolism pathway in combating LUAD development, providing new insights into potential therapeutic targets and prognostic biomarkers for this aggressive cancer subtype.

SGPP2 is activated by SP1 and promotes lung adenocarcinoma progression.

The late diagnosis and easy metastasis of lung adenocarcinoma (LADC) remains a challenge. SGPP2 is reported to modulate cell processes in many cancers. However, the roles and molecular mechanisms of SGPP2 in LADC are unclear. Online bioinformatics tools GEPIA, CPTAC, and K-M plotter were used to analyze the expression of SGPP2 and the prognosis in LADC. JASPAR and PROMO were used to predict the transcription factors of SGPP2. Real-time quantitative reverse transcription PCR and western blot were used to detect the levels of SGPP2 in LADC cell lines and tissues. Cell counting kit-8, colony formation, flow cytometry, and transwell assay were used to detect cell proliferation, apoptosis, and invasion. The anti-cancer effect of SGPP2 silence was evaluated in the LADC xenograft model. It was found that SGPP2 was highly expressed and related to the poor prognosis of LADC patients. Elevated SGPP2 expression was detected in LADC cell lines and tissues. The chi-square test indicated that the expression of SGPP2 was positively related to tumor, node, metastasis grades and lymph node metastasis. Knocking down SGPP2 significantly inhibited LADC cell viability, and invasion, but induced apoptosis. The anti-tumor effects of SGPP2 were verified in vivo. The upstream transcription factor of SGPP2 was predicted to be SP1, which was highly expressed in LADC tissues and cell lines. Overexpression of SP1 partly rescued the inhibition of SGPP2-shRNA in cell growth, colony formation, and invasion capabilities, and decreased apoptotic cell number in LADC cells. This study demonstrated that SGPP2, activated by SP1, promotes LADC cell proliferation and invasion, and suppresses apoptosis in LADC.

DNA methylation and mRNA expression of ZNF577 as biomarkers for the detection and prognosis of lung adenocarcinoma.

Despite advances in science and technology, lung cancer remains a major public health issue. The discovery of early diagnostic and prognostic markers is still needed to reduce the mortality rate of lung cancer, which is the highest among all cancer types. Aberrations in the DNA methylation system have an important role in human cancer and are promising for the development of early diagnostic and prognostic markers. The present study focused on zinc finger protein (ZNF)577, whose encoding gene was indicated to exhibit promoter hypermethylation together with 9 other genes in lung adenocarcinoma (LADC) in a previous study by our group. ZN577 is a member of the ZNG family and its functional role has so far remained elusive. LADC tissue samples surgically resected at Tokushima University Hospital (Tokushima, Japan) between April 1999 and November 2013 were collected. A total of 73 tumors and 27 paired tumor-adjacent normal tissues were examined for DNA methylation and mRNA expression of ZNF577. A total of 149 LADC tissue samples were collected and evaluated by immunohistochemistry (IHC) for the tissue expression of ZNF577. High methylation (n=27, P<0.0001) and low mRNA expression levels (n=27, P<0.031) of ZNF577 were identified in LADC tissues, and it was demonstrated that methylation levels were inversely correlated with mRNA expression levels (P=0.0116, ρ=-0.2515). Among the LADC tissues, lepidic-patterned samples had lower methylation levels of ZNF577 than other pathological types. In addition, mRNA expression levels of ZNF577 were significantly higher in females, non-smokers and stage I samples. Overall survival [P<0.0001; area under curve (AUC)=0.8658] and disease-free survival (DFS; P<0.0004; AUC=0.7232) rates were significantly higher in the ZNF577 high mRNA expression group than in the ZNF577 low mRNA expression group. Among the 149 LADC samples examined by IHC, 105 were negative and 44 were positive for the tissue expression of ZNF577. Cox regression analysis showed poorer DFS (hazard ratio: 3.917; P=0.023) in patients with lower expression of ZNF577. In conclusion, higher methylation levels of ZNF577 were observed in LADC tissues than in normal lung tissue and low mRNA expression of ZNF577 was associated with unfavorable prognosis.

Prediction of lung adenocarcinoma prognosis and diagnosis with a novel model anchored in circadian clock-related genes

Lung adenocarcinoma is the most common primary lung cancer seen in the world, and identifying genetic markers is essential for predicting the prognosis of lung adenocarcinoma and improving treatment outcomes. It is well known that alterations in circadian rhythms are associated with a higher risk of cancer. Moreover, circadian rhythms play a regulatory role in the human body. Therefore, studying the changes in circadian rhythms in cancer patients is crucial for optimizing treatment. The gene expression data and clinical data were sourced from TCGA database, and we identified the circadian clock-related genes. We used the obtained TCGA-LUAD data set to build the model, and the other 647 lung adenocarcinoma patients’ data were collected from two GEO data sets for external verification. A risk score model for circadian clock-related genes was constructed, based on the identification of 8 genetically significant genes. Based on ROC analyses, the risk model demonstrated a high level of accuracy in predicting the overall survival times of lung adenocarcinoma patients in training folds, as well as external data sets. This study has successfully constructed a risk model for lung adenocarcinoma prognosis, utilizing circadian rhythm as its foundation. This model demonstrates a dependable capacity to forecast the outcome of the disease, which can further guide the relevant mechanism of lung adenocarcinoma and combine behavioral therapy with treatment to optimize treatment decision-making.

Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma

Tumor spread through air spaces (STAS) is a distinctive metastatic pattern affecting prognosis in lung adenocarcinoma (LUAD) patients. Several challenges are associated with STAS detection, including misdetection, low interobserver agreement, and lack of quantitative analysis. In this research, a total of 489 digital whole slide images (WSIs) were collected. The deep learning-based STAS detection model, named STASNet, was constructed to calculate semi-quantitative parameters associated with STAS density and distance. STASNet demonstrated an accuracy of 0.93 for STAS detection at the tiles level and had an AUC of 0.72–0.78 for determining the STAS status at the WSI level. Among the semi-quantitative parameters, T10S, combined with the spatial location information, significantly stratified stage I LUAD patients on disease-free survival. Additionally, STASNet was deployed into a real-time pathological diagnostic environment, which boosted the STAS detection rate and led to the identification of three easily misidentified types of occult STAS.

TONSL promotes lung adenocarcinoma progression, immune escape and drug sensitivity.

The tonsoku-like DNA repair protein (TONSL) encoded by the TONSL gene, located on chromosome 8q24.3, is crucial for repairing DNA double-strand breaks through homologous recombination. However, TONSL overexpression in lung adenocarcinoma (LUAD) promotes tumor development, leading to a poor prognosis. TONSL was verified as a reliable prognostic marker for LUAD using bioinformatics, and clinical features related to LUAD prognosis were screened from the TCGA database to establish the relationship between risk factors and TONSL expression. In addition, TONSL expression in normal and LUAD tissues was verified using real-time quantitative polymerase chain reaction and immunohistochemistry. To elucidate the possible functions of TONSL, TONSL-related differentially expressed genes were screened, and functional enrichment analysis was performed. Subsequently, siRNA was used to knock down TONSL expression in lung cancer cells for cytobehavioral experiments. The effects of TONSL expression on tumor immune escape were analyzed using the ESTIMATE algorithm and tumor immune-infiltration analysis. In addition, the half-maximal inhibitory concentration of LUAD with varying TONSL expression levels in response to first-line chemotherapeutic drugs and epidermal growth factor receptor-tyrosine kinase inhibitors was analyzed for drug sensitivity. Up-regulation of TONSL in LUAD promotes the proliferation, migration, and invasion of lung cancer cells, thereby contributing to a poor prognosis. Furthermore, TONSL overexpression promotes immune escape and drug sensitivity in LUAD. TONSL serves as a reliable prognostic marker for LUAD, and its up-regulation is associated with increased immune escape and drug sensitivity. These findings suggest that TONSL holds potential as a novel therapeutic target for LUAD.

Cuproptosis Related Gene DLD Associated with Poor Prognosis and Malignant Biological Characteristics in Lung Adenocarcinoma.

Cuproptosis plays a crucial role in the biological function of cells. The subject of this work was to analyze the effects of cuproptosis-related genes (CRGs) on the prognosis and biological function in lung adenocarcinoma (LUAD). In this study, RNA sequencing and clinical data of LUAD samples were screened from public databases and our institution. A CRG signature was identified by least absolute shrinkage and selection operator and Cox regression. In addition, this study analyzed the correlation between prognostic CRGs and clinicopathological features. Finally, this study studied the effect of inhibiting dihydrolipoamide dehydrogenase (DLD) expression on cell biological function. There were 10 CRGs that showed differential expression between LUAD and normal tissues (p<0.05). A prognostic signature (DLD and lipoyltransferase 1 [LIPT1]) was constructed. Survival analysis suggested that patients with LUAD in the high-risk group had shorter overall survival (OS) (p<0.05). High expression of DLD and low expression of LIPT1 were significantly associated with shorter OS (p<0.05). Immunohistochemical analysis revealed that, in LUAD tissues, DLD was highly expressed, whereas LIPT1 was not detected. Finally, inhibition of DLD expression could significantly restrain cell proliferation, invasion and migration. Overall, this prognostic CRG signature may play a pivotal role in LUAD outcome, while oncogene DLD may be a future therapeutic candidate for LUAD.

Association of R-loop binding proteins with prognosis and anti-tumor drug sensitivity in lung adenocarcinoma: a bioinfor-matic study.

To investigate the association of R-loop binding proteins with prognosis and chemotherapy efficacy in lung adenocarcinoma. The data related to R-loop regulatory genes were obtained from literature of R-loop proteomics and relevant databases. We used 403 cases of lung adenocarcinoma in the Cancer Genome Atlas as training set, and two datasets GSE14814 and GSE31210 in Gene Expression Omnibus as validation sets. The weighted gene co-expression network analysis (WGCNA) was employed to identify R-loop genes with a significant impact on the clinical phenotype of lung adenocarcinoma. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to eliminate genes exhibiting multicollinearity. A multivariate Cox regression analysis was employed to scrutinize clinical variables and R-loop characteristic genes that exert independent prognostic effects on patient survival. Subsequently, a risk score model was constructed. The predictive capacity of this model for the prognosis of patients was analyzed and validated. Additionally, the performance of risk model on the anti-tumor drug sensitivity was assessed. The mutations of R-loop genes were analyzed by maftools. The effect of PLEC expression on anti-tumor drug sensitivity was tested on non-small cell lung adenocarcinoma H1299 and A549 cells in vitro. A collection of 1551 R-loop genes were obtained, and 78 genes exhibited significant effects on the clinical phenotype shown on WGCNA. The LASSO regression analysis retained fourteen R-loop genes. A multivariate Cox regression analysis further identified three R-loop genes (HEXIM1, GLI2, PLEC) and a clinical variable (tumor grading) that were associated with patient prognosis. Risk prediction model was established according to the regression coefficients of each parameter. Kaplan-Meier survival analysis showed that the prognosis of high-risk group was significantly worse than that of low-risk group (P<0.01). The time-dependent ROC curve showed that the risk model had good predictive ability in both training and validation sets. Predictive analyses of anti-neoplastic drug sensitivity indicated a diminished responsiveness to both chemotherapy and targeted treatment drugs among high-risk patients. The expression of PLEC was strongly correlated with sensitivity to gefitinib, a classical EGFR inhibitor. R-loop binding proteins have been identified as significant determinants in the prognosis and therapeutic strategies for lung adenocarcinoma, which indicates that therapeutic interventions targeting these specific R-loop binding proteins might contribute to a better survival of the patients.

A methylation-related lncRNA-based prediction model in lung adenocarcinomas.

The collaboration between methylation and the lung adenocarcinoma (LUAD) occurrence and development is closes. Long noncoding RNA (lncRNA), as a regulatory factor of various biological functions, can be used for cancer diagnosis. Our study aimed to construct a robust methylation-related lncRNA signature of LUAD. In the Cancer Genome Atlas (TCGA) dataset, we download the RNA expression data and clinical information of LUAD cases. To develop the best prognostic signature based on methylation-related lncRNAs, Cox regression analyses were utilized. Using Kaplan-Meier analysis, overall survival rates were compared between risk category included both low- and high-risk patients. To categorize genes according to their functional significance, GSEA (Subramanian et al, 2005) was used. Single-sample gene set enrichment analysis (ssGSEA) was used to further reveal the potential molecular mechanism of the methylation-related lncRNA prognostic model in immune infiltration. Using TRLnc (http://www.licpathway.net/TRlnc) and lncRNASNP to analyse the SNP sites and TRLnc of these 18 lncRNAs. LncSEA website was used to analyse 18 lncRNA in the process of tumour development and development. Go was used to analyse the enriched pathways enriched by TFs (transcription factors), Cerna networks, and proteins bound to each other of these 18 lncRNAs. The 'prophetic' package was used to analyse the value of this prognostic model in guiding personalized immunotherapy. In this study, we identified 18 methylation-related lncRNAs (AP002761.1, AL118558.3, CH17-340M24.3, AL353150.1, AC004687.1, LINC00996, AF186192.1, HSPC324, AC087752.3, FAM30A, AC106047.1, AC026355.1, ABALON, LINC01843, AL606489.1, NKILA, AP001453.2, GSEC) to establish a methylation-related lncRNA signature that can detect patients prognosis in LUAD. The enriched pathways enriched by proteins interacting with 18 lncRNAs are mainly EMT, hypoxia, stemness and proliferation, among which LINC00996 and AF186192.1 are regulated by multiple tumour associated transcription factors, such as TP53 and TP63, and fam30a and mRNA form a Cerna network. There are 2319 SNP loci in LINC00996, 36 of which are risk SNP loci and 205 SNP loci in af186192.1; AF186192.1 affects 95 conserved miRNAs and 123 non-conserved miRNAs, promotes the binding of 149 pairs of miRNAs: lncRNAs and inhibits the binding of 95 pairs of miRNAs: lncRNAs. The ROC curve demonstrated that the established methylation-related lncRNA signature was more effective in predicting the prognosis of patients in LUAD than the clinicopathological parameters. Our research has confirmed that patients in the high-risk group which was separated by the risk score model based on methylation-related lncRNA had shorter OS. According to GSEA, the high-risk group had a predominantly tumour- and immune-related pathway enrichment. A significant association was shown by ssGSEA between predictive signature and immune status in LUAD patients. In addition, principal component analysis (PCA) demonstrated the prognostic and predictive value of our signature. The correlation between the predictive signature of methylation-related lncRNA and IC50 of conventional chemotherapy drugs can provide personalized chemotherapy regimens for LUAD patients. Methylation-related lncRNA signature can effectively predict DFS of patients in LUAD.

Construction of lncRNA prognostic model related to disulfidptosis in lung adenocarcinoma

BackgroundLung 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. MethodsLUAD 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. ResultsThe 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. ConclusionSix 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.

Identification of an Exosome-relevant SNHG6-hsa-miR-429- CHRDL1/CCNA2 Axis for Lung Adenocarcinoma Prognosis Evaluation.

To explore an exosome-relevant molecular classification in lung adenocarcinoma (LUAD). Exosome genes or relevant non-coding RNAs are regulators of cancer treatment and prognosis, but their function in LUAD has not yet been determined. Unraveling a molecular classification applying exosome-related RNA networks for LUAD prognosis evaluation. MicroRNA sequencing data (miRNAs-seq) and RNA sequencing data (RNA- seq) were derived from The Cancer Genome Atlas (TCGA). The ConsensusCluster- Plus package was used for molecular typing in LUAD based on 121 Exosome-related genes. Then, a limma package was conducted to explore differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs) and differentially expressed lncRNAs (DElncRNAs) in molecular typing for constructing an Exosome-driven competing endogenous RNA network (ceRNA). Dominant miRNAs, as well as target mRNAs, were identified by COX modeling and Kaplan-Meier survival analysis. Two Exosome-associated molecular clusters classified in LUAD. The C2 cluster favored high clinicopathology and showed a trend toward poor prognosis. 29 lncRNA- miRNA and 12 miRNA-mRNA interaction pairs were identified. The hsa-miR-429 was the pivotal miRNA in the network that affected the prognosis of LUAD. According to the interaction relationship and LUAD prognostic role, SNHG6-hsa- miR-429-CHRDL1/CCNA2 was identified. SNHG6-hsa-miR-429-CHRDL1 exerts oncogenic effects, and SNHG6-hsa-miR-429- CCNA2 exerts pro-oncogenic effects. Overall, our study identified an Exosome-driven ceRNA network in LUAD, and the SNHG6-hsa-miR-429-CHRDL1/CCNA2 axis could be a new therapeutic target for LUAD and our study provides new insights into the molecular mechanisms of LUAD.

A Six-gene Prognostic Model Based on Neutrophil Extracellular Traps (NETs)-related Gene Signature for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant cancers. Neutrophil extracellular traps (NETs) have been discovered to play a crucial role in the pathogenesis of LUAD. We aimed to establish an innovative prognostic model for LUAD based on the distinct expression patterns of NETs-related genes. The TCGA LUAD dataset was utilized as the training set, while GSE31210, GSE37745, and GSE50081 were undertaken as the verification sets. The patients were grouped into clusters based on the expression signature of NETs-related genes. Differentially expressed genes between clusters were identified through the utilization of the random forest and LASSO algorithms. The NETs score model for LUAD prognosis was developed by multiplying the expression levels of specific genes with their corresponding LASSO coefficients and then summing them. The validity of the model was confirmed by analysis of the survival curves and ROC curves. Additionally, immune infiltration, GSEA, mutation analysis, and drug analysis were conducted. Silencing ABCC2 in A549 cells was achieved to investigate its effect. We identified six novel NETs-related genes, namely UPK1B, SFTA3, GGTLC1, SCGB3A1, ABCC2, and NTS, and developed a NETs score signature, which exhibited a significant correlation with the clinicopathological and immune traits of the LUAD patients. High-risk patients showed inhibition of immune-related processes. Mutation patterns exhibited variability among the different groups. AZD3759, lapatinib, and dasatinib have been identified as potential candidates for LUAD treatment. Moreover, the downregulation of ABCC2 resulted in the induction of apoptosis and suppression of migration and invasion in A549 cells. Altogether, this study has identified a novel NET-score signature based on six novel NET-related genes to predict the prognosis of LUAD and ABCC2 and has also explored a new method for personalized chemo-/immuno-therapy of LUAD.

XRCC2 driven homologous recombination subtypes and therapeutic targeting in lung adenocarcinoma metastasis

Lung adenocarcinoma (LUAD) is a leading cause of cancer mortality, with many patients facing poor prognosis, particularly those with metastatic or drug-resistant tumors. Homologous recombination genes (HRGs) are crucial in tumor progression and therapy resistance, but their clinical significance in LUAD is not well understood. In this study, we systematically characterize key HRGs in LUAD patients, identifying two distinct HR subtypes associated with different outcomes and biological functions. We establish a 5-gene scoring system (XRCC2, RAD51, BRCA1, FANCA, and CHEK1) that reliably predicts patient outcomes and immunotherapy responses in LUAD. Bioinformatics analysis and clinical validation highlight XRCC2 as a crucial biomarker in LUAD. Functional investigations through in vivo and in vitro experiments reveal the role of XRCC2 in promoting lung cancer migration and invasion. Mechanistically, XRCC2 stabilizes vimentin (VIM) protein expression through deubiquitylation. We predict c-MYC as a potential regulator of XRCC2 and demonstrate that inhibiting c-MYC with compound 10058-F4 reduces XRCC2 and VIM expression. Preclinical studies show the synergistic inhibition of metastasis in vivo when combining 10058-F4 with doxorubicin (Dox). Our findings present a potential personalized predictive tool for LUAD prognosis, identifying XRCC2 as a critical biomarker. The c-Myc-XRCC2-VIM axis emerges as a promising therapeutic target for overcoming lung metastasis. This study provides valuable insights into LUAD, proposing a prognostic tool for further clinical validation and unveiling a potential therapeutic strategy for combating lung metastasis by targeting c-Myc-XRCC2-VIM.

Construction of a mitophagy-related prognostic signature for predicting prognosis and tumor microenvironment in lung adenocarcinoma

BackgroundMitophagy is the selective degradation of mitochondria by autophagy. It becomes increasingly clear that mitophagy pathways are important for cancer cells to adapt to their high-energy needs. However, which genes associated with mitophagy could be used to prognosis cancer is unknown. MethodsWe created a clinical prognostic model using mitophagy-related genes (MRGs) in lung adenocarcinoma (LUAD) patients for the first time, and we employed bioinformatics methods to search for biomarkers that affect the progression and prognosis of LUAD. Transcriptome data for LUAD were obtained from The Cancer Genome Atlas (TCGA) database, and additional expression data from LUAD patients were sourced from the Gene Expression Omnibus (GEO) database. Furthermore, 25 complete MRGs were identified based on annotations from the MSigDB database. ResultsA comparison of the mitophagy scores between the groups with high and low scores was done using receiver operating characteristic (ROC) curves, which also revealed the differential gene expression patterns between the two groups. Using Kaplan-Meier analysis, two prognostic MRGs from the groups with high and low mitophagy scores were identified: TOMM40 and VDAC1. Using univariate and multivariate Cox regression, the relationship between the expression levels of these two genes and prognostic clinical features of LUAD was examined further.The prognosis of LUAD patients was shown to be significantly correlated (P < 0.05) with the expression levels of these two genes. ConclusionsOur prognostic model would improve the prognosis of LUAD and guide clinical treatments.