Potential Biomarker For Lung Cancer Research Articles

Circulating miRNA panels as a novel non-invasive diagnostic, prognostic, and potential predictive biomarkers in non-small cell lung cancer (NSCLC)

BackgroundNon-small cell lung cancer (NSCLC) is characterised by its aggressiveness and poor prognosis. Early detection and accurate prediction of therapeutic responses remain critical for improving patient outcomes. In the present study, we investigated the potential of circulating microRNA (miRNA) as non-invasive biomarkers in patients with NSCLC.MethodsWe quantified miRNA expression in plasma from 122 participants (78 NSCLC; 44 healthy controls). Bioinformatic tools were employed to identify miRNA panels for accurate NSCLC diagnosis. Validation was performed using an independent publicly available dataset of more than 4000 NSCLC patients. Next, we correlated miRNA expression with clinicopathological information to identify independent prognostic miRNAs and those predictive of anti-PD-1 treatment response.ResultsWe identified miRNA panels for lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) diagnosis. The LUAD panel consists of seven circulating miRNAs (miR-9-3p, miR-96-5p, miR-147b-3p, miR-196a-5p, miR-708-3p, miR-708-5p, miR-4652-5p), while the LUSC panel comprises nine miRNAs (miR-130b-3p, miR-269-3p, miR-301a-5p, miR-301b-5p, miR-744-3p, miR-760, miR-767-5p, miR-4652-5p, miR-6499-3p). Additionally, miR-135b-5p, miR-196a-5p, miR-31-5p (LUAD), and miR-205 (LUSC) serve as independent prognostic markers for survival. Furthermore, two miRNA clusters, namely miR-183/96/182 and miR-767/105, exhibit predictive potential in anti-PD-1-treated LUAD patients.ConclusionsCirculating miRNA signatures demonstrate diagnostic and prognostic value for NSCLC and may guide treatment decisions in clinical practice.

Molecular Mechanisms and Potential Predictive Biomarkers in Advanced Non-small Cell Lung Cancer: A Summary of Current and Future Trends

Non-small-cell lung cancer (NSCLC) accounts for 85% of lung cancer cases and is associated with different risk factors (smoking habits, gender, and age). In this scenario, many studies have been conducted to pursue improvement of survival, faster and better therapy response, reduced adverse events, and expanded available therapies and treatments against tumor resistance to drugs. These studies have focused on defining the most prevalent NSCLC biomarkers (EGFR, HER2, ALK, MET, ROS1, BRAF, KRAS G12C, HER3, NTRK, and NRG1) and their actionability. It is noteworthy that expressed kinase receptors can have overlapping mechanisms of activation of different pathways (JAK-STAT, MAPK, PI3K-AKT-mTOR, and PLC-c), which can lead to the same outcome of cell proliferation, migration, and survival resulting in increased tumor resistance to treatment. This review provides an overview of the latest findings regarding NSCLC treatment, emphasizing particular biomarkers and potential molecularly altered pathways implicated as targeted therapies. Additionally, it explores the clinical significance of the proposed treatments, their implication on progression-free survival, ongoing clinical trials, and their perspective of evolution so far.

Lung cancer metabolomics: a pooled analysis in the Cancer Prevention Studies

BackgroundA better understanding of lung cancer etiology and the development of screening biomarkers have important implications for lung cancer prevention.MethodsWe included 623 matched case–control pairs from the Cancer Prevention Study (CPS) cohorts. Pre-diagnosis blood samples were collected between 1998 and 2001 in the CPS-II Nutrition cohort and 2006 and 2013 in the CPS-3 cohort and were sent for metabolomics profiling simultaneously. Cancer-free controls at the time of case diagnosis were 1:1 matched to cases on date of birth, blood draw date, sex, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression, controlling for confounders. The Benjamini–Hochberg method was used to correct for multiple comparisons.ResultsSphingomyelin (d18:0/22:0) (OR: 1.32; 95% CI: 1.15, 1.53, FDR = 0.15) and taurodeoxycholic acid 3-sulfate (OR: 1.33; 95% CI: 1.14, 1.55, FDR = 0.15) were positively associated with lung cancer risk. Participants diagnosed within 3 years of blood draw had a 55% and 48% higher risk of lung cancer per standard deviation increase in natural log-transformed sphingomyelin (d18:0/22:0) and taurodeoxycholic acid 3-sulfate level, while 26% and 28% higher risk for those diagnosed beyond 3 years, compared to matched controls. Lipid and amino acid metabolism accounted for 47% to 80% of lung cancer-associated metabolites at P < 0.05 across all participants and subgroups. Notably, ever-smokers exhibited a higher proportion of lung cancer-associated metabolites (P < 0.05) in xenobiotic- and lipid-associated pathways, whereas never-smokers showed a more pronounced involvement of amino acid- and lipid-associated metabolic pathways.ConclusionsThis is the largest prospective study examining untargeted metabolic profiles regarding lung cancer risk. Sphingomyelin (d18:0/22:0), a sphingolipid, and taurodeoxycholic acid 3-sulfate, a bile salt, may be risk factors and potential screening biomarkers for lung cancer. Lipid and amino acid metabolism may contribute significantly to lung cancer etiology which varied by smoking status.

Weighted gene co-expression network analysis and whole genome sequencing identify potential lung cancer biomarkers

BackgroundTuberculosis (TB) leads to an increased risk of lung cancer (LC). However, the carcinogenetic mechanism of TB remains unclear. We constructed gene co-expression networks and carried out whole-exome sequencing (WES) to identify key modules, hub genes, and the most recurrently mutated genes involved in the pathogenesis of TB-associated LC.MethodsThe data used in this study were obtained from the Gene Expression Omnibus (GEO) and WES. First, we screened LC-related genes in GSE43458 and TB-related genes in GSE83456 by weighted gene co-expression network analysis (WGCNA). Subsequently, we screened differentially expressed genes related to LC and TB in GSE42834. We also performed WES of 15 patients (TB, n = 5; LC, n = 5; TB+LC, n = 5), constructed mutational profiles, and identified differences in the profiles of the three groups for further investigation.ResultsWe identified 278 hub genes associated with tumorigenesis of pulmonary TB. Moreover, WES identified 112 somatic mutations in 25 genes in the 15 patients. Finally, four common genes (EGFR, HSPA2, CECR2, and LAMA3) were confirmed in a Venn diagram of the 278 hub genes and the mutated genes from WES. KEGG analysis revealed various pathway changes. The PI3K–AKT signaling pathway was the most enriched pathway, and all four genes are included in this pathway. Thus, these four genes and the PI3K–AKT signaling pathway may play important roles in LC.ConclusionSeveral potential genes and pathways related to TB-associated LC were identified, including EGFR and three target genes not found in previous studies. These genes are related to cell proliferation, colony formation, migration, and invasion, and provide a direction for future research into the mechanisms of LC co-occurring with TB. The PI3K–AKT signaling pathway was also identified as a potential key pathway involved in LC development.

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Human epididymal protein 4 and its combined detection show good diagnostic value in lung cancer: A retrospective study.

This study aimed to assess the diagnostic value of human epididymal protein 4 (HE4), a potential novel biomarker for lung cancer, and its combined detection with five other conventional biomarkers in lung cancer diagnosis and subtyping. In this retrospective study, 115 lung cancer patients, 50 patients with benign pulmonary disease, and 50 healthy controls were included. Serum HE4, progastrin-releasing peptide (ProGRP), squamous cell carcinoma (SCC) antigen, cytokeratin-19 fragment (CYFRA21-1), neuron-specific enolase (NSE), and carcinoembryonic antigen (CEA) were analyzed using the electrochemiluminescence immunoassay and chemiluminescence immunoassay. The receiver operating characteristic curve was performed to analyze the diagnostic efficacy of individual biomarkers in identifying both lung cancer and its histologic subtypes. All six biomarkers showed significantly elevated levels in the lung cancer group compared to both benign pulmonary disease and control groups (P < 0.05). Among the biomarkers evaluated, HE4 exhibited the highest diagnostic performance for lung cancer, lung adenocarcinoma, and lung squamous cell carcinoma with area under the curve (AUC) values of 0.921, 0.891, and 0.937, respectively. ProGRP was the optimal biomarker for small cell lung cancer with an AUC of 0.973. The combination of all six biomarkers yielded the largest AUCs in the diagnosis of lung cancer subtypes (0.937 for lung adenocarcinoma, 0.998 for lung squamous cell carcinoma, and 0.985 for small cell lung cancer). Furthermore, specific combinations, such as HE4 + CEA, HE4 + SCC, and ProGRP + HE4 + NSE, showed strong diagnostic performance in lung cancer. HE4 and its combined detection held substantial clinical significance in the diagnosis of lung cancer and its histologic subtyping, especially for lung adenocarcinoma and lung squamous cell carcinoma.

Photodynamic therapy upregulates expression of HIF-1α and PD-L1 in related pathways and its clinical relevance in non-small-cell lung cancer

BackgroundPhotodynamic therapy (PDT) is a promising interventional treatment approach that contributes to antitumor immunity. It has been reported that PDT can enhance the effectiveness of immune checkpoint inhibitors (ICIs), but its mechanism is yet unclear. Herein, we implemented bioinformatics analysis to detect common pathways and potential biomarkers in non-small cell lung cancer (NSCLC), PDT, and NSCLC immunotherapy to investigate potential links between PDT, immunotherapy and NSCLC, and their clinical impact.MethodsDifferentially expressed genes in NSCLC- and NSCLC immunotherapy-related data in the GEO database were intersected with PDT-related genes in the GeneCards database to obtain candidate genes and shared pathways. Enrichment analysis and protein–protein interaction were established to identify key genes in functionally enriched pathways. The expression profiles and the prognostic significance of key genes were depicted.ResultsBioinformatics analysis showed that HIF-1α was screened as a prognostic gene in hypoxia, HIF-1, and PD-L1-related signaling pathways, which was associated with clinical response in NSCLC patients after PDT and immunotherapy. In vivo experiments showed that PDT could inhibit tumor growth and upregulate HIF-1α and PD-L1 expressions in NSCLC tissues with a positive correlation, which might influence the blocking activity of ICIs on the HIF-1, and PD-L1-related signaling pathways.ConclusionsPDT might improve the clinical response of ICIs by upregulating tumor HIF-1α and PD-L1 expressions in NSCLC.

Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

Multi-targeted therapeutic potential of stigmasterol from the Euphorbia ammak plant in treating lung and breast cancer

Cancer is the most prevalent disease globally, which presents a significant challenge to the healthcare industry, with breast and lung cancer being predominant malignancies. This study used RNA-seq data from the TCGA database to identify potential biomarkers for lung and breast cancer. Tumor Necrosis Factor (TNFAIP8) and Sulfite Oxidase (SUOX) showed significant expression variation and were selected for further study using structure-based drug discovery (SBDD). Compounds derived from the Euphorbia ammak plant were selected for in-silico study with both TNFAIP8 and SUOX. Stigmasterol had the greatest binding scores (normalized scores of −8.53 kcal/mol and −9.69 kcal/mol) with both proteins, indicating strong stability in their binding pockets throughout the molecular dynamics’ simulation. Although Stigmasterol first changed its initial conformation (RMSD = 0.5 nm with the starting conformation) in SUOX, it eventually reached a stable conformation (RMSD of 1.5 nm). The compound on TNFAIP8 showed a persistent shape (RMSD of 0.35 nm), indicating strong protein stability. The binding free energy of the complex was calculated using the MM/GBSA technique; TNFAIP8 had a ΔGTOTAL of −24.98 kcal/mol, with TYR160 being the most significant residue, contributing −2.52 kcal/mol. On the other hand, the SUOX complex had a binding free energy of −16.87 kcal/mol, with LEU151 being the primary contributor (-1.17 kcal/mol). Analysis of the complexes' free energy landscape unveiled several states with minimum free energy, indicating robust interactions between the protein and ligand. In its conclusion, this work emphasises the favourable ability of Stigmasterol to bind with prospective targets for lung and breast cancer, indicating the need for more experimental study.

Expression of FAT1 in Lung Adenocarcinoma and Its Relationship with Immune Cell Infiltration

Lung cancer is a leading cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) is the most common pathological subtype, with adenocarcinoma being the predominant type. FAT atypical cadherin 1 (FAT1) is a receptor-like protein with a high frequency of mutations in lung adenocarcinoma. The protein encoded by FAT1 plays a crucial role in processes such as cell adhesion, proliferation, and differentiation. This study aims to investigate the expression of FAT1 in lung adenocarcinoma and its relationship with immune infiltration. Gene expression levels and relevant clinical information of 513 lung adenocarcinoma samples and 397 adjacent lung samples were obtained through The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data. The mRNA expression levels of the FAT1 gene in lung adenocarcinoma tissues were analyzed, along with its association with the prognosis of lung adenocarcinoma patients. Pathway enrichment analysis was conducted to explore the signaling pathways regulated by the FAT1 gene. Immunoblotting was used to detect the differential expression of FAT1 in lung epithelial cells and various lung cancer cell lines, while immunohistochemistry was employed to assess FAT1 expression in lung cancer and adjacent tissues. FAT1 gene mutations were identified in 14% of lung adenocarcinoma patients. TCGA database data revealed significantly higher FAT1 mRNA expression in lung adenocarcinoma tissues compared to adjacent lung tissues. Kaplan-Meier analysis indicated lower survival rates in lung adenocarcinoma patients with higher FAT gene expression. Pathway enrichment analysis suggested the involvement of FAT1 in tumor development pathways, and its expression was closely associated with immune cell infiltration. Immunohistochemical validation demonstrated significantly higher expression of FAT1 in cancer tissues compared to adjacent lung tissues. FAT1 mRNA is highly expressed in lung adenocarcinoma tissues, and elevated FAT1 mRNA expression is associated with poor prognosis in lung adenocarcinoma patients. FAT1 may serve as a potential biomarker for lung cancer.

PD-L1 mRNA derived from tumor-educated platelets as a potential immunotherapy biomarker in non-small cell lung cancer.

To date, the role of programmed death ligand-1 (PD-L1) messenger RNA (mRNA) derived from tumor-educated platelets (TEPs) has not been well investigated in patients with advanced non-small cell lung cancer (NSCLC). A few reports have examined whether mRNA in TEPs can predict the clinical responses of patients with advanced NSCLC following immunotherapy. This study aimed to identify novel biomarkers to improve the clinical benefits and outcomes of NSCLC patients. Advanced NSCLC patients receiving a combination of immunotherapy and chemotherapy, or immunotherapy alone as a first- or second-line treatment at the Fudan University Shanghai Cancer Center were enrolled in this study. All the patients had wild-type epidermal growth factor receptor/anaplastic lymphoma kinase. The patients were enrolled in clinical trials for immune checkpoint inhibitors (ICIs), including nivolumab, pembrolizumab, atezolizumab, durvalumab, tremelimumab, and camrelizumab. Tumoral PD-L1 expression was tested by immunohistochemistry (PD-L1 22C3 pharmDx kit, Agilent, Santa Clara, CA, USA) in archived tissue samples, when available, to calculate the tumor proportion scores (TPSs). RNA and exosomal RNA of blood were isolated before immunotherapy using the Yunying RNA extraction kit (Yunying Medicine, Shanghai, China). The concentration and quality of the RNA was determined using a Qubit fluorometer (Life Technologies, Carlsbad, CA, USA). Finally, we analyzed the predictive value of TEP-derived PD-L1 mRNA expression and association with the level of the tumoral PD-L1 expression. In total, 72 patients were enrolled in this study. Most of the patients were male (n=54, 75.0%), had adenocarcinoma (n=49, 68.1%). We found there was no significant correlation between the TEP-derived mRNA of PD-L1 and tumoral PD-L1 expression based on the results of the Pearson Correlation test (r=-0.19, P=0.233). Based on the median of PD-L1 mRNA, 72 patients were divided into a high PD-L1 group and a low PD-L1 group. We found that 19 patients (44.4%) responded to immunotherapy [partial response or progression-free survival (PFS) >6 months] in the high PD-L1 group, but only five patients (13.9%) responded to immunotherapy in the low PD-L1 group (P<0.01). The median PFS of the low PD-L1 group was lower than that of the high PD-L1 group (2.8 vs. 8.3 months, P<0.001). For the patients who were treated with immunotherapy alone (n=64), a similar PFS advantage was observed in the high PD-L1 group (2.8 vs. 8.0 months, P=0.002). This article presented the first data on TEP-derived PD-L1 mRNA in advanced NSCLC patients following immunotherapy and showed the potential advantage of using it as the surrogate biomarker for predicting the PFS and overall survival of patients following immunotherapy.

Survivin in lung cancer: a potential target for therapy and prevention-a narrative review.

A versatile biomarker, survivin, is highly expressed in proliferating cells of multiple cancers in humans and animals. It is an apoptosis-regulating protein, engaging in a cascade of reactions that involve several other genes and protein interactions. Currently, researchers are investigating its therapeutic potential due to the evidence linking its overexpression to advanced-stage lung cancer. This review is centered around examining survivin-related molecular mechanisms and its therapeutic role specifically in lung cancer. Our objective is to discuss the role of survivin in prognosis and treatment response, shedding light on immune-targeted therapies, as well as outlining future directions for survivin-based vaccines in lung cancer. The PubMed database and the United States National Library of Medicine search engine at the National Institutes of Health were searched on 24 August 2023 to identify published research studies. Searching "((((((airway [Title/Abstract]) OR (lung [Title/Abstract])) OR (pulm[Title/Abstract])) OR (bronch[Title/Abstract])) OR (nslc[Title/Abstract])) AND (((cancer[Title/Abstract]) OR (carcino[Title/Abstract])) OR (oncol[Title/Abstract]))) AND (survivin[Title/Abstract])" gave 728 results. After screening the title and abstracts and excluding the review articles 168 titles were shortlisted and full text studied. The discussions are added to relevant sections. Survivin is a cell cycle-dependent, inhibitor of apoptosis protein that contributes to carcinogenesis, tumor vascularization, metastasis, and treatment resistance. Several treatments that impact survivin either directly or indirectly have been reported as effective in treating lung cancer. Immunity-based therapy, a novel approach known for its targeted nature and minimal side effects, is currently under investigation for lung cancer treatment. Emerging survivin-centered vaccines exhibit promising attributes in terms of safety, effectiveness, and ability to stimulate an immune response. These factors point towards a significant potential for advancing the future of lung cancer prevention and enhancing overall survival rates. Nuclear survivin is a potential biomarker for advanced non-small cell lung cancer. It plays a role in determining drug responsiveness and is found to be significantly elevated in cases of resistance to chemotherapy. Multiple compounds and immunization strategies have been identified to impact lung cancer cells; however, they are currently in the early stages of phase I or phase II clinical trials. The substantial promise of survivin-based immunogenicity-focused treatments warrants in-depth investigation and exploration.

Systematic Analysis to Identify the MIR99AHG-has-miR-21-5p-EHD1 CeRNA Regulatory Network as Potential Biomarkers in Lung Cancer.

Lung cancer (LC) remains an extremely lethal disease worldwide, and effective prognostic biomarkers are at top priority. With the rapid development of high-throughput sequencing and bioinformatic analysis methods, the quest to characterize cancer transcriptomes continues to move forward. However, the integrated systematic analysis of lncRNA-miRNA-mRNA regulatory network in LC is lacking. In this study, we collected samples of cancer tissues and adjacent normal tissues from patients with lung cancer and conducted transcriptome and small RNA sequencing to identify differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs). The regulatory roles of miRNAs in LC were explained by functional analysis on DEM-targeted genes. The lncRNA-miRNA pairs, miRNA-mRNA pairs, and lncRNA-mRNA pairs were identified and combined to construct the interplay of lncRNA-miRNA-mRNA. We evaluated the prognostic value of selected lncRNA-miRNA-mRNA by Kaplan-Meier analysis. Finally, we analyzed the expression levels of selected DEM, DELs, and DEGs in lung cancer patients and healthy people to verify our findings. A total of 1492 DEGs, 12 DEMs, and 604 DELs were identified in LC patients. Based on the bioinformatic analysis and the regulatory mechanism of ceRNAs, 3 lncRNAs (GATA2-AS1, LINC00632, MIR99AHG), 1 miRNA (hsa-miR-21-5p) and 5 targeted genes (RECK, TIMP3, EHD1, RASGRP1 and ERG) were figured out first. Through further Kaplan-Meier analysis screening the prognostic value, we finally found the hub subnetwork (MIR99AHG-hsa-miR-21-5p-EHD1) by collating lncRNA-miRNA pairs, miRNA-mRNA pairs and lncRNA-mRNA pairs. As the key of ceRNA regulatory network, the expression of miRNA-21-5p in lung cancer patients was significantly higher than that in healthy people (P < 0.01), and its high expression was significantly associated with poor prognosis (P = 0.0025). Our study successfully constructed a MIR99AHG-hsa-miR-21-5p-EHD1 mutually regulatory network, suggesting the potential efficient biomarkers in LC.

FOXA1, induced by RC48, regulates HER2 transcription to enhance the tumorigenic capacity of lung cancer through PI3K/AKT pathway.

Lung cancer remains the tumor with the highest global incidence and mortality rates. Current primary treatment modalities encompass targeted therapy, immunotherapy, and chemotherapy; however, a subset of patients derives no benefit from these interventions. Recently, the HER2-targeting antibody drug Disitamab vedotin (RC48) was approved and introduced primarily for gastric and bladder cancers, with minimal investigation in the field of lung cancer. This study demonstrates that FOXA1 directly binds to the promoter region of HER2, influencing the HER2/PI3K/AKT signaling pathway, which consequently modulates factors that foster lung cancer proliferation and impede apoptosis. Unlike FOXA1, HER2 does not influence the expression of FOXA1. Intriguingly, in lung cancer cells, RC48 not only impacts the HER2/PI3K/AKT pathway but also affects the FOXA1/HER2/PI3K/AKT pathway, thereby exerting a robust antitumor effect. In clinical specimens, heightened expressions of FOXA1 and HER2 correlate positively with clinical progression and poorer prognosis. These findings suggest that FOXA1 may serve as a potential biomarker or therapeutic target in future non-small cell lung cancer (NSCLC) treatments, and ongoing research may position RC48 as a transformative agent in lung cancer therapy.

Predicting outcomes of Lung Cancer using the modified glasgow prognostic score: A systematic review and meta-analysis.

Previous studies have suggested that the modified Glasgow Prognostic Score (mGPS) could be a potential biomarker for lung cancer (LC). However, the association between mGPS and overall survival (OS) or progression-free survival (PFS) in lung cancer patients remains unclear. The purpose of our study was to investigate possible correlation between mGPS and OS or PFS in LC patients. An extensive search of PubMed, Cochrane Library, EMbase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Trip Database, Worldwide Science, and Google Scholar databases was done for relevant articles, published prior to May 30, 2021, that report correlation between mGPS and OS or PFS in LC patients. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were used as the main parameters for evaluation. A total of 28 studies involving 9,748 lung cancer patients were analysed. The pooled analysis revealed that elevated mGPS (≥ 0) was associated with poor OS (HR=1.54; 95% CI, 1.32-1.77) and PFS (HR=1.49; 95% CI, 1.17-1.82). Furthermore, a significant correlation between mGPS (1 or 2) and OS was observed. However, no significant correlation was found between mGPS (1 or 2) and PFS. Subgroup analysis based on ethnicity demonstrated that mGPS ≥ 0 was associated with worse OS compared to mGPS=0 in both Asian (HR=1.46; 95% CI, 1.04-1.89; p<0.05) and Caucasian (HR=1.64; 95% CI, 1.35-1.94; p<0.05) cohorts of LC patients. Our results demonstrate that positive mGPS is associated with poor survival results. Therefore, mGPS may be used as a biomarker for predicting prognosis in LC patients.

Long non-coding RNA signatures in non-small cell lung cancer and their clinicopathological significance

Lung cancer is the most common type of cancer in our country and worldwide, and it is a leading cause of cancer-related deaths. According to the latest global cancer statistics, lung cancer was identified as the second most common type of cancer, and the leading cause of cancer-related deaths. Long non-coding RNAs (lncRNAs) are a highly heterogeneous class of RNA molecules sharing many characteristics with mRNAs, except for the protein-coding potential. Accumulating mass of evidence suggest that lncRNAs play key regulatory roles during the multistep formation of human cancers including lung cancer. In previous studies, it has been shown that many lncRNA molecules play significant roles in the formation and progression of lung cancer. However, there are still numerous lncRNA molecules in lung cancer whose roles remain unknown. Accordingly, here we sought to ascertain the diagnostic and prognostic value of lncRNAs by analyzing the expression profiles of THRIL, NEAT1, and LOC105376095 in lung cancer. Remarkably, NEAT1 and LOC105376095 but not THRIL were identified to be differentially expressed in tissues of lung tumors. More importantly, LOC105376095, a yet uncharacterized lncRNA molecule, was significantly associated with the disease severity. Collectively, NEAT1 and LOC105376095 hold promise as potential diagnostic and prognostic biomarkers for lung cancer, presenting opportunities for targeted therapeutic interventions in the future.

Effect of chemotherapy on urinary volatile biomarkers for lung cancer by HS-SPME-GC-MS and chemometrics.

Volatile organic compounds (VOCs) have been studied as possible biomarkers in several diseases, including lung cancer. Early detection of cancer can improve long-term survival rates and the quality of life, so the study of VOCs in exhaled breath and urine has been increasing in recent years. This study aimed to assess the urinary VOCs that are modified after chemotherapy to identify those with the potential to be lung cancer biomarkers that can be monitored during treatment. Three urine samples from 10 men with stage IV lung adenocarcinoma were collected, as well as urine samples from 14 men with other types of cancer (control group). All samples were analyzed by headspace-solid phase microextraction gas chromatography coupled with mass spectrometry. A total of 21 urinary VOCs were found with different levels after the administration of chemotherapy, with 2-pentanone being one of those that significantly decreased. Furthermore, 2-pentanone and 3-hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate showed statistically significant differences with the control group. Chemotherapy administered to patients with advanced lung adenocarcinoma modified the volatile profile of urine. 2-Pentanone, a final product of the increased rate of fatty acid oxidation and protein hypermetabolism, significantly decreased after chemotherapy. Therefore, monitoring its urinary excretion could be very useful since its decrease over time could indicate an adequate response to chemotherapy and arrest of cancer development. Another VOC that could be a potential lung cancer biomarker is 3 hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate, whose origin may be due to inhibition of the propanoic acid metabolic pathway or increased aldehyde dehydrogenase activity.

MicroRNA Profiling of Red Blood Cells for Lung Cancer Diagnosis.

Despite extensive endeavors to establish cell-free circulating biomarkers for lung cancer diagnosis, clinical adoption remains elusive. Noteworthy, emergent evidence suggests the pivotal roles of red blood cells (RBCs) and their derivatives in tumorigenesis, illuminating potential avenues for diagnostic advancements using blood cell-derived microRNAs (miRNAs). We executed microarray analyses on three principal blood cell types-RBCs, peripheral blood mononuclear cells (PBMCs), and neutrophils-encompassing 26 lung cancer patients and 26 healthy controls. Validation was performed using droplet digital PCR within an additional cohort comprising 42 lung cancer and 39 control cases. Our investigation unearthed distinct miRNA profiles associated with lung cancer across all examined blood cell types. Intriguingly, RBC-miRNAs emerged as potential novel biomarkers for lung cancer, an observation yet to be documented. Importantly, integrating miRNAs from disparate blood cell types yielded a superior diagnostic accuracy for lung cancer over individual cell-type miRNAs. Subsequently, we formulated three diagnostic panels, adeptly discerning non-small cell lung cancer, adenocarcinoma, and squamous cell carcinoma, maintaining consistency across various disease stages. RBC-derived molecules introduce novel cancer biomarkers, and exploiting miRNA profiles across varied blood cell types unveils a promising frontier for lung cancer's early detection and histological classification.

MiR-122 and miR-21 are Stable Components of miRNA Signatures of Early Lung Cancer after Validation in Three Independent Cohorts

Several panels of circulating miRNAs have been reported as potential biomarkers of early lung cancer, yet the overlap of components between different panels is limited, and the universality of proposed biomarkers has been minimal across proposed panels. To assess the stability of the diagnostic potential of plasma miRNA signature of early lung cancer among different cohorts, a panel of 24 miRNAs tested in the frame of one lung cancer screening study (MOLTEST-2013, Poland) was validated with material collected in the frame of two other screening studies (MOLTEST-BIS, Poland; and SMAC, Italy) using the same standardized analytical platform (the miRCURY LNA miRNA PCR assay). On analysis of selected miRNAs, two associated with lung cancer development, miR-122 and miR-21, repetitively differentiated healthy participants from individuals with lung cancer. Additionally, miR-144 differentiated controls from cases specifically in subcohorts with adenocarcinoma. Other tested miRNAs did not overlap in the three cohorts. Classification models based on neither a single miRNA nor multicomponent miRNA panels (24-mer and 7-mer) showed classification performance sufficient for a standalone diagnostic biomarker (AUC, 75%, 71%, and 53% in MOLTEST-2013, SMAC, and MOLTEST-BIS, respectively, in the 7-mer model). The performance of classification in the MOLTEST-BIS cohort with the lowest contribution of adenocarcinomas was increased when only this cancer type was considered (AUC,60% in 7-mer model).

Biomarkers of lung cancer for screening and in never-smokers-a narrative review.

Lung cancer is the leading cause of cancer-related mortality worldwide, partially attributed to late-stage diagnoses. In order to mitigate this, lung cancer screening (LCS) of high-risk patients is performed using low dose computed tomography (CT) scans, however this method is burdened by high false-positive rates and radiation exposure for patients. Further, screening programs focus on individuals with heavy smoking histories, and as such, never-smokers who may otherwise be at risk of lung cancer are often overlooked. To resolve these limitations, biomarkers have been posited as potential supplements or replacements to low-dose CT, and as such, a large body of research in this area has been produced. However, comparatively little information exists on their clinical efficacy and how this compares to current LCS strategies. Here we conduct a search and narrative review of current literature surrounding biomarkers of lung cancer to supplement LCS, and biomarkers of lung cancer in never-smokers (LCINS). Many potential biomarkers of lung cancer have been identified with varying levels of sensitivity, specificity, clinical efficacy, and supporting evidence. Of the markers identified, multi-target panels of circulating microRNAs, lipids, and metabolites are likely the most clinically efficacious markers to aid current screening programs, as these provide the highest sensitivity and specificity for lung cancer detection. However, circulating lipid and metabolite levels are known to vary in numerous systemic pathologies, highlighting the need for further validation in large cohort randomised studies. Lung cancer biomarkers is a fast-expanding area of research and numerous biomarkers with potential clinical applications have been identified. However, in all cases the level of evidence supporting clinical efficacy is not yet at a level at which it can be translated to clinical practice. The priority now should be to validate existing candidate markers in appropriate clinical contexts and work to integrating these into clinical practice.