Biomarker For Lung Adenocarcinoma Research Articles

E2F7 serves as a potential prognostic biomarker for lung adenocarcinoma.

E2F transcription factors (E2Fs) are a family of transcription factors critical regulators of the cell cycle, apoptosis, and differentiation, thus influencing tumorigenesis. However, the specific roles of E2Fs in lung adenocarcinoma (LUAD) remain unclear. Data from The Cancer Genome Atlas (TCGA) were used. R version. 4.0.3 and multiple databases (TIMER, cBioportal, gene expression profile interaction analysis [GEPIA], LinkedOmics, and CancerSEA) were utilized to investigate mRNA expression, mutational analysis, prognosis, clinical correlations, co-expressed gene, pathway and network, and single-cell analyses. Immunohistochemistry (IHC) confirmed that E2F transcription factor 7 (E2F7) correlated with LUAD. Among the E2Fs, E2F7 was identified by constructing a prognostic model most significantly associated with overall survival (OS) in LUAD patients. The univariate and multivariate Cox regression analyses showed that E2F7, p-T stage, and p-TNM stage were closely related to OS and progression-free survival (PFS) (P < .05) in LUAD. E2F 7/8 were also identified as significantly associated with tumor stage in the GEPIA database. Compared with paracancerous tissues, E2F7 was up-regulated in LUAD by IHC, and E2F7 might be positively correlated with larger tumors and higher TNM stages. E2F7 may primarily regulate DNA repair, damage, and cell cycle processes and thus affect LUAD tumorigenesis, invasion, and metastasis by LinkedOmics and CancerSEA. E2F7 serves as a potential prognostic biomarker for LUAD.

Comprehensive analysis the prognostic and immune characteristics of mitochondrial transport-related gene SFXN1 in lung adenocarcinoma

BackgroundMitochondria, which serve as the fundamental organelle for cellular energy and metabolism, are closely linked to the growth and survival of cancer cells. This study aims to identify and assess Sideroflexin1 (SFXN1), an unprecedented mitochondrial gene, as a potential prognostic biomarker for lung adenocarcinoma (LUAD).MethodsThe mRNA and protein levels of SFXN1 were investigated based on the Cancer Genome Atlas (TCGA) LUAD dataset, and then validated by real-time quantitative PCR, Western Blotting and immunohistochemistry from our clinical samples. The clinical correlation and prognostic value were evaluated by the TCGA cohort and verified via our clinical dataset (n = 90). The somatic mutation, drug sensitivity data, immune cell infiltration and single-cell RNA sequencing data of SFXN1 were analyzed through public databases.ResultsSFXN1 was markedly upregulated at both mRNA and protein levels in LUAD, and high expression of SFXN1 were correlated with larger tumor size, positive lymph node metastasis, and advanced clinical stage. Furthermore, SFXN1 upregulation was significantly associated with poor clinical prognosis. SFXN1 co-expressed genes were also analyzed, which were mainly involved in the cell cycle, central carbon metabolism, DNA repair, and the HIF-1α signaling pathway. Additionally, SFXN1 expression correlated with the expression of multiple immunomodulators, which act to regulate the tumor immune microenvironment. Results also demonstrated an association between SFXN1 expression and increased immune cell infiltration, such as activated CD8 + T cells, natural killer cells (NKs), activated dendritic cells (DCs), and macrophages. LUAD patients with high SFXN1 expression exhibited heightened sensitivity to multiple chemotherapies and targeted drugs and predicted a poor response to immunotherapy. SFXN1 represented an independent prognostic marker for LUAD patients with an improved prognostic value for overall survival when combined with clinical stage information.ConclusionsSFXN1 is frequently upregulated in LUAD and has a significant impact on the tumor immune environment. Our study uncovers the potential of SFXN1 as a prognostic biomarker and as a novel target for intervention in LUAD.

Identification of Biomarker in Lung Adenocarcinoma Using Machine Learning and Neural Network

Identification of Biomarker in Lung Adenocarcinoma Using Machine Learning and Neural Network

Immunohistochemical analysis of epidermal growth factor receptor as a predictive biomarker in lung adenocarcinoma – A cross-sectional study of 33 cases on computed tomography-guided core needle biopsy specimens

Background: Lung cancer is the second most common cause of cancer in all ages and both genders and is the leading cause of cancer death. Two major groups of lung cancers are – small cell lung carcinoma (SCLC) and non-SCLC (NSCLC). Due to discovery of driver mutation such as epidermal growth factor receptor (EGFR) mutation in NSCLC, the paradigm of lung cancer therapy has shifted from cytotoxic platinum based therapy to tyrosine kinase inhibitor (TKI) therapy. In this study, we have analyzed EGFR expression in NSCLC by immunohistochemistry (IHC) and studied the response to TKI therapy in terms of progression free survival (PFS) and overall survival (OS). Aims and Objectives: In this study, we have retrospectively analyzed the EGFR overexpression in adenocarcinoma of lung in core needle biopsy (CNB) specimen by IHC analysis and correlated with the therapeutic response and survival rate (OS and PFS) in lung carcinoma patients. Materials and Methods: We have analyzed retrospectively EGFR expression in lung adenocarcinoma cases (computed tomography-guided CNB specimen) by IHC. In our study, we have used monoclonal primary antibodies against two most common EGFR mutations, that is, L858R point mutation and E 746–A 750 deletion. Results: Out of 33 cases, EGFR expression was seen in 28 cases. For EGFR expression assessment by IHC, both cytoplasmic and/or membranous staining taken into consideration. EGFR positivity was interpreted only when &gt;10% tumor cells having 2+ or more intensely staining pattern. The response to TKI therapy in terms of PFS and OS was also studied. Conclusion: IHC analysis of EGFR mutation using specific antibodies has extremely high specificity with good sensitivity. Use of targeted therapy in the form of TKI in EGFR positive lung adenocarcinoma has high response rate and long duration of survival (PFS and OS) with acceptable toxicity profile in contrast to the conventional therapy.

NCAPD2 serves as a potential prognostic biomarker for lung adenocarcinoma and promotes cell proliferation, migration, invasion and cell cycle in vitro.

The pro-oncogenic effects of NCAPD2 have been extensively studied across various tumor types; however, its precise role within the context of lung adenocarcinoma (LUAD) remains elusive. This study aims to elucidate the biological functions of NCAPD2 in LUAD and unravel the underlying mechanistic pathways. Utilizing bioinformatics methodologies, we explored the differential expression of NCAPD2 between normal and tumor samples, along with its correlations with clinical-pathological characteristics, survival prognosis, and immune infiltration. In the TCGA-LUAD dataset, tumor samples demonstrated significantly elevated levels of NCAPD2 expression compared to normal samples (p < 0.001). Clinically, higher NCAPD2 expression was notably associated with advanced T, N, and M stages, pathologic stage, gender, smoking status, and diminished overall survival (OS). Moreover, differentially expressed genes (DEGs) associated with NCAPD2 were predominantly enriched in pathways related to cell division. Immune infiltration analysis revealed that NCAPD2 expression levels were linked to the infiltration of memory B cells, naïve CD4+ T cells, activated memory CD4+ T cells, and M1 macrophages. In vitro experiments demonstrated that silencing NCAPD2 suppressed LUAD cell proliferation, migration, invasion, epithelial-mesenchymal transition (EMT), and cell cycle progression. In summary, NCAPD2 may represent a promising prognostic biomarker and novel therapeutic target for LUAD.

Integrated bioinformatics analysis of nucleotide metabolism based molecular subtyping and biomarkers in lung adenocarcinoma.

Lung adenocarcinoma (LUAD), a predominant subtype of non-small cell lung cancers, continues to challenge treatment outcomes due to its heterogeneity and complex tumor microenvironment (TME). Dysregulation in nucleotide metabolism has been identified as a significant factor in tumorigenesis, suggesting its potential as a therapeutic target. This study analyzed LUAD samples from The Cancer Genome Atlas (TCGA) using Non-negative Matrix Factorization (NMF) clustering, Weighted Correlation Network Analysis (WGCNA), and various machine learning techniques. We investigated the role of nucleotide metabolism in relation to clinical features and immune microenvironment through large-scale data analysis and single-cell sequencing. Using in vivo and in vitro experiments such as RT-qPCR, Western Blot, immunohistochemistry, and subcutaneous tumor formation in mice, we further validated the functions of key nucleotide metabolism genes in cell lines and animals. Nucleotide metabolism genes classified LUAD patients into two distinct subtypes with significant prognostic differences. The 'C1' subtype associated with active nucleotide metabolism pathways showed poorer prognosis and a more aggressive tumor phenotype. Furthermore, a nucleotide metabolism-related score (NMRS) calculated from the expression of 28 key genes effectively differentiated between patient outcomes and predicted associations with oncogenic pathways and immune responses. By integrating various immune infiltration algorithms, we delineated the associations between nucleotide metabolism signature genes and the tumor microenvironment, and characterized their distribution differences at the cellular level by analyzing single-cell sequencing dataset related to immunochemotherapy. Finally, we demonstrated the differential expression of the key nucleotide metabolism gene AUNIP acts as an oncogene to promote LUAD cell proliferation and is associated with tumor immune infiltration. The study underscores the pivotal role of nucleotide metabolism in LUAD progression and prognosis, highlighting the NMRS as a valuable biomarker for clinical outcomes and therapeutic responses. Specifically, AUNIP functions as a critical oncogene, offering a promising target for novel treatment strategies in LUAD.

Identification of Ion Channel-Associated Prognostic Biomarkers for Lung Adenocarcinoma

Identification of Ion Channel-Associated Prognostic Biomarkers for Lung Adenocarcinoma

Comprehensive analysis of m6A modification patterns and m6A-related lncRNAs as potential biomarkers in lung adenocarcinoma.

N6-methyladenosine (m6A) methylation is considered to induce tumor cell proliferation, migration, and apoptosis. Understanding the mechanism of m6A-related lncRNAs in the development of lung adenocarcinoma (LUAD) may help predict prognosis. m6A-related lncRNAs related to lung cancer were identified and combined with the MeRIP-Seq dataset. The consensus clustering method was utilized to divide LUAD patients, and prognostic model was constructed using the Lasso Cox algorithm. The cluster profiler package was used for gene ontology and KEGG enrichment. The proportion of immune infiltration was estimated using the CIBERSORT algorithm. The decision tree was constructed by the rpart package, and nomograms were built by the rms package. The Connectivity Map database was analyzed for the therapeutic effects of small molecule drugs for LUAD. In addition, qPCR, colony formation and transwell assays were performed to validate functions of m6A-associated lncRNAs. Nineteen m6A-modified lncRNAs in LUAD were identified. LUAD patients were divided into two categories based on the expression of 19 m6A-related lncRNAs. Cluster 2 patients had better antigen production and expression, while naive B cells, plasma cells, and activated NK cells were lower in cluster 1. Nine m6A-related lncRNAs were selected to establish a risk model for evaluating the prognosis of LUAD patients. The high-risk group had higher tumor mutational burden and lower TIDE scores with more gamma delta T cells and neutrophils. Nomograms showed that the prognostic model had predominant predictive ability for LUAD patients based on the risk score analyzed by the decision tree model. Benzo(a)pyrene and neurodazine might improve the prognosis of LUAD patients. The qRT-PCR results confirmed the reliability of the analytical results. The establishment of a prognostic model of m6A-related lncRNAs can independently predict overall survival in LUAD and may help to develop personalized immunotherapy strategies.

Overexpression of SLC35F2 is a potential prognostic biomarker for lung adenocarcinoma

ObjectiveTo explore the potential clinical and prognostic significance of Homo sapiens solute carrier family 35 member F2 (SLC35F2) in the context of lung adenocarcinoma (LUAD). MethodsThe expression pattern of SLC35F2 in LUAD tissues and normal tissues was analyzed in The Cancer Genome Atlas (TCGA) datasets and validated in 12 pairs of fresh clinical LUAD tissues and their corresponding adjacent normal tissues using quantitative real-time PCR (qRT-PCR) and western blotting. Immunohistochemistry (IHC) was used to assess the protein expression of SLC35F2 in 60 paraffin-embedded LUAD tissues, and its associations with clinicopathological parameters were further examined. The prognostic significance of SLC35F2 mRNA expression was also evaluated using the Kaplan-Meier method, and Cox regression models in LUAD patients from the TCGA database. The potential utility of SLC35F2 as an indicator of recurrence or metastasis was explored through the follow-up of selected clinical LUAD cases. Lastly, gene set enrichment analysis (GSEA) was conducted to investigate the underlying biological mechanisms and signaling pathways. ResultsBioinformatics analysis utilizing the TCGA database indicated that SLC35F2 mRNA exhibited heightened expression in LUAD tissues when compared to normal tissues. These findings were further substantiated through the examination of 12 pairs of clinical LUAD tissues and their corresponding adjacent normal tissues, employing qRT-PCR and western blotting techniques. IHC results from a cohort of 60 LUAD patients demonstrated an up-regulation of SLC35F2 in 38 out of 60 individuals (63.3 %), which exhibited a significant correlation with tumor size, lymph node metastasis, and clinical stage (all P < 0.05). Both the Kaplan-Meier curve and the Cox proportional hazard analyses indicated a strong association between the up-regulation of SLC35F2 mRNA expression and unfavorable overall survival (OS) in patients with LUAD, as observed in the TCGA datasets (P < 0.05). The follow-up findings from select clinical LUAD cases provided evidence that the expression of SLC35F2 could serve as a dependable biomarker for monitoring the recurrence or metastasis. Additionally, the GSEA highlighted the enrichment of apoptosis, adhesion, small cell lung cancer (SCLC), and p53 signaling pathways in the subgroup of LUAD patients with elevated SLC35F2 expression. ConclusionSLC35F2 exhibited an up-regulated in both mRNA and protein expression, rendering it a valuable independent prognostic indicator for patients diagnosed with LUAD.

TICRR serves as a prognostic biomarker in lung adenocarcinoma with implications in RNA epigenetic modification, DDR pathway, and RNA metabolism.

TOPBP1 interacting checkpoint and replication regulator (TICRR), a hub gene of the Cdk2-mediated initiation step of DNA replication, has been shown an essential role in tumorigenesis by accelerating the DNA replication of tumor cells. RT-qPCR was used to detect the mRNA expression of TICRR in LUAD tumors and adjacent normal tissues. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database of LUAD were acquired to analyze the critical role of TICRR expression in survival prognosis and clinicopathology characters in LUAD. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed using the R package. The correlation of TICRR expression with immune cellinfiltration, RNA epigenetic modification, DNA damage repair (DDR) pathway, and cell metabolism of LUAD was further explored to verify significant conclusions. TICRR was significantly upregulated in most cancer types, including LUAD, lung squamous cell carcinoma (LUSC), and others. Cox regression analysis indicated the overexpression of TICRR was associated with poor survival in several cancers. In LUAD, TICRR expression was positively correlated with tumor stage and was increased in smoking, male, and high tumor mutational burden (TMB) patients. Enrichment analysis revealed that TICRR could influence tumor proliferation and prognosis via activating pathways involving cell cycle, DNA repair, DNA replication, cysteine metabolism, oxidative phosphorylation, and ubiquitin-mediated proteolysis pathways. Interestingly, high TICRR expression correlated with DDR pathway signature (34 genes), 37 m6A/m5C regulated genes, and some metabolism-regulated genes. Silencing the TICRR gene affects cysteine metabolism and modifies cancer-related pathways, with decreased cell cycle and increased B/T cell receptor signaling. Our TICRR risk model accurately predicts LUAD patient prognosis, validated across GEO datasets, and is integrated with clinical characteristics via a nomogram, facilitating personalized treatment strategies and enhancing patient management. Taken together, TICRR has emerged as a promising prognostic biomarker in lung adenocarcinoma (LUAD), with implications in immune activation, cell cycle regulation, RNA modification, and tumor energy metabolism. These findings suggest that TICRR could serve as a viable therapeutic target and a reliable prognostic indicator for LUAD.

SPOCK1, as a potential prognostic and therapeutic biomarker for lung adenocarcinoma, is associated with epithelial-mesenchymal transition and immune evasion

BackgroundThe occurrence of epithelial-mesenchymal transition (EMT) and immune evasion is considered to contribute to poor prognosis in lung adenocarcinoma (LUAD). Therefore, this study aims to explore the key oncogenes that promote EMT and immune evasion and reveal the expression patterns, prognostic value, and potential biological functions.MethodsFirstly, we identified gene modules associated with EMT and Tumor Immune Dysfunction and Exclusion (TIDE) through weighted gene co-expression network analysis (WGCNA). Next, we utilized differential analysis and machine learning to identify the key genes and validate them. Moreover, we analyzed the correlation between key genes and tumor microenvironment remodeling, drug sensitivity, as well as mutation frequency. Furthermore, we explored and validated their malignant biological characteristics through in vitro experiments and clinical samples. Finally, potential drugs for LUAD were screened based on CMap and validated through experiments.ResultsFirstly, WGCNA analysis revealed that red and green modules were highly correlated with EMT and TIDE. Among them, upregulated expression of SPOCK1 was observed in lung adenocarcinoma tissues and was associated with poor prognosis. Additionally, patients in the high SPOCK1 group showed more activation of malignant oncogenic pathways, higher infiltration of immunosuppressive components, and a higher frequency of mutations. The knockdown of SPOCK1 suppressed invasion and metastasis capabilities of lung adenocarcinoma cells, and the high expression of SPOCK1 was associated with low infiltration of CD8+ T cells. Therapeutic aspects, SPOCK1 can be a candidate indicator for drug sensitivity and CMap showed that VER-155008 was the drug candidate with the largest perturbation effect on the SPOCK1 expression profile. In vitro and in vivo experiments validated the cancer-inhibitory effect of VER-155008 in LUAD.ConclusionThis study revealed through comprehensive bioinformatics analysis and experimental analysis that SPOCK1 can promote EMT and immune escape in LUAD, and it may serve as a promising candidate prognostic biomarker and therapeutic target for LUAD.

Integrative Multianalytical Model Based on Novel Plasma Protein Biomarkers for Distinguishing Lung Adenocarcinoma and Benign Pulmonary Nodules.

Given the pressing clinical problem of making a decision in diagnosis for subjects with pulmonary nodules, we aimed to discover novel plasma protein biomarkers for lung adenocarcinoma (LUAD) and benign pulmonary nodules (BPNs) and then develop an integrative multianalytical model to guide the clinical management of LUAD and BPN patients. Through label-free quantitative plasma proteomic analysis (data are available via ProteomeXchange with identifier PXD046731), 12 differentially expressed proteins (DEPs) in LUAD and BPN were screened. The diagnostic abilities of DEPs were validated in two independent validation cohorts. The results showed that the levels of three candidate proteins (PRDX2, PON1, and APOC3) were lower in the plasma of LUAD than in BPN. The three candidate proteins were combined with three promising computed tomography indicators (spiculation, vascular notch sign, and lobulation) and three traditional markers (CEA, CA125, and CYFRA21-1) to construct an integrative multianalytical model, which was effective in distinguishing LUAD from BPN, with an AUC of 0.904, a sensitivity of 81.44%, and a specificity of 90.14%. Moreover, the model possessed impressive diagnostic performance between early LUADs and BPNs, with the AUC, sensitivity, specificity, and accuracy of 0.868, 65.63%, 90.14%, and 82.52%, respectively. This model may be a useful auxiliary diagnostic tool for LUAD and BPN by achieving a better balance of sensitivity and specificity.

Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma

Objective To screen and identify microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) using clinical samples and construct a prediction model for the prognosis of LUAD. Methods 160 patient samples were used to screen and identify miRNAs associated with the prognosis of LUAD. Differentially expressed miRNAs were analyzed using gene chip technology. The selected miRNAs were validated using samples from the validation sample group. Cox proportional hazards regression was used to construct the model and Kaplan-Meier was used to plot survival curves. Model power was assessed by testing the prognosis of the constructed model using real-time polymerase chain reaction (RT-PCR) data. Results The data showed that miR-1260b, miR-21-3p and miR-92a-3p were highly expressed in the early recurrence and metastasis group, while miR-2467-3p, miR-4659a-3p, miR-4514, miR-1471 and miR-3621 were lowly expressed. It was further confirmed that miR-21-3p was significantly highly expressed in the early recurrence and metastasis group (p = 0.02). Receiver operating characteristic (ROC) curve results showed cut-off point value of 0.0172, sensitivity of 88.2% and specificity of 100%. The predictive results of the constructed model were in good agreement with the actual prognosis of patients by using the validation sample test (Kappa = 0.426, p < 0.001), with a model sensitivity of 74.4%, a specificity of 68.3%, and an accuracy of 71.3%. Conclusion miRNAs associated with the prognosis of patients with stage I LUAD were screened and validated, and a risk model for predicting the prognosis of patients was constructed. This model has good consistency with the actual prognosis of patients.

Identification of epithelial-mesenchymal transition-related biomarkers in lung adenocarcinoma using bioinformatics and lab experiments.

Lung adenocarcinoma accounts for approximately 40% of lung cancer cases and poses a serious threat to human health. Therefore, there is an urgent need to identify central biomarkers in lung adenocarcinoma. We first identified the EMT-associated genes in LUAD based on the TCGA cohort. Then we screened these 90 EMT-associated genes using univariate Cox regression analysis and LASSO regression analysis to develop a prognostic gene signature in the training set. The predictive performance of the gene signature was assessed in the validation set and multiple external test sets using the ROC cure, C index and log-rank tests. RT-PCR, western blot, wound healing assays, and siRNA methods were further used to investigate the role of PLEK2 in tumor behaviors. Eight genes (CCNB1, PLEK2, DERL3, C1QTNF6, DLGAP5, HMMR, GJB3, and SPOCK1) were eventually selected to develop an eight-gene signature. The 5-year AUC of the gene signature has a robust predictive ability both for predicting overall survival (0.774, 0.756, and 0.669 in the external test sets, respectively), and for progression free survival (0.774, 0.746, and 0.755 in the external test sets, respectively). C-index of the gene signature was 0.961 ± 0.005, 0.916 ± 0.011, and 0.868 ± 0.234 in the external test sets, respectively. Four genes (C1QTNF6, DLGAP5, HMMR, and PLEK2) were identified as key genes in LUAD progression, which were upregulated in the cancerous tissue compared with in the normal tissue (P < 0.001), and correlated with an unwanted prognosis in lung cancer (P < 0.05). PLEK2 was used as an example to explore its effect on LUAD progression in vitro using RT-PCR, western blot, CCK8, si-RNA and wound healing assay. Silencing of PLEK2 was shown to reduce proliferative and migrated ability of lung cancer cells via prohibition of autophagy. This study developed a novel EMT-related gene signature benefiting precision medicine, and identified four pivotal genes which can serve as therapeutic targets in LUAD. Four key genes can serve as molecular targets for patients with LUAD; silencing of PLEK2 was shown to reduce proliferative and migrated ability of lung cancer cells via prohibition of autophagy.

EP03.05-01 Aberrant Methylation of ZNF577 As a Potential Prognostic Biomarker for Lung Adenocarcinoma

EP03.05-01 Aberrant Methylation of ZNF577 As a Potential Prognostic Biomarker for Lung Adenocarcinoma

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma.

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy. This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites. Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models. To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

MicroRNA‑mediated regulation in lung adenocarcinoma: Signaling pathways and potential therapeutic implications (Review).

Lung adenocarcinoma (LUAD) poses a significant global health burden owing to its high incidence rate and unfavorable prognosis, driven by frequent recurrence and drug resistance. Understanding the biological mechanisms underlying LUAD is imperative to developing advanced therapeutic strategies. Recent research has highlighted the role of dysregulated microRNAs (miRNAs) in LUAD progression through diverse signaling pathways, including the Wnt and AKT pathways. Of particular interest is the novel pathological mechanism involving the interaction between competing endogenous RNAs (ceRNAs) and miRNAs. This review critically analyzed the impact of aberrant miRNA expression on LUAD development, shedding light on the associated signaling pathways. It also highlighted the emerging significance of ceRNA‑miRNA interactions in LUAD pathogenesis. Elucidating the intricate regulatory networks involving miRNAs and ceRNAs presents a promising avenue for the development of potential therapeutic interventions and diagnostic biomarkers in LUAD. Further research in this area is essential to advance precision medicine approaches and improve patient outcomes.

Erythropoietin receptor is a risk factor for prognosis: A potential biomarker in lung adenocarcinoma

Lung cancer has the highest mortality rate of all cancers, and LUAD's survival rate is particularly poor. Erythropoietin receptor (EPOR) can be detected in lung adenocarcinoma (LUAD), however, the expression levels and prognostic value of EPOR in LUAD are still unclear. In our study, clinicopathological data of 92 LUAD patients between January 2008 and June 2016, multiple bioinformatics databases and immunohistochemistry were used to explore the EPOR expression, the mutant genes affecting EPOR expression, and the correlation of EPOR expression with oxidative stress - related genes, prognosis, immune microenvironment. All statistical analyses were performed in the R version 4.1.1. The study found that EPOR expression might be down-regulated at the mRNA levels and significantly up-regulated at the protein levels in LUAD, which indicates that the mRNA and protein levels of EPOR are inconsistent. The muTarget showed that the expression of EPOR was significantly different between the mutant group and the wild group of 15 genes, including DDX60L and C1orf168. Importantly, we found that EPOR was associated with VEGF and HIF family members, and had significant positive correlation with oxidative stress - related genes such as CCS, EPX and TXNRD2. This suggests that EPOR may be involved in the regulation of oxidative stress. The Kaplan-Meier Plotter and PrognoScan databases consistently concluded that EPOR was associated with prognosis in LUAD patients. Our clinicopathological data showed that high EPOR expression was associated with poorer overall survival (29.5 vs 46 months) and had a good predictive ability for 4-year and 5-year survival probability. EPOR is expected to be a potential new prognostic marker for LUAD.

Comprehensive assessment of base excision repair (BER)-related lncRNAs as prognostic and functional biomarkers in lung adenocarcinoma: implications for personalized therapeutics and immunomodulation.

Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer, and comprehending its molecular mechanisms is pivotal for advancing treatment efficacy. This study aims to explore the prognostic and functional significance of base excision repair (BER)-related long non-coding RNAs (BERLncs) in LUAD. A risk score model for BERLncs was developed using the least absolute shrinkage and selection operator regression and Cox regression analysis. Model validation and prognostic evaluation were performed using Kaplan-Meier and receiver-operating characteristic curve analyses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to elucidate the potential biological functions of BERLncs. Comparative analyses were carried out to investigate disparities in tumor mutation burden (TMB), immune infiltration, tumor immune dysfunction and exclusion (TIDE) score, chemosensitivity, and immune checkpoint gene expression between the two risk groups. A predictive risk score model comprising 19 BERLncs was successfully developed. Patients were divided into high-risk and low-risk groups according to the median risk score. The high-risk subgroup exhibited significantly inferior overall survival. Functional enrichment analysis revealed pathways associated with lung cancer development, notably the neuroactive ligand-receptor interaction pathway. High-risk patients demonstrated elevated TMB, diminished TIDE scores, and an immunosuppressive tumor microenvironment, while low-risk patients displayed potential benefits from immunotherapy. Additionally, the risk model identified potential anticancer agents. The risk score model based on BERLncs shows promise as a prognostic biomarker for LUAD patients, providing valuable insights for clinical decision-making, therapeutic strategies, and understanding of underlying biological mechanisms.

Diagnosis and Prognosis of Non-small Cell Lung Cancer based on Machine Learning Algorithms.

Non-small cell lung cancer (NSCLC) has been the subject of intense scholarly debate. We aimed to identify the potential biomarkers via bioinformatics analysis. Three datasets were downloaded from gene expression omnibus database (GEO). R software was applied to screen differentially expressed genes (DEGs)and analyze immune cell infiltrates. Gene set enrichment analysis (GSEA) showed significant function and pathway in two groups. The diagnostic markers were further investigated by multiple machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)). Various online analytic platforms were utilized to explore the expression and prognostic value of differential genes. Furthermore, western blotting was performed to test the effects of genes on cell proliferation in vitro. We identified 181 DEGs shared by two datasets and selected nine diagnostic markers. Those genes were also significantly overexpressed in the third dataset. Topoisomerase II alpha (TOP2A) is overexpressed in lung cancer and associated with a poor prognosis, which was confirmed using immunohistochemistry (IHC) and Western blotting. Additionally, TOP2A showed a negative correlation with immune cells, such as CD8+ T cells, eosinophils and natural killer (NK) cell. Collectively, for the first time, we applied multiple machine learning algorithms, online databases and experiments in vitro to show that TOP2A is a potential biomarker for lung adenocarcinoma and could facilitate the development of new treatment strategies.