[Experts consensus on transbronchial diagnosis, localization and treatment of peripheral pulmonary nodules guided by the augmented reality optical lung navigation].

Lung cancer is the second most common malignancy with the highest mortality rate worldwide. In recent years, the rapid development of various bronchoscopic navigation techniques has provided conditions for the minimally invasive diagnosis and treatment of peripheral pulmonary nodules through the airway.Augmented reality optical lung navigation is a new technology that combined virtual bronchoscopy navigation (VBN) with augmented reality (AR) and optical navigation technology, which could assist bronchoscopist and has been widely applied in clinics. The clinical evidence certified that the navigation, has the advantages of safety and efficacy in guiding transbronchial diagnosis, localization, and treatment of pulmonary nodules. In order to standardize the clinical operation of augmented reality optical lung navigation technology and guide its application in clinical practice, Interventional Group, Society of Respiratory Diseases, Chinese Medical Association/Interventional Pulmonology Group of the Zhejiang Medical Association organized multidisciplinary experts to take the lead in formulating the Consensus of experts on transbronchial diagnosis, localization and treatment of peripheral pulmonary nodules guided by the augmented reality optical lung navigation after multiple rounds of discussion, and provided recommendation opinions and clinical guidance for the indications and contraindications, equipment and devices, perioperative treatment, operating process and complication management of peripheral pulmonary nodules applicable to augmented reality optical lung diagnosis navigation technology.

[Current Status and Issues of Companion Diagnostics for Non-Small Cell Lung Cancer].

There have been many driver gene abnormalities identified in NSCLC, and the development and clinical adoption of targeting drugs for such are progressing. This has, as a result, complicated the situation surrounding companion diagnostics (CDx). Lung cancer treatment guidelines recommend the use of Multiplex testing that allow for CDx for many driver gene abnormalities to be used prior to 1L treatment for advanced lung cancer. The problem is that insurance rules stipulate that only one of Multiplex test is deemed reimbursable per junction in time, particular CDx are linked to particular targeted drugs instead of particular driver gene abnormalities, and none of the currently available Multiplex tests contain the CDx for all genetic mutations for which drugs have been approved. This results in a fair number of cases in which regulatory issues ensue. It is also very difficult to obtain enough of a tissue sample to get accurate results in Multiplex testing for advanced NSCLC. CDx are not full-proof, as results must be interpreted with a consideration of the potential for a false-negative due to the nature of tests. This paper, therefore, will focus on the issues with CDx from the viewpoint of medical oncologists in the clinical setting.

Dépistage du cancer du poumon de routine dans la pratique clinique : que des bénéfices ?

Le dépistage annuel du cancer du poumon (lung cancer screening, LCS) par CT-scan à faible dose (LDCT) chez les adultes éligibles augmente la détection précoce de cancer pulmonaire dans la pratique communautaire et réduit la mortalité dans des études cliniques randomisées. Cependant, le LCS peut aussi présenter des inconvénients tels que des résultats faussement positifs, des imageries avec irradiation, la nécessité de procédures diagnostiques invasives et le risque de complications. Par conséquent, comprendre l'équilibre entre les avantages et les risques liés au LCS dans la pratique clinique est primordial afin d'optimiser les directives et les critères de qualité pour améliorer l'efficacité du dépistage à travers l'ensemble des systèmes de santé et des populations.

Saliva­microbiome­derived signatures: expected to become a potential biomarker for pulmonary nodules (MCEPN-1).

BACKGROUND: Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown. METHODS: We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16 S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses. RESULTS: The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators. CONCLUSIONS: Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs. TRIAL REGISTRATION: Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank.

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

Abnormal expression of LCA and CD43 in SCLC: a rare case report and brief literature review.

BACKGROUND: To present an unusual case of abnormal LCA expression and CD43 in SCLC and to review the reported literature to avoid potential diagnostic pitfalls. CASE PRESENTATION: A 73-year-old male patient suffered from persistent back pain for more than one month. MRI revealed a compression fracture of the L1-L5 vertebra. A CT scan revealed multiple nodules and masses at the left root of the neck, lung hilum and mediastinum, and multiple areas of bony destruction of the ribs. Histology of the tumor revealed that small and round cells were arranged in nests with areas of necrosis. The tumor cells were round to ovoid with scant cytoplasm and indistinct cell borders. The nuclear chromatin was finely granular, and the nucleoli were absent or inconspicuous. Immunohistochemically, the tumor cells were positive for cytokeratin, TTF-1, POU2F3, LCA, and CD43. CONCLUSION: This report highlights a potential diagnostic pitfall in the diagnosis of SCLC, urges pathologists to exercise caution in cases of LCA and CD43 positivity and illustrates the need for further immunohistochemical studies to avoid misdiagnosis.

Gold Nanoprobes for Detection of a Crucial EGFR Deletion for Early Diagnosis of Non-Small-Cell Lung Cancer.

Gold nanoparticles (AuNPs) exhibit improved optical and spectral properties compared to bulk materials, making them suitable for the detection of DNA, RNA, antigens, and antibodies. Here, we describe a simple, selective, and rapid non-cross linking detection assay, using approx. 35 nm spherical Au nanoprobes, for a common mutation occurring in exon 19 of the epidermal growth factor receptor (EGFR), associated with non-small-cell lung cancer cells. AuNPs were synthesized based on the seed-mediated growth method and functionalized with a specific 16 bp thiolated oligonucleotide using a pH-assisted method. Both AuNPs and Au nanoprobes proved to be highly stable and monodisperse through ultraviolet-visible spectrophotometry, dynamic light scattering (DLS), and electrophoretic light scattering (ELS). Our results indicate a detection limit of 1.5 µg mL-1 using a 0.15 nmol dm-3 Au nanoprobe concentration. In conclusion, this work presents an effective possibility for a straightforward, fast, and inexpensive alternative for the detection of DNA sequences related to lung cancer, leading to a potential platform for early diagnosis of lung cancer patients.

Perspectives of potentially eligible Indigenous Maori on a lung cancer screening programme: a qualitative study.

AIMS: Lung cancer causes more deaths than any other cancer, globally and in Aotearoa New Zealand, where it disproportionately affects Maori. We aimed to understand Maori perspectives on lung cancer screening in Aotearoa New Zealand to guide its equity-focussed implementation, including identifying enablers and barriers. METHODS: We took a Kaupapa Maori based co-design approach to inform future screening, recruiting Maori current/ex-smokers and members of their whanau (family) for three focus group phases held in Auckland, Aotearoa New Zealand in August 2019. Participants responded to a proposed lung cancer screening pathway and shared their attitudes and beliefs about lung cancer and screening. Results were thematically analysed. RESULTS: The 21 Maori participants supported future lung cancer screening in Aotearoa New Zealand. Perceived benefits included being more informed about lung cancer and screening and enabling healthier future generations. Barriers to screening were previous negative health service experiences; fear; stigma; and access, including time, cost and transport. Enablers included providers' cultural competence; clear communication; a one-stop shop; and support with transport. A range of factors could potentially influence a decision to participate in screening. CONCLUSIONS: Participants favoured future lung cancer screening and identified key barriers and facilitators of screening.

Application Value of CYFRA21-1 Combined with NSE, CEA, and SCC-Ag in Lung Cancer.

BACKGROUND: This study aims to investigate the application value of serum cytokeratin 19 fragment (CYFRA21-1) combined with nerve-specific enolase (NSE), carcinoembryonic antigen (CEA), and squamous cell carcinoma antigen (SCC-Ag) in the diagnosis of lung cancer (LC). METHODS: A total of 831 cases of LC, 360 cases of benign lung disease (BLD) and 102 healthy controls, were enrolled. The data were processed using SPSS, GraphPad Prism, and MedCalc software. RESULTS: The tumor marker (TM) levels in the LC and BLD groups were significantly higher than those in the control group; the CYFRA21-1, NSE, and CEA levels in the patients with LC were higher than in those with BLD. In particular, the increase was predominantly observed for the levels of CEA and CYFRA21-1 in adenocarcinoma (LUAD), CYFRA21-1 and SCC-Ag in squamous cell carcinoma (LUSC), and NSE in small cell carcinoma (SCLC). The CYFRA21-1, NSE, and CEA levels were significantly higher in stage IV than in other stages in LC. Univariate binary logistic analysis showed that increased levels of all four TMs were risk factors for BLD and LC. The area under the curve (AUC) of CYFRA21-1 was most effective in distinguishing patients with BLD or LC from the controls and in distinguishing patients with BLD and LC. The AUCs of combined CYFRA21-1, NSE, and CEA were increased to 0.755, 0.922, and 0.783, respectively, with no significant difference with the AUC of the four combined tests. In the histological classification, the best predictors were CEA, for LUAD, CYFRA21-1 for LUSC, and NSE for SCLC. Moreover, the expression levels of CYFRA21-1, NSE, and CEA significantly decreased after each treatment course. CONCLUSIONS: The combined assay of CYFRA21-1, NSE, and CEA addresses the aspects of accuracy, sensitivity, specificity, and economic cost and should be considered as a potential diagnostic test in LC.

FTIR spectroscopy for assessment of hair from lung cancer patients and its application in monitoring the chemotherapy treatment effect.

Lung cancer is the most common cancer and the leading cause of death in China. The current gold standard for clinical lung cancer diagnosis is based on histopathological examination of tumors, but it has the limitation for easy operation and convenient applications. Therefore, researchers are still striving to develop other tools and methods for non-invasive and rapid assessment of the health conditions of lung cancer patients. Hair, as a reflection of the metabolism of the body, is closely related to human health conditions. In principle, Fourier-transform infrared (FTIR) spectroscopy can probe the major chemical compositions in the hair. However, as indicated by previous studies, there is still the challenge to make good use of FTIR spectroscopy for achieving reliable analysis of hair from cancer patients. In this study, hair samples from 82 lung cancer patients were collected and subjected to FTIR measurements and analysis, which showed the protein content in the hair is closely related to the protein content in the blood serum of patients, and the contents of protein and lipid are statistically lower in the lung cancer patients. Furthermore, we demonstrated that FTIR spectroscopy could be employed to monitor the hair of lung cancer patients undergoing chemotherapy, and confirmed that the FTIR spectra of the hair may reflect the resultant effect of the chemotherapy. As such, this work validates the way of using FTIR spectroscopy in hair analysis for the assistance of medical diagnosis of lung cancer as well as monitoring the conditions of the patients under the medical treatment.

Detection of lung cancer through SERS analysis of serum.

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation.

Lung cancer stands as the most prevalent form of cancer globally, posing a significant threat to human well-being. Due to the lack of effective and accurate early diagnostic methods, many patients are diagnosed with advanced lung cancer. Although surgical resection is still a potential means of eradicating lung cancer, patients with advanced lung cancer usually miss the best chance for surgical treatment, and even after surgical resection patients may still experience tumor recurrence. Additionally, chemotherapy, the mainstay of treatment for patients with advanced lung cancer, has the potential to be chemo-resistant, resulting in poor clinical outcomes. The emergence of liquid biopsies has garnered considerable attention owing to their noninvasive nature and the ability for continuous sampling. Technological advancements have propelled circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), tumor metabolites, tumor-educated platelets (TEPs), and tumor-associated antigens (TAA) to the forefront as key liquid biopsy biomarkers, demonstrating intriguing and encouraging results for early diagnosis and prognostic evaluation of lung cancer. This review provides an overview of molecular biomarkers and assays utilized in liquid biopsies for lung cancer, encompassing CTCs, ctDNA, non-coding RNA (ncRNA), EVs, tumor metabolites, TAAs and TEPs. Furthermore, we expound on the practical applications of liquid biopsies, including early diagnosis, treatment response monitoring, prognostic evaluation, and recurrence monitoring in the context of lung cancer.

Comprehensive analysis and validation reveal DEPDC1 as a potential diagnostic biomarker associated with tumor immunity in non-small-cell lung cancer.

Current evidence suggests that DEP domain containing 1 (DEPDC1) has an important effect on non-small-cell lung cancer (NSCLC). However, the diagnostic value and the regulatory function within NSCLC are largely unclear. This work utilized publicly available databases and in vitro experiments for exploring, DEPDC1 expression, clinical features, diagnostic significance and latent molecular mechanism within NSCLC. According to our results, DEPDC1 was remarkably upregulated in the tissues of NSCLC patients compared with non-carcinoma tissues, linked with gender, stage, T classification and N classification based on TCGA data and associated with smoking status and stage according to GEO datasets. Meanwhile, the summary receiver operating characteristic (sROC) curve analysis result showed that DEPDC1 had a high diagnostic value in NSCLC (AUC = 0.96, 95% CI: 0.94-0.98; diagnostic odds ratio = 99.08, 95%CI: 31.91-307.65; sensitivity = 0.89, 95%CI: 0.81-0.94; specificity = 0.92, 95%CI: 0.86-0.96; positive predictive value = 0.94, 95%CI: 0.89-0.98; negative predictive value = 0.78, 95%CI: 0.67-0.90; positive likelihood ratio = 11.77, 95%CI: 6.11-22.68; and negative likelihood ratio = 0.12, 95%CI: 0.06-0.22). Subsequently, quantitative real-time PCR (qRT-PCR) and western blotting indicated that DEPDC1 was high expressed in NSCLC cells. According to the in vitro MTS and apoptotic assays, downregulated DEPDC1 expression targeting P53 signaling pathway inhibited the proliferation of NSCLC cells while promoting apoptosis of NSCLC cells. Moreover, DEPDC1 was significantly correlated with immune cell infiltrating levels in NSCLC based on TCGA data, which were primarily associated with T cells CD4 memory activated, macrophages M1, B cells memory, mast cells resting, T cells regulatory, monocytes, and T cells CD4 memory resting. Compared with the group with high expression of DEPDC1, the group with low expression level had higher scores for immune checkpoint inhibitors (ICIs) treatment. GSEA confirmed that DEPDC1 was involved in gene expression and tumor-related signaling pathways. Finally, DEPDC1 and its associated immune-related genes were shown to be enriched in 'receptor ligand activity', 'external side of plasma membrane', 'regulation of innate immune response', and 'Epstein-Barr virus infection' pathways. The present study demonstrates that DEPDC1 may contribute to NSCLC tumorigenesis and can be applied as the biomarker for diagnosis and immunology.

Comparison between next-generation sequencing and multiplex polymerase chain reaction assays for nonsmall-cell lung cancer molecular diagnosis.

PURPOSE: Genetic mutation detection has become an important step in nonsmall-cell lung cancer (NSCLC) treatment because of the increasing number of drugs that target genomic rearrangements. A multiplex test that can detect multiple gene mutations prior to treatment is thus necessary. Currently, either next-generation sequencing (NGS)-based or polymerase chain reaction (PCR)-based tests are used. We evaluated the performance of the Oncomine Dx Target Test (ODxTT), an NGS-based multiplex biomarker panel test, and the AmoyDx Pan Lung Cancer PCR Panel (AmoyDx PLC panel), a real-time PCR-based multiplex biomarker panel test. MATERIALS AND METHODS: Patients with histologically diagnosed NSCLC and a sufficient sample volume to simultaneously perform the AmoyDx PLC panel and ODxTT-M were included in the study. The success and detection rates of both tests were evaluated. RESULTS: Biopsies revealed 116 cases of malignancies, 100 of which were NSCLC. Of these, 59 met the inclusion criteria and were eligible for analysis. The success rates were 100% and 98% for AmoyDx PLC panel and ODxTT-M, respectively. Nine driver mutations were detected in 35.9% and 37.3% of AmoyDx PLC and ODxTT-M panels, respectively. EGFR mutations were detected in 14% and 12% of samples using the AmoyDx PLC panel and ODxTT-M, respectively. Of the 58 cases in which both NGS and AmoyDx PLC panels were successful, discordant results were observed in seven cases. These differences were mainly due to different sensitivities of the detection methods used and the gene variants targeted in each test. DISCUSSION: The AmoyDx PLC panel, a PCR-based multiplex diagnostic test, exhibits a high success rate. The frequency of the nine genes targeted for treatment detected by the AmoyDx PLC panel was comparable to the frequency of mutations detected by ODxTT-M. Clinicians should understand and use the AmoyDx PLC panel and ODxTT-M with respect to their respective performances and limitations.

High CASC expression predicts poor prognosis of lung cancer: A systematic review with meta-analysis.

BACKGROUND: The long non-coding RNA cancer susceptibility candidate (CASC) has abnormal expression in lung cancer tissues and may correlate with lung cancer prognosis. This study aimed to comprehensively evaluate the association between CASC expression and the cancer prognosis. METHODS: PubMed, Embase, Web of Science, Google Scholar, Cochrane Library, and China National Knowledge Infrastructure databases were searched until April 1, 2023, to obtain the relevant literature. Studies that met the predefined eligibility criteria were included, and their quality was independently assessed by 2 investigators according to the Newcastle-Ottawa Scale (NOS) score. Detailed information was obtained, such as first author, year of publication, and number of patients. Hazard ratio (HR) with a 95% confidence interval (CI) was extracted and grouped to assess the relationship between CASC expression and cancer prognosis. The dichotomous data was merged and shown as the odds ratio (OR) with a 95% CI was extracted to assess the relationship between CASC expression and clinicopathological parameters. RESULTS: A total of 12 studies with 746 patients with lung cancer were included in the meta-analysis. The expression levels of lncRNA CASC2 and CASC7 were decreased, while those of CASC9, 11, 15, and 19 were induced in lung cancer tissues compared with paracancerous tissues. In the population with low CASC expression (CASC2 and CASC7), high CASC expression indicated a good lung cancer prognosis (HR = 0.469; 95% CI, 0.271-0.668). Conversely, in the population with high CASC expression (CASC9, 11, 15, and 19), high CASC expression predicted a poor lung cancer outcome (HR = 1.910; 95% CI, 1.628-2.192). High CASC expression also predicted worse disease-free survival (DFS) (HR = 2.803; 95% CI, 1.804-6.319). Combined OR with 95% CI revealed an insignificant positive association between high CASC expression and advanced TNM stage (OR = 1.061; 95% CI, 0.775-1.454), LNM (OR = 0.962; 95% CI, 0.724-1.277), tumor size (OR = 0.942; 95% CI, 0.667-1.330), and histological grade (OR = 1.022; 95% CI, 0.689-1.517). CONCLUSION: The CASC expression levels negatively correlate with lung cancer prognosis. Therefore, CASC expression may serve as a prognostic marker and a potential therapeutic target for lung cancer.

Evaluation of the preoperative neutrophil-to-lymphocyte ratio as a predictor of the micropapillary component of stage IA lung adenocarcinoma.

OBJECTIVE: To assess the ability of markers of inflammation to identify the solid or micropapillary components of stage IA lung adenocarcinoma and their effects on prognosis. METHODS: We performed a retrospective study of clinicopathologic data from 654 patients with stage IA lung adenocarcinoma collected between 2013 and 2019. Logistic regression analysis was used to identify independent predictors of these components, and we also evaluated the relationship between markers of inflammation and recurrence. RESULTS: Micropapillary-positive participants had high preoperative neutrophil-to-lymphocyte ratios. There were no significant differences in the levels of markers of systemic inflammation between the participants with or without a solid component. Multivariate analysis showed that preoperative neutrophil-to-lymphocyte ratio (odds ratio [OR] = 2.094; 95% confidence interval [CI], 1.668-2.628), tumor size (OR = 1.386; 95% CI, 1.044-1.842), and carcinoembryonic antigen concentration (OR = 1.067; 95% CI, 1.017-1.119) were independent predictors of a micropapillary component. There were no significant correlations between markers of systemic inflammation and the recurrence of stage IA lung adenocarcinoma. CONCLUSIONS: Preoperative neutrophil-to-lymphocyte ratio independently predicts a micropapillary component of stage IA lung adenocarcinoma. Therefore, the potential use of preoperative neutrophil-to-lymphocyte ratio in the optimization of surgical strategies for the treatment of stage IA lung adenocarcinoma should be further studied.

Six-Gene Signature for Differential Diagnosis and Therapeutic Decisions in Non-Small-Cell Lung Cancer-A Validation Study.

Non-small-cell lung cancer (NSCLC) poses a challenge due to its heterogeneity, necessitating precise histopathological subtyping and prognostication for optimal treatment decision-making. Molecular markers emerge as a potential solution, overcoming the limitations of conventional methods and supporting the diagnostic-therapeutic interventions. In this study, we validated the expression of six genes (MIR205HG, KRT5, KRT6A, KRT6C, SERPINB5, and DSG3), previously identified within a 53-gene signature developed by our team, utilizing gene expression microarray technology. Real-time PCR on 140 thoroughly characterized early-stage NSCLC samples revealed substantial upregulation of all six genes in squamous cell carcinoma (SCC) compared to adenocarcinoma (ADC), regardless of clinical factors. The decision boundaries of the logistic regression model demonstrated effective separation of the relative expression levels between SCC and ADC for most genes, excluding KRT6C. Logistic regression and gradient boosting decision tree classifiers, incorporating all six validated genes, exhibited notable performance (AUC: 0.8930 and 0.8909, respectively) in distinguishing NSCLC subtypes. Nevertheless, our investigation revealed that the gene expression profiles failed to yield predictive value regarding the progression of early-stage NSCLC. Our molecular diagnostic models manifest the potential for an exhaustive molecular characterization of NSCLC, subsequently informing personalized treatment decisions and elevating the standards of clinical management and prognosis for patients.

Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.