Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

Homogentisic acid metabolism inhibits papillary thyroid carcinoma proliferation through ROS and p21-induced cell cycle arrest

Thyroid cancer is one of the most common primary endocrine malignancies worldwide, and papillary thyroid carcinoma (PTC) is the predominant histological type observed therein. Although PTC has been studied extensively, our understanding of the altered metabolism and metabolic profile of PTC tumors is limited. We identified that the content of metabolite homogentisic acid (HGA) in PTC tissues was lower than that in adjacent non-cancerous tissues. We evaluated the potential of HGA as a novel molecular marker in the diagnosis of PTC tumors, as well as its ability to indicate the degree of malignancy. Studies have further shown that HGA contributes to reactive oxygen species (ROS) associated oxidative stress, leading to toxicity and inhibition of proliferation. In addition, HGA caused an increase in p21 expression levels in PTC cells and induced G1 arrest. Moreover, we found that the low HGA content in PTC tumors was due to the low expression levels of tyrosine aminotransferase (TAT) and p-hydroxyphenylpyruvate hydroxylase (HPD), which catalyze the conversion of tyrosine to HGA. The low expression levels of TAT and HPD are strongly associated with a higher probability of PTC tumor invasion and metastasis. Our study demonstrates that HGA could be used to diagnose PTC and provides mechanisms linking altered HGA levels to the biological behavior of PTC tumors.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Diagnosis of Malignant Pulmonary Nodules Using a Combination of Tumor-associated Autoantibodies and Computed Tomography

BACKGROUND

Diagnosis of malignant pulmonary nodules can greatly reduce the occurrence of lung cancer death, and computed tomography (CT) is commonly used in diagnosis. In addition, tumor-associated autoantibodies (TAAbs) show high specificity and stability. We aim to establish a computable risk model of pulmonary nodules by combining CT with TAAb detection.

METHODS

The concentrations of 7 TAAbs (p53, PGP9.5, SOX2, GAGE7, GBU4-5, CAGE, MAGEA1, and CAGE) were assayed using the enzyme-linked immunosorbent assay in 136 patients with pulmonary nodules (84 with newly diagnosed lung adenocarcinoma, 21 with squamous cell carcinoma, and 31 with benign nodules) and 42 control subjects without pulmonary nodules. We then drew receiver operating characteristic curves and conducted logistic regression to analyze the diagnostic efficiency of our method in the detection of lung cancer.

RESULTS

The positivity rate of the 7 TAAbs was 49.5%, and the specificity was 83.6%. Our regression results indicated 65% overall accuracy, 44.76% sensitivity, and 76.71% specificity. Notably, when combined with CT imaging and the demographic characteristics, diagnostic accuracy increased to 73.4%, sensitivity to 61.5%, and specificity to 87.1%. The positive predictive value and negative predictive value were 93% and 41%, respectively.

CONCLUSION

Our study provides a method that combines 7 serum TAAbs with imaging and demographic characteristics to diagnose malignant pulmonary nodules more accurately than existing methods.

Screening for lung cancer with computed tomography: protocol for systematic reviews for the Canadian Task Force on Preventive Health Care

PURPOSE

Lung cancer is the leading cause of cancer deaths in Canada, and because early cancers are often asymptomatic screening aims to prevent mortality by detecting cancer earlier when treatment is more likely to be curative. These reviews will inform updated recommendations by the Canadian Task Force on Preventive Health Care on screening for lung cancer.

METHODS

We will update the review on the benefits and harms of screening with CT conducted for the task force in 2015 and perform de novo reviews on the comparative effects between (i) trial-based selection criteria and use of risk prediction models and (ii) trial-based nodule classification and different nodule classification systems and on patients' values and preferences. We will search Medline, Embase, and Cochrane Central (for questions on benefits and harms from 2015; comparative effects from 2012) and Medline, Scopus, and EconLit (for values and preferences from 2012) via peer-reviewed search strategies, clinical trial registries, and the reference lists of included studies and reviews. Two reviewers will screen all citations (including those in the previous review) and base inclusion decisions on consensus or arbitration by another reviewer. For benefits (i.e., all-cause and cancer-specific mortality and health-related quality of life) and harms (i.e., overdiagnosis, false positives, incidental findings, psychosocial harms from screening, and major complications and mortality from invasive procedures as a result of screening), we will include studies of adults in whom lung cancer is not suspected. We will include randomized controlled trials comparing CT screening with no screening or alternative screening modalities (e.g., chest radiography) or strategies (e.g., CT using different screening intervals, classification systems, and/or patient selection via risk models or biomarkers); non-randomized studies, including modeling studies, will be included for the comparative effects between trial-based and other selection criteria or nodule classification methods. For harms (except overdiagnosis) we will also include non-randomized and uncontrolled studies. For values and preferences, the study design may be any quantitative design that either directly or indirectly measures outcome preferences on outcomes pertaining to lung cancer screening. We will only include studies conducted in Very High Human Development Countries and having full texts in English or French. Data will be extracted by one reviewer with verification by another, with the exception of result data on mortality and cancer incidence (for calculating overdiagnosis) where duplicate extraction will occur. If two or more studies report on the same comparison and it is deemed suitable, we will pool continuous data using a mean difference or standardized mean difference, as applicable, and binary data using relative risks and a DerSimonian and Laird model unless events are rare (< 1%) where we will pool odds ratios using Peto's method or (if zero events) the reciprocal of the opposite treatment arm size correction. For pooling proportions, we will apply suitable transformation (logit or arcsine) depending on the proportions of events. If meta-analysis is not undertaken we will synthesize the data descriptively, considering clinical and methodological differences. For each outcome, two reviewers will independently assess within- and across-study risk of bias and rate the certainty of the evidence using GRADE (Grading of Recommendations Assessment, Development, and Evaluation), and reach consensus.

DISCUSSION

Since 2015, additional trials and longer follow-ups or additional data (e.g., harms, specific patient populations) from previously published trials have been published that will improve our understanding of the benefits and harms of screening. The systematic review of values and preferences will allow fulsome insights that will inform the balance of benefits and harms.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022378858.

New clinical trial design in precision medicine: discovery, development and direction

In the era of precision medicine, it has been increasingly recognized that individuals with a certain disease are complex and different from each other. Due to the underestimation of the significant heterogeneity across participants in traditional "one-size-fits-all" trials, patient-centered trials that could provide optimal therapy customization to individuals with specific biomarkers were developed including the basket, umbrella, and platform trial designs under the master protocol framework. In recent years, the successive FDA approval of indications based on biomarker-guided master protocol designs has demonstrated that these new clinical trials are ushering in tremendous opportunities. Despite the rapid increase in the number of basket, umbrella, and platform trials, the current clinical and research understanding of these new trial designs, as compared with traditional trial designs, remains limited. The majority of the research focuses on methodologies, and there is a lack of in-depth insight concerning the underlying biological logic of these new clinical trial designs. Therefore, we provide this comprehensive review of the discovery and development of basket, umbrella, and platform trials and their underlying logic from the perspective of precision medicine. Meanwhile, we discuss future directions on the potential development of these new clinical design in view of the "Precision Pro", "Dynamic Precision", and "Intelligent Precision". This review would assist trial-related researchers to enhance the innovation and feasibility of clinical trial designs by expounding the underlying logic, which be essential to accelerate the progression of precision medicine.

T790M mutation upconversion fluorescence biosensor via mild ATRP strategy and site-specific DNA cleavage of restriction endonuclease

A unique combination of a specific nucleic acid restriction endonuclease (REase) and atom transfer radical polymerization (ATRP) signal amplification strategy was employed for the detection of T790M mutations prevalent in the adjuvant diagnosis of lung cancer. REase selectively recognizes and cleaves T790M mutation sites on double-stranded DNA formed by hybridization of a capture sequence and a target sequence. At the same time, the ATRP strategy resulted in the massive aggregation of upconverted nanoparticles (UCNPs), which significantly improved the sensitivity of the biosensor. In addition, the UCNPs have excellent optical properties and can eliminate the interference of autofluorescence in the samples, thus further improving the detection sensitivity. The proposed upconversion fluorescent biosensor is characterized by high specificity, high sensitivity, mild reaction conditions, fast response time, and a detection limit as low as 0.14 fM. The performance of the proposed biosensor is comparable to that of clinical PCR methods when applied to clinical samples. This work presents a new perspective for assisted diagnosis in the pre-intervention stage of tumor diagnostics in the early stage of precision oncology treatments.

Glycan-specific molecularly imprinted polymers towards cancer diagnostics: merits, applications, and future perspectives

Aberrant glycans are a hallmark of cancer states. Notably, emerging evidence has demonstrated that the diagnosis of cancers with tumour-specific glycan patterns holds great potential to address unmet medical needs, especially in improving diagnostic sensitivity and selectivity. However, despite vast glycans having been identified as potent markers, glycan-based diagnostic methods remain largely limited in clinical practice. There are several reasons that prevent them from reaching the market, and the lack of anti-glycan antibodies is one of the most challenging hurdles. With the increasing need for accelerating the translational process, numerous efforts have been made to find antibody alternatives, such as lectins, boronic acids and aptamers. However, issues concerning affinity, selectivity, stability and versatility are yet to be fully addressed. Molecularly imprinted polymers (MIPs), synthetic antibody mimics with tailored cavities for target molecules, hold the potential to revolutionize this dismal progress. MIPs can bind a wide range of glycan markers, even those without specific antibodies. This capacity effectively broadens the clinical applicability of glycan-based diagnostics. Additionally, glycoform-resolved diagnosis can also be achieved through customization of MIPs, allowing for more precise diagnostic applications. In this review, we intent to introduce the current status of glycans as potential biomarkers and critically evaluate the challenges that hinder the development of in vitro diagnostic assays, with a particular focus on glycan-specific recognition entities. Moreover, we highlight the key role of MIPs in this area and provide examples of their successful use. Finally, we conclude the review with the remaining challenges, future outlook, and emerging opportunities.

Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer

Plasma proteins are promising biomarkers and potential drug targets in lung cancer. To evaluate the causal association between plasma proteins and lung cancer, we performed proteome-wide Mendelian randomization meta-analysis (PW-MR-meta) based on lung cancer genome-wide association studies (GWASs), protein quantitative trait loci (pQTLs) of 4,719 plasma proteins in deCODE and 4,775 in Fenland. Further, causal-protein risk score (CPRS) was developed based on causal proteins and validated in the UK Biobank. 270 plasma proteins were identified using PW-MR meta-analysis, including 39 robust causal proteins (both FDR-q < 0.05) and 78 moderate causal proteins (FDR-q < 0.05 in one and p < 0.05 in another). The CPRS had satisfactory performance in risk stratification for lung cancer (top 10% CPRS:Hazard ratio (HR) (95%CI):4.33(2.65-7.06)). The CPRS [AUC (95%CI): 65.93 (62.91-68.78)] outperformed the traditional polygenic risk score (PRS) [AUC (95%CI): 55.71(52.67-58.59)]. Our findings offer further insight into the genetic architecture of plasma proteins for lung cancer susceptibility.

Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers

Current lung cancer screening (LCS) guidelines rely on age and smoking history. Despite its benefit, only 5%-15% of eligible patients receive LCS. Personalized screening strategies select individuals based on their lung cancer risk and may increase LCS's effectiveness. We assess current LCS practices and the acceptability of personalized LCS among primary care providers (PCP) in Texas. We surveyed 32,983 Texas-based PCPs on an existing network (Protocol 2019-1257; PI: Dr. Shete) and 300 attendees of the 2022 Texas Academy of Family Physicians (TAFP) conference. We analyzed the responses by subgroups of interest. Using nonparametric bootstrap, we derived an enriched dataset to develop logistic regression models to understand current LCS practices and acceptability of personalized LCS. Response rates were 0.3% (n = 91) and 15% (n = 60) for the 2019-1257 and TAFP surveys, respectively. Most (84%) respondents regularly assess LCS in their practice. Half of the respondents were interested in adopting personalized LCS. The majority (66%) of respondents expressed concerns regarding time availability with the personalized LCS. Most respondents would use biomarkers as an adjunct to assess eligibility (58%), or to help guide indeterminate clinical findings (63%). There is a need to enhance the engagement of Texas-based PCPs in LCS. Most of the respondents expressed interest in personalized LCS. Time availability was the main concern related to personalized LCS. Findings from this project highlight the need for better education of Texas-based PCPs on the benefits of LCS, and the development of efficient decision tools to ensure successful implementation of personalized LCS.

PREVENTION RELEVANCE

Personalized LCS facilitated by a risk model and/or a biomarker test is proposed as an alternative to existing programs. Acceptability of personalized approach among PCPs is unknown. The goal of this study is to assess the acceptability of personalized LCS among PCPs.

Mediation of association between benzo[a]pyrene exposure and lung cancer risk by plasma microRNAs: A Chinese case-control study

Epidemiologic studies have reported the positive relationship of benzo[a]pyrene (BaP) exposure with the risk of lung cancer. However, the mechanisms underlying the relationship is still unclear. Plasma microRNA (miRNA) is a typical epigenetic biomarker that was linked to environment exposure and lung cancer development. We aimed to reveal the mediation effect of plasma miRNAs on BaP-related lung cancer. We designed a lung cancer case-control study including 136 lung cancer patients and 136 controls, and measured the adducts of benzo[a]pyrene diol epoxide-albumin (BPDE-Alb) and sequenced miRNA profiles in plasma. The relationships between BPDE-Alb adducts, normalized miRNA levels and the risk of lung cancer were assessed by linear regression models. The mediation effects of miRNAs on BaP-related lung cancer were investigated. A total of 190 plasma miRNAs were significantly related to lung cancer status at Bonferroni adjusted P < 0.05, among which 57 miRNAs showed different levels with |fold change| > 2 between plasma samples before and after tumor resection surgery at Bonferroni adjusted P < 0.05. Especially, among the 57 lung cancer-associated miRNAs, BPDE-Alb adducts were significantly related to miR-17-3p, miR-20a-3p, miR-135a-5p, miR-374a-5p, miR-374b-5p, miR-423-5p and miR-664a-5p, which could in turn mediate a separate 42.2%, 33.0%, 57.5%, 36.4%, 48.8%, 32.5% and 38.2% of the relationship of BPDE-Alb adducts with the risk of lung cancer. Our results provide non-invasion biomarker candidates for lung cancer, and highlight miRNAs dysregulation as a potential intermediate mechanism by which BaP exposure lead to lung tumorigenesis.

Methylated Cell-Free Tumor DNA in Sputum as a Tool for Diagnosing Lung Cancer-A Systematic Review and Meta-Analysis

Lung cancer is the leading cause of cancer-related mortality worldwide. Early diagnosis is pivotal for the prognosis. There is a notable overlap between lung cancer and chronic bronchitis, and the potential use of methylated tumor DNA in sputum as a biomarker for lung cancer detection is appealing. This systematic review and meta-analysis followed the PRISMA 2020 statement. A comprehensive search was conducted in Embase, Medline, Web of Science, and the Cochrane Library, using these search strings: Lung cancer, sputum, and methylated tumor DNA. A total of 15 studies met the eligibility criteria. Studies predominantly utilized a case-control design, with sensitivity ranging from 10 to 93% and specificity from 8 to 100%. A meta-analysis of all genes across studies resulted in a summary sensitivity of 54.3% (95% CI 49.4-59.2%) and specificity of 79.7% (95% CI 75.0-83.7%). Notably, two less explored genes (TAC1, SOX17) demonstrated sensitivity levels surpassing 85%. The study's findings highlight substantial variations in the sensitivity and specificity of methylated tumor DNA in sputum for lung cancer detection. Challenges in reproducibility could stem from differences in tumor site, sample acquisition, extraction methods, and methylation measurement techniques. This meta-analysis provides a foundation for prioritizing high-performing genes, calling for a standardization and refinement of methodologies before potential application in clinical trials.

Characterizing the secretome of EGFR mutant lung adenocarcinoma

Background

Lung cancer is the leading cause of cancer related death worldwide, mainly due to the late stage of disease at the time of diagnosis. Non-invasive biomarkers are needed to supplement existing screening methods to enable earlier detection and increased patient survival. This is critical to EGFR-driven lung adenocarcinoma as it commonly occurs in individuals who have never smoked and do not qualify for current screening protocols.

Methods

In this study, we performed mass spectrometry analysis of the secretome of cultured lung cells representing different stages of mutant EGFR driven transformation, from normal to fully malignant. Identified secreted proteins specific to the malignant state were validated using orthogonal methods and their clinical activity assessed in lung adenocarcinoma patient cohorts.

Results

We quantified 1020 secreted proteins, which were compared for differential expression between stages of transformation. We validated differentially expressed proteins at the transcriptional level in clinical tumor specimens, association with patient survival, and absolute concentration to yield three biomarker candidates: MDK, GDF15, and SPINT2. These candidates were validated using ELISA and increased levels were associated with poor patient survival specifically in EGFR mutant lung adenocarcinoma patients.

Conclusions

Our study provides insight into changes in secreted proteins during EGFR driven lung adenocarcinoma transformation that may play a role in the processes that promote tumor progression. The specific candidates identified can harnessed for biomarker use to identify high risk individuals for early detection screening programs and disease management for this molecular subgroup of lung adenocarcinoma patients.

TFR1-Mediated Iron Metabolism Orchestrates Tumor Ferroptosis and Immunity in Non-Small Cell Lung Cancer

This study aimed to investigate the underlying molecular mechanisms of transferrin receptor (TFR1) in non-small cell lung cancer (NSCLC). Histological analysis was performed using hematoxylin-eosin (HE) staining. The number of CD8+ T cell were determined by flow cytometry and immunofluorescence assays. mRNA levels were analyzed by qRT-PCR. Protein expression was detected by western blot. Ferroptosis was detected by using propidium iodide (PI) staining. Xenograft experiment was applied for determining tumor growth. The results showed that interferon (IFN)-γ plus iron dextran (FeDx) induced iron overload and the ferroptosis of NSCLC cells. Moreover, IFN-γ-mediated upregulation of TFR1 promoted ferritinophagy and tumor cell ferroptosis via blocking via blocking ferritin heavy chain 1 (FTH1)/ ferritin light chain (FTL) signaling. However, TFR1 knockout suppressed the ferroptosis of tumor cells. Furthermore, FeDx-mediated iron overload promoted the sensitivity of anti-programmed death ligand 1 (PD-L1) therapies. Clinically, TFR1 was downregulated in NSCLC patients. Low levels of TFR1 predicted decreased CD8+ T cells. Taken together, IFN-γ combined with iron metabolism therapies may provide a novel alternative for NSCLC.

Lack of clinical utility of serum macrophage migration inhibitory factor (MIF) for monitoring therapy response and estimating prognosis in advanced lung cancer

BACKGROUND

Lung cancer is a major burden to global health and is still among the most frequent and most lethal malignant diseases. Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine involved in a variety of processes including tumorigenesis, formation of a tumor microenvironment and metastasis. It is therefore a potential prognostic biomarker in malignant diseases.

OBJECTIVE

In this study, we investigated the applicability of MIF in serum samples as a biomarker in lung cancer.

METHODS

In a retrospective approach, we analyzed the sera of 79 patients with non-small-cell lung cancer (NSCLC) and 14 patients with small-cell lung cancer (SCLC) before the start of chemotherapy, as well as before the second and third chemotherapy cycle, respectively. Serum MIF levels were measured using a sandwich immunoassay with a sulfo-tag-labelled detection antibody, while pro-gastrin releasing peptide (proGRP) levels were determined with an enzyme-linked immunosorbent assay.

RESULTS

No difference in serum MIF levels between responders and non-responders to chemotherapy was observed at all time points, while proGRP levels were significantly lower in responders before the second chemotherapy cycle (p = 0.012). No differences in biomarker levels depending on the histopathological classification of NSCLC patients was found. Moreover, in ROC curve analyses MIF was not able to distinguish between responders and non-responders to therapy. proGRP could differentiate between responders and non-responders before the second chemotherapy cycle (p = 0.015) with sensitivities of 43% at 90% and 95% specificity, respectively. Likewise, proGRP yielded significantly longer survival times of patients with low proGRP concentrations before the second chemotherapy cycle (p = 0.015) in Kaplan-Meier analyses, yet MIF showed no significant differences in survival times at all time points. Comparison with the biomarkers CEA and CYFRA 21-1 in the same cohort showed that these established biomarkers clearly performed superior to MIF and proGRP.

CONCLUSIONS

From the present results, there is no indication that serum MIF may serve as a biomarker in prognosis and monitoring of response to therapy in lung cancer. Limitations of this study include its retrospective design, the inclusion of a larger NSCLC and a smaller SCLC subgroup, the classical chemotherapeutic treatment, the use of a non-diagnostic immunoassay (RUO-test) for MIF measurement and the lack of a validation cohort. Strengths of the study are its highly standardized procedures concerning sample collection, preanalytic treatment, measurements and quality control of the laboratory assays.

Screening approaches for lung cancer by blood-based biomarkers: Challenges and opportunities

Lung cancer (LC) is one of the leading causes for cancer-related deaths in the world, accounting for 28% of all cancer deaths in Europe. Screening for lung cancer can enable earlier detection of LC and reduce lung cancer mortality as was demonstrated in several large image-based screening studies such as the NELSON and the NLST. Based on these studies, screening is recommended in the US and in the UK a targeted lung health check program was initiated. In Europe lung cancer screening (LCS) has not been implemented due to limited data on cost-effectiveness in the different health care systems and questions on for example the selection of high-risk individuals, adherence to screening, management of indeterminate nodules, and risk of overdiagnosis. Liquid biomarkers are considered to have a high potential to address these questions by supporting pre- and post- Low Dose CT (LDCT) risk-assessment thereby improving the overall efficacy of LCS. A wide variety of biomarkers, including cfDNA, miRNA, proteins and inflammatory markers have been studied in the context of LCS. Despite the available data, biomarkers are currently not implemented or evaluated in screening studies or screening programs. As a result, it remains an open question which biomarker will actually improve a LCS program and do this against acceptable costs. In this paper we discuss the current status of different promising biomarkers and the challenges and opportunities of blood-based biomarkers in the context of lung cancer screening.

Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer

Low-dose computed tomography (LDCT) screening for lung cancer substantially reduces mortality from lung cancer, as revealed in randomized controlled trials and meta-analyses. This review is based on the ninth CT screening symposium of the International Association for the Study of Lung Cancer, which focuses on the major themes pertinent to the successful global implementation of LDCT screening and develops a strategy to further the implementation of lung cancer screening globally. These recommendations provide a 5-year roadmap to advance the implementation of LDCT screening globally, including the following: (1) establish universal screening program quality indicators; (2) establish evidence-based criteria to identify individuals who have never smoked but are at high-risk of developing lung cancer; (3) develop recommendations for incidentally detected lung nodule tracking and management protocols to complement programmatic lung cancer screening; (4) Integrate artificial intelligence and biomarkers to increase the prediction of malignancy in suspicious CT screen-detected lesions; and (5) standardize high-quality performance artificial intelligence protocols that lead to substantial reductions in costs, resource utilization and radiologist reporting time; (6) personalize CT screening intervals on the basis of an individual's lung cancer risk; (7) develop evidence to support clinical management and cost-effectiveness of other identified abnormalities on a lung cancer screening CT; (8) develop publicly accessible, easy-to-use geospatial tools to plan and monitor equitable access to screening services; and (9) establish a global shared education resource for lung cancer screening CT to ensure high-quality reading and reporting.

The diagnostic performance of micro-RNA and metabolites in lung cancer: A meta-analysis

BACKGROUND

The diagnosis of lung cancer is based on the microscopic exam of tissue or liquid. During the recent decade, many biomarkers have been pointed to have a potential diagnostic role. These biomarkers may be assessed in blood, pleural effusion or sputum and they could avoid biopsies or other risky procedures. The authors aimed to assess the diagnostic performances of biomarkers focusing on micro-RNA and metabolites.

METHODS

This meta-analysis was conducted under the PRISMA guidelines during a nine-year-period (2013-2022). the Meta-Disc software 5.4 (free version) was used. Q test and I2 statistics were carried out to explore the heterogeneity among studies. Meta-regression was performed in case of significant heterogeneity. Publication bias was assessed using the funnel plot test and the Egger's test (free version JASP).

RESULTS

According to our inclusion criteria, 165 studies from 79 articles were included. The pooled SEN, SPE and dOR accounted, respectively, for 0.76, 0.79 and 13.927. The AUC was estimated to 0.859 suggesting a good diagnostic accuracy. The heterogeneity in the pooled SEN and SPE was statistically significant. The meta-regression analysis focusing on the technique used, the sample, the number of biomarkers, the biomarker subtype, the tumor stage and the ethnicity revealed the biomarker number (p  =  0.009) and the tumor stage (p  =  0.0241) as potential sources of heterogeneity.

CONCLUSION

Even if this meta-analysis highlighted the potential diagnostic utility of biomarkers, more prospective studies should be performed, especially to assess the biomarkers' diagnostic potential in early-stage lung cancers.

Ultrasensitive immunosensor for multiplex detection of cancer biomarkers carcinoembryonic antigen (CEA) and yamaguchi sarcoma viral oncogene homolog 1 (YES1) based on eco-friendly synthesized gold nanoparticles

Cancer is one of the most extensive diseases with the highest mortality rate, accounting for almost 10 million deaths in 2020. The most common cancers are breast, lung, colon and rectum and prostate cancers. Of these, lung cancer, accounted for about 1.8 million of all cancer deaths (25%) in 2020. Detection of cancer relies on presence of biomarkers such as DNA molecules, proteins and metabolites released by cancerous cells into the circulation. Carcinoembryonic antigen (CEA) is one of the biomarkers that has been used for the detection of lung cancer. However, CEA is not specific to lung cancer since it is also manifested in gastric cancer, pancreatic cancer, colorectal cancer, and breast cancer. Recently, v-YES1 Yamaguchi sarcoma viral oncogene homolog 1 (YES1) was described as a specific biomarker for lung cancer. The detection of both CEA and YES1 would give more precise and authentic information for detecting lung cancer. This is because detection of a single tumor marker usually limits the precision in tumor diagnosis, due to the fact that several cancers have more than one marker linked with their prevalence. Whereas traditional methods have been used for the detection of CEA, electrochemical immunosensors have attracted considerable attention owing to their profound advantages, including fast response, miniaturization, high selectivity, low sample requirements and magnificent sensitivity. The fabrication of a multiplex and simultaneous immunosensor is met with challenge of preparation of distinguishable immunoprobes with different redox activities. This can be addressed by incorporation of electroactive Nano metals into the sensing platform. In this study, gold nanoparticles were used for the fabrication of an ultrasensitive sandwich electrochemical multiplex immunosensor for simultaneous detection of CEA and YES1. Under optimized conditions, the electrochemical immunosensor detection limit for YES1 and CEA was found to be 0.0022 and 0.0034 ng/mL respectively within a linear range of 0.1-50 ng/mL. The proposed immunosensor proved to be stable for up to 2 weeks and had negligible cross reactivity towards various interfering compounds in human plasma. This study reports that gold nanoparticles can be bio synthesized using shade dried Mangifera indica leaves extract. The bio-synthesized gold nanoparticles coupled with thiolated protein G can be used for fabrication of a multiplex immunosensor for detection of CEA and YES1. The proposed immunosensor can provide a new approach for early diagnosis of circulating cancer biomarkers and holds great promise for application in clinical diagnosis.

High SNHG expression may predict a poor lung cancer prognosis based on a meta-analysis

BACKGROUND

An increasing number of small nucleolar RNA host genes (SNHGs) have been revealed to be dysregulated in lung cancer tissues, and abnormal expression of SNHGs is significantly correlated with the prognosis of lung cancer. The purpose of this study was to conduct a meta-analysis to explore the correlation between the expression level of SNHGs and the prognosis of lung cancer.

METHODS

A comprehensive search of six related databases was conducted to obtain relevant literature. Relevant information, such as overall survival (OS), progression-free survival (PFS), TNM stage, lymph node metastasis (LNM), and tumor size, was extracted. Hazard ratios (HRs) and 95% confidence intervals (CIs) were pooled to evaluate the relationship between SNHG expression and the survival outcome of lung cancers. Sensitivity and publication bias analyses were performed to explore the stability and reliability of the overall results.

RESULTS

Forty publications involving 2205 lung cancer patients were included in this meta-analysis. The pooled HR and 95% CI values indicated a significant positive association between high SNHG expression and poor OS (HR: 1.890, 95% CI: 1.595-2.185), disease-free survival (DFS) (HR: 2.31, 95% CI: 1.57-3.39) and progression-free survival (PFS) (HR: 2.01, 95% CI: 0.66-6.07). The pooled odds ratio (OR) and 95% CI values indicated that increased SNHG expression may be correlated with advanced TNM stage (OR: 1.509, 95% CI: 1.267-1.799), increase risk of distant lymph node metastasis (OR: 1.540, 95% CI: 1.298-1.828), and large tumor size (OR: 1.509, 95% CI: 1.245-1.829). Sensitivity analysis and publication bias results showed that each result had strong reliability and robustness, and there was no significant publication bias or other bias.

CONCLUSION

Most SNHGs are upregulated in lung cancer tissues, and high expression of SNHGs predicts poor survival outcomes in lung cancer. SNHGs may be potential prognostic markers and promising therapeutic targets.

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.

A diagnostic biomarker of acid glycoprotein 1 for distinguishing malignant from benign pulmonary lesions

BACKGROUND

The acid glycoprotein 1 (AGP1) is downregulated in lung cancer. However, the performance of AGP1 in distinguishing benign from malignant lung lesions is still unknown.

METHODS

The expression of AGP1 in benign diseases and lung cancer samples was detected by Western blot. The receiver operating characteristic curves, bivariate correlation, and multivariate analysis was analyzed by SPSS software.

RESULTS

AGP1 expression levels were significantly downregulated in lung cancer and correlated with carcinoembryonic antigen (CEA), CA199, and CA724 tumor biomarkers. The diagnostic performance of AGP1 for distinguishing malignant from benign pulmonary lesions was better than the other four clinical biomarkers including CEA, squamous cell carcinoma-associated antigen, neuron-specific enolase, and cytokeratin 19 fragment 21-1, with an area under the curve value of 0.713 at 88.8% sensitivity. Furthermore, the multivariate analysis indicated that the variates of thrombin time and potassium significantly affected the AGP1 levels in lung cancer.

CONCLUSIONS

Our study indicates that AGP1 expression is decreased in lung cancer compared to benign samples, which helps distinguish benign and malignant pulmonary lesions.

Epigenetic regulation in lung cancer

Lung cancer is indeed a major cause of cancer-related deaths worldwide. The development of tumors involves a complex interplay of genetic, epigenetic, and environmental factors. Epigenetic mechanisms, including DNA methylation (DNAm), histone modifications, and microRNA expression, play a crucial role in this process. Changes in DNAm patterns can lead to the silencing of important genes involved in cellular functions, contributing to the development and progression of lung cancer. MicroRNAs and exosomes have also emerged as reliable biomarkers for lung cancer. They can provide valuable information about early diagnosis and treatment assessment. In particular, abnormal hypermethylation of gene promoters and its effects on tumorigenesis, as well as its roles in the Wnt signaling pathway, have been extensively studied. Epigenetic drugs have shown promise in the treatment of lung cancer. These drugs target the aberrant epigenetic modifications that are involved in the development and progression of the disease. Several factors have been identified as drug targets in non-small cell lung cancer. Recently, combination therapy has been discussed as a successful strategy for overcoming drug resistance. Overall, understanding the role of epigenetic mechanisms and their targeting through drugs is an important area of research in lung cancer treatment.

Increased expression of individual genes in whole blood is associated with late-stage lung cancer at and close to diagnosis

Lung cancer (LC) mortality rates are still increasing globally. As survival is linked to stage, there is a need to identify markers for earlier LC diagnosis and individualized treatment. The whole blood transcriptome of LC patients represents a source of potential LC biomarkers. We compared expression of > 60,000 genes in whole blood specimens taken from LC cases at diagnosis (n = 128) and controls (n = 62) using genome-wide RNA sequencing, and identified 14 candidate genes associated with LC. High expression of ANXA3, ARG1 and HP was strongly associated with lower survival in late-stage LC cases (hazard ratios (HRs) = 2.81, 2.16 and 2.54, respectively). We validated these markers in two independent population-based studies with pre-diagnostic whole blood specimens taken up to eight years prior to LC diagnosis (n = 163 cases, 184 matched controls). ANXA3 and ARG1 expression was strongly associated with LC in these specimens, especially with late-stage LC within two years of diagnosis (odds ratios (ORs) = 3.47 and 5.00, respectively). Additionally, blood CD4 T cells, NK cells and neutrophils were associated with LC at diagnosis and improved LC discriminative ability beyond candidate genes. Our results indicate that in whole blood, increased expression levels of ANXA3, ARG1 and HP are diagnostic and prognostic markers of late-stage LC.

Cancer Relevance of Circulating Antibodies Against LINE-1 Antigens in Humans

Long interspersed nuclear element-1 (LINE-1 or L1), the most abundant family of autonomous retrotransposons occupying over 17% of human DNA, is epigenetically silenced in normal tissues by the mechanisms involving p53 but is frequently derepressed in cancer, suggesting that L1-encoded proteins may act as tumor-associated antigens recognized by the immune system. In this study, we established an immunoassay to detect circulating autoantibodies against L1 proteins in human blood. Using this assay in >2,800 individuals with or without cancer, we observed significantly higher IgG titers against L1-encoded ORF1p and ORF2p in patients with lung, pancreatic, ovarian, esophageal, and liver cancers than in healthy individuals. Remarkably, elevated levels of anti-ORF1p-reactive IgG were observed in patients with cancer with disease stages 1 and 2, indicating that the immune response to L1 antigens can occur in the early phases of carcinogenesis. We concluded that the antibody response against L1 antigens could contribute to the diagnosis and determination of immunoreactivity of tumors among cancer types that frequently escape early detection.

SIGNIFICANCE

The discovery of autoantibodies against antigens encoded by L1 retrotransposons in patients with five poorly curable cancer types has potential implications for the detection of an ongoing carcinogenic process and tumor immunoreactivity.

Clinical Adjuncts to Lung Cancer Screening: A Narrative Review

The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.

Prospective Detection of Early Lung Cancer in Patients With COPD in Regular Care by Electronic Nose Analysis of Exhaled Breath

BACKGROUND

Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD.

RESEARCH QUESTION

Can eNose technology be used for prospective detection of early lung cancer in patients with COPD?

STUDY DESIGN AND METHODS

BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis.

RESULTS

Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95).

INTERPRETATION

Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.

A circRNA-based ceRNA network shows its diagnostic value in non-small-cell lung cancer

BACKGROUND

Numerous studies have reported the vital roles of circular RNA (circRNA)-based competitive endogenous RNA (ceRNA) regulatory networks in cancers. Here, we established a non-small-cell lung cancer (NSCLC)-related circRNA-miRNA-mRNA axis and estimated its diagnostic value in NSCLC.

METHODS

The circ_0061235-miR-3180-5p-PPM1L axis was constructed by small RNA deep sequencing, bioinformatics databases, and preliminary testing. The serum levels of the selected circ_0061235, miR-3180-5p, and PPM1L were quantified using quantitative polymerase chain reaction. Receiver operating characteristic analyses were conducted to evaluate the diagnostic power.

RESULTS

The levels of circ_0061235, miR-3180-5p, and PPM1L showed close correlations according to the ceRNA regulation rule. They were significantly dysregulated in NSCLC and showed the diagnostic ability to discriminate between healthy and NSCLC, and remarkably, between benign lung tumors and NSCLC. Additionally, the down-regulated levels of hsa_circ_0061235, the up-regulated levels of miR-3180-5p, and the decreased levels of PPM1L were correlated to more aggressive features of NSCLC, such as lymph node metastasis, distant metastasis, and higher stages. Intriguingly, compared to the single circ_0061235, miR-3180-5p, PPM1L, and traditional tumor markers, the diverse combinations of circ_0061235, miR-3180-5p, and PPM1L showed much higher sensitivity and specificity to differentiate greater or lesser severity of NSCLC. GO annotation and KEGG pathway analyses revealed the underlying role of the circ_0061235-miR-3180-5p-PPM1L axis in NSCLC.

CONCLUSIONS

We established a specific circRNA-miRNA-mRNA network with higher sensitivity and specificity to diagnose NSCLC, particularly more aggressive NSCLC, providing a new strategy for further developing tumor biomarkers.

Predictive value of 18 F-FDG PET/CT and serum tumor markers for tumor mutational burden in patients with non-small cell lung cancer

PURPOSE

To investigate the correlations between metabolic parameters (MPs) of 18 F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT), serum tumor markers (STMs), and tumor mutational burden (TMB) in patients with non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

In this retrospective study, we enrolled 129 patients with NSCLC (males, 78; females, 51) who underwent baseline TMB and STM tests and 18 F-FDG PET/CT scans before treatment between March 2018 and September 2022. Patients were categorized into TMB-high (TMB ≥10 mutations/Mb; n = 27 [20.9%]) and non-TMB-high (TMB <10 mutations/Mb; n = 102 [79.1%]) groups. Binary logistic regression analyses were performed to determine independent predictors of TMB-high. Univariate and multivariate linear regression analyses were performed to determine independent predictors of TMB level on a log scale. Subgroup analyses for adenocarcinoma (ADC), ADC with EGFR+, ADC with EGFR-, and squamous cell carcinoma (SCC) were performed.

RESULTS

For ADC, all MPs (SULpeak , SULmax , SULmean , MTV, and TLG) were significantly higher in the TMB-high group than the non-TMB-high group; smoker (odds ratio [OR] = 27.08, p = 0.018), EGFR+ (OR = 0.03, p = 0.033), KRAS+ (OR = 7.98, p = 0.083), high CEA (OR = 33.56, p = 0.029), and high CA125 (OR = 13.68, p = 0.030) were independent predictors of TMB-high; and all MPs showed significant positive linear correlations with TMB on a log scale, with SULpeak as an independent predictor. However, no significant correlation was observed for SCC.

CONCLUSION

MPs and STMs can predict the TMB level for patients with ADC, and may serve as potential substitutes for TMB with increased value and easy implementation in guiding immunotherapy through noninvasive methods.

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

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

Background and Objective

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.

Methods

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).

Key Content and Findings

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.

Conclusions

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.

Theranostic signature of tumor-derived exosomes in cancer

Cancer is the most challenging global health crisis. In the recent times, studies on extracellular vesicles (EVs) are adding a new chapter to cancer research and reports on EVs explores cancer in a new dimension. Exosomes are a group of subpopulations of EVs. It originates from the endosomes and carries biologically active molecules to the neighboring cells which in turn transforms the recipient cell activity. In general, it plays a role in cellular communication. The correlation between exosomes and cancer is fascinating. Tumor-derived exosomes (TEXs) play a dynamic role in cancer progression and are associated with uncontrolled cell growth, angiogenesis, immune suppression, and metastasis. Its molecular cargo is an excellent source of cancer biomarkers. Several advanced molecular profiling approaches assist in exploring the TEXs in depth. This paves the way for a strong foundation for identifying and detecting more specific and efficient biomarkers. TEXs are also gaining importance in scientific society for its role in cancer therapy and several clinical trials based on TEXs is a proof of its significance. In this review, we have highlighted the role of TEXs in mediating immune cell reprogramming, cancer development, metastasis, EMT, organ-specific metastasis, and its clinical significance in cancer theranostics. TEXs profiling is an effective method to understand the complications associated with cancer leading to good health and well-being of the individual and society as a whole.

Therapeutic effects of medicinal plants and their constituents on lung cancer, in vitro, in vivo and clinical evidence

The most common type of cancer in the world is lung cancer. Traditional treatments have an important role in cancer therapy. In the present review, the most recent findings on the effects of medicinal plants and their constituents or natural products (NP) in treating lung cancer are discussed. Empirical studies until the end of March 2022 were searched using the appropriate keywords through the databases PubMed, Science Direct and Scopus. The extracts and essential oils tested were all shown to effect lung cancer by several mechanisms including decreased tumour weight and volume, cell viability and modulation of cytokine. Some plant constituents increased expression of apoptotic proteins, the proportion of cells in the G2/M phase and subG0/G1 phase, and Cyt c levels. Also, natural products (NP) activate apoptotic pathways in lung cancer cell including p-JNK, Akt/mTOR, PI3/ AKT\ and Bax, Bcl2, but suppressed AXL phosphorylation. Plant-derived substances altered the cell morphology, reduced cell migration and metastasis, oxidative marker production, p-eIF2α and GRP78, IgG, IgM levels and reduced leukocyte counts, LDH, GGT, 5'NT and carcinoembryonic antigen (CEA). Therefore, medicinal plant extracts and their constituents could have promising therapeutic value for lung cancer, especially if used in combination with ordinary anti-cancer drugs.

Association of immunotoxicological indices with lung cancer biomarkers in poultry, grape, and rose farming workers

Exposure to occupational hazards like dust, pesticides, diesel emission particles, or physical hazards in the agricultural sector is known to cause adverse health effects on farm workers. Our study aimed at addressing the association of immunomodulatory status with plasma levels of lung cancer biomarkers in farming population, attempting to recognition of vulnerable farming group. Blood samples from apparently healthy 51 chicken husbandry, 19 grape orchard, and 21 rose greenhouse workers were subjected to evaluate plasma levels of two representative lung cancer biomarkers, pro-gastrin releasing peptide (Pro-GRP) and cytokeratin fragment 19 (CYFRA 21-1). Peripheral blood mononuclear cells obtained from farmers were used for natural killer (NK) cell phenotyping and cytokines (interferon-gamma, IFN-γ and interleukin-13, IL-13) profiling in the culture supernatant. Compared to the rose greenhouse farmers, the grape orchard and chicken husbandry workers revealed a significantly upregulated plasma Pro-GRP and CYFRA 21-1 level. A low proportion of NK cells was observed among the female grape orchard workers and a lowered IFN- γ:IL-13 ratio was seen in the grape and chicken husbandry workers than the rose workers. Our findings imply that grape orchard and chicken husbandry workers have more disturbed immune homeostasis implicated with augmentation in the levels of lung cancer biomarkers than the rose greenhouse workers.

Biosensors for the detection of lung cancer biomarkers: A review on biomarkers, transducing techniques and recent graphene-based implementations

Lung cancer remains the leading cause of cancer-related death. In addition to chest X-rays and computerised tomography, the detection of cancer biomarkers serves as an emerging diagnostic tool for lung cancer. This review explores biomarkers including the rat sarcoma gene, the tumour protein 53 gene, the epidermal growth factor receptor, the neuron-specific enolase, the cytokeratin-19 fragment 21-1 and carcinoembryonic antigen as potential indicators of lung cancer. Biosensors, which utilise various transduction techniques, present a promising solution for the detection of lung cancer biomarkers. Therefore, this review also explores the working principles and recent implementations of transducers in the detection of lung cancer biomarkers. The transducing techniques explored include optical techniques, electrochemical techniques and mass-based techniques for detecting biomarkers and cancer-related volatile organic compounds. Graphene has outstanding properties in terms of charge transfer, surface area, thermal conductivity and optical characteristics, on top of allowing easy incorporation of other nanomaterials. Exploiting the collective merits of both graphene and biosensor is an emerging trend, as evidenced by the growing number of studies on graphene-based biosensors for the detection of lung cancer biomarkers. This work provides a comprehensive review of these studies, including information on modification schemes, nanomaterials, amplification strategies, real sample applications, and sensor performance. The paper concludes with a discussion of the challenges and future outlook of lung cancer biosensors, including scalable graphene synthesis, multi-biomarker detection, portability, miniaturisation, financial support, and commercialisation.

Circulating proteome for pulmonary nodule malignancy

BACKGROUND

Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules.

METHODS

Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated.

RESULTS

We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited to Lung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001).

CONCLUSIONS

Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.

Trends in Lung Cancer Incidence and Mortality (1990-2019) in the United States: A Comprehensive Analysis of Gender and State-Level Disparities

PURPOSE

Lung cancer is the leading cause of cancer-related deaths in the United States. This study aims to analyze lung cancer incidence, mortality, and related statistics from 1990 to 2019, focusing on national- and state-level trends and exploring potential disparities between sexes.

METHODS

The Global Burden of Disease database was used to extract tracheal, bronchus, and lung cancer mortality data from 1990 to 2019 for both males and females and across all states of the United States. Age-standardized incidence rates, age-standardized mortality rates, disability-adjusted life years (DALYs), and mortality-to-incidence indices (MIIs) were studied to assess for gender-based, geographic, and temporal disparities. Joinpoint regression analysis was performed to further evaluate trends.

RESULTS

The incidence of these cancers in the United States decreased between 1990 and 2019 by 23.35%, with a more significant decline in males (37.73%) than females (1.41%). Similarly, for mortality, a decrease was observed for both sexes combined (26.83%), but much more significantly for males (40.23%) than females (6.01%). The MIIs decreased overall, but there were variations across states. DALYs decreased for both sexes combined, with males experiencing a larger reduction, but an increase was noted in some states for females.

CONCLUSION

This analysis reveals diverse trends pertaining to the incidence, mortality, and disability burden associated with lung cancer by sex and states in the United States, emphasizing the need for targeted interventions to reduce disparities. These findings contribute to our understanding of the current landscape of lung cancer and can inform future strategies for prevention, early detection, and management.

Does the last 20 years paradigm of clinical research using volatile organic compounds to non-invasively diagnose cancer need to change? Challenges and future direction

PURPOSE

Volatile organic compounds (VOCs) have shown great potential as novel biomarkers for cancer detection; however, comprehensive quantitative analysis is lacking. In this study, we performed a bibliometric analysis of non-invasive cancer diagnosis using VOCs to better characterise international trends and to predict future hotspots in this field, and then we focussed on human studies to analyse clinical characteristics for presenting the current controversies and future perspectives of further clinical work.

METHODS

Publications, from 2002 to 2022, were retrieved from the Web of Science Core Collection database. CiteSpace and VOSviewer were used to generate network maps and identify the annual publications, top countries, authors, institutions, journals, references, and keywords. Then, we further screened clinical trials, and the key information was extracted into Microsoft Excel for further systematical analysis.

RESULTS

Six hundred and forty-one articles were identified to evaluate research trends, of which 301 clinical trials were selected for further systematical analysis. Overall, the annual publications in this area increased, with an overall upward trend, while the quality of clinical research remains remarkably uneven.

CONCLUSION

The study of non-invasive cancer diagnosis using VOCs would continue to be an active field. However, without stringent clinical design criteria, most suitable acquisition and analysis devices and statistical approaches, a list of exclusive, specific, reliable and reproducible VOCs to identify a disease and these VOCs appearing in a breath at detectable levels at early stage disease, the clinical utility of VOC tests will be difficult to have any breakthroughs.

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

Design of stretchable and self-powered sensing device for portable and remote trace biomarkers detection

Timely and remote biomarker detection is highly desired in personalized medicine and health protection but presents great challenges in the devices reported so far. Here, we present a cost-effective, flexible and self-powered sensing device for H2S biomarker analysis in various application scenarios based on the structure of galvanic cells. The sensing mechanism is attributed to the change in electrode potential resulting from the chemical adsorption of gas molecules on the electrode surfaces. Intrinsically stretchable organohydrogels are used as solid-state electrolytes to enable stable and long-term operation of devices under stretching deformation or in various environments. The resulting open-circuit sensing device exhibits high sensitivity, low detection limit, and excellent selectivity for H2S. Its application in the non-invasive halitosis diagnosis and identification of meat spoilage is demonstrated, emerging great commercial value in portable medical electronics and food security. A wireless sensory system has also been developed for remote H2S monitoring with the participation of Bluetooth and cloud technologies. This work breaks through the shortcomings in the traditional chemiresistive sensors, offering a direction and theoretical foundation for designing wearable sensors catering to other stimulus detection requirements.

Intratumor microbiome derived glycolysis-lactate signatures depicts immune heterogeneity in lung adenocarcinoma by integration of microbiomic, transcriptomic, proteomic and single-cell data

Introduction

Microbiome plays roles in lung adenocarcinoma (LUAD) development and anti-tumor treatment efficacy. Aberrant glycolysis in tumor might promote lactate production that alter tumor microenvironment, affecting microbiome, cancer cells and immune cells. We aimed to construct intratumor microbiome score to predict prognosis of LUAD patients and thoroughly investigate glycolysis and lactate signature's association with LUAD immune cell infiltration.

Methods

The Cancer Genome Atlas-LUAD (TCGA-LUAD) microbiome data was downloaded from cBioPortal and analyzed to examine its association with overall survival to create a prognostic scoring model. Gene Set Enrichment Analysis (GSEA) was used to find each group's major mechanisms involved. Our study then investigated the glycolysis and lactate pattern in LUAD patients based on 19 genes, which were correlated with the tumor microenvironment (TME) phenotypes and immunotherapy outcomes. We developed a glycolysis-lactate risk score and signature to accurately predict TME phenotypes, prognosis, and response to immunotherapy.

Results

Using the univariate Cox regression analysis, the abundance of 38 genera were identified with prognostic values and a lung-resident microbial score (LMS) was then developed from the TCGA-LUAD-microbiome dataset. Glycolysis hallmark pathway was significantly enriched in high-LMS group and three distinct glycolysis-lactate patterns were generated. Patients in Cluster1 exhibited unfavorable outcomes and might be insensitive to immunotherapy. Glycolysis-lactate score was constructed for predicting prognosis with high accuracy and validated in external cohorts. Gene signature was developed and this signature was elevated in epithelial cells especially in tumor mass on single-cell level. Finally, we found that the glycolysis-lactate signature levels were consistent with the malignancy of histological subtypes.

Discussion

Our study demonstrated that an 18-microbe prognostic score and a 19-gene glycolysis-lactate signature for predicting prognosis of LUAD patients. Our LMS, glycolysis-lactate score and glycolysis-lactate signature have potential roles in precision therapy of LUAD patients.

Overexpression of SERPINA3 suppresses tumor progression by modulating SPOP/NF‑κB in lung cancer

The pathogenesis mechanism of lung cancer is very complex, with high incidence and mortality. Serpin family A member 3 (SERPINA3) expression levels were reduced in the sera of patients with lung cancer and may be a candidate diagnostic and prognostic survival biomarker in lung cancer, as previously reported. However, the detailed biological functions of SERPINA3 in the pathogenesis of lung cancer remain unknown. In the present study, it was aimed to explore the effects of SERPINA3 on the occurrence of lung cancer. SERPINA3 expression was assessed using bioinformatics database analysis and experimental detection. Then, the biological effects of SERPINA3 were investigated in a cell culture system and a xenograft model of human lung cancer. The potential regulatory mechanism of SERPINA3 in lung cancer was explored by data‑independent acquisition mass spectrometry (DIA‑MS) detection and further validated by western blotting (WB). The results indicated that SERPINA3 expression levels were significantly downregulated in lung cancer tissues and cell lines. At the cellular level, it was revealed that overexpressed SERPINA3 inhibited cell growth, proliferation, migration and invasion and promoted the apoptosis of lung cancer cells. Moreover, overexpressed SERPINA3 enhanced the sensitivity of lung cancer cells to osimertinib. In vivo, a xenograft model of human lung cancer was established with BALB/c nude mice. After the injection of A549 cells, the tumor growth of the tumor‑bearing mice in the SERPINA3‑overexpressing group increased more slowly, and the tumor volume was smaller than that in the empty‑vector group. Mechanistically, a total of 65 differentially expressed proteins were identified. It was found that the speckle‑type POZ protein (SPOP) was significantly upregulated in SERPINA3‑overexpressing H157 cells using DIA‑MS detection and analysis. WB validation showed that SPOP expression increased, and NF‑kappaB (NF‑κB) p65 was inhibited in cell lines and tumor tissues of mice when SERPINA3 was overexpressed. The present findings suggest that SERPINA3 is involved in the development of lung cancer and has an antineoplastic role in lung cancer.

Circulating exosomal microRNA-4497 as a potential biomarker for metastasis and prognosis in non-small-cell lung cancer

Circulating exosomal microRNAs (miRNAs) have shown great potential for the diagnosis, prognosis, and treatment monitoring of patients with non-small-cell lung cancer (NSCLC). Our main purpose was to determine the clinical value of serum exosomal miR-4497 as a new non-invasive biomarker for NSCLC. The exoRNeasy Kit (QIAGEN, Hilden, Germany) was used to isolate exosomes and exoRNA from the serum of 84 patients with NSCLC (NSCLC group), 30 patients with benign lung lesion (BLL group), and 47 healthy controls. Six serum exosomal miRNAs (Let-7b-5p, miR-122-5p, miR-155-5p, miR-223-3p, miR-320c, and miR-4497) were selected as candidate miRNAs and analyzed using real-time qPCR, among which miR-4497 displayed the most striking differences. Exosomal miR-4497 expressed significantly lower in NSCLC than in BLL patients and healthy controls (P < 0.001). Further investigation showed that miR-4497 was negatively correlated with the malignant characteristics of tumors (tumor size, tumor-node-metastasis [TNM] stage, and distant metastasis) and was an independent tumor suppressor (P < 0.05). According to receiver operating characteristic (ROC) analysis, exosomal miR-4497 independently exhibited excellent diagnostic efficacy, which could be improved by combining it with traditional markers (for identifying tumor size, the area under the curve [AUC] = 0.761; TNM stage, AUC = 0.878; distant metastasis, AUC = 0.895; all P < 0.001). Moreover, longitudinal analysis revealed that exosomal miR-4497 levels increased after chemoradiotherapy (P < 0.001). According to the survival analysis, poor overall survival (OS) and disease-free survival (DFS) were associated with low exosomal miR-4497 levels (P < 0.05). Moreover, exosomal miR-4497 was an independent protective factor affecting DFS (hazard ratio = 0.190, P = 0.009) in the Cox proportional hazards model. Therefore, serum exosomal miR-4497 can be used as a potential biomarker to identify NSCLC and healthy individuals or BLL patients for early screening or as a biomarker for staging and grading, prognosis, and monitoring recurrence, metastasis, and the therapeutic effects in patients with NSCLC.

Long non-coding RNAs in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) refer to a class of RNAs greater than 200 nucleotides in length, most of which are considered unable to encode proteins, thus deemed to be junk genes formerly. But with emerging studies about lncRNAs coming out in recent years, it is much more clearly depicted that they can regulate gene expression at different levels, with various mechanisms, thus participating in diverse biological or pathological processes, including complicated tumor-associated pathways. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, the third leading cause of cancer-related mortality worldwide, which has been found to tightly associate with aberrant expression of a variety of lncRNAs regulating tumor proliferation, invasion, drug resistance, and so on, making it a potential novel tumor marker and therapeutic target. In this review, we highlight a few lncRNAs that are closely related to the occurrence and progression of HCC and try to cover their multifarious roles from different layers.

Novel Screening Tools for Lung Cancer

Screening with low-dose computed tomography has been shown to decrease lung cancer mortality. However, the issues of low detection rates and false positive results remain, highlighting the need for adjunctive tools in lung cancer screening. To this end, researchers have investigated easily applicable, minimally invasive tests with high validity. We herein review some of the more promising novel markers utilizing plasma, sputum, and airway samples.

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development

Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated. As omics data grow, artificial intelligence models, particularly deep learning, are prominent in developing more rapid and effective methods to potentially improve lung cancer patient diagnosis, prognosis and treatment strategy. This decade has seen genome-based deep learning models thriving in various lung cancer tasks, including cancer prediction, subtype classification, prognosis estimation, cancer molecular signatures identification, treatment response prediction and biomarker development. In this study, we summarized available data sources for deep-learning-based lung cancer mining and provided an update on recent deep learning models in lung cancer genomics. Subsequently, we reviewed the current issues and discussed future research directions of deep-learning-based lung cancer genomics research.

Systematic Review of Lung Cancer Screening: Advancements and Strategies for Implementation

Lung cancer is the leading cause of cancer-related deaths in Europe, with low survival rates primarily due to late-stage diagnosis. Early detection can significantly improve survival rates, but lung cancer screening is not currently implemented in Italy. Many countries have implemented lung cancer screening programs for high-risk populations, with studies showing a reduction in mortality. This review aimed to identify key areas for establishing a lung cancer screening program in Italy. A literature search was conducted in October 2022, using the PubMed and Scopus databases. Items of interest included updated evidence, approaches used in other countries, enrollment and eligibility criteria, models, cost-effectiveness studies, and smoking cessation programs. A literature search yielded 61 scientific papers, highlighting the effectiveness of low-dose computed tomography (LDCT) screening in reducing mortality among high-risk populations. The National Lung Screening Trial (NLST) in the United States demonstrated a 20% reduction in lung cancer mortality with LDCT, and other trials confirmed its potential to reduce mortality by up to 39% and detect early-stage cancers. However, false-positive results and associated harm were concerns. Economic evaluations generally supported the cost-effectiveness of LDCT screening, especially when combined with smoking cessation interventions for individuals aged 55 to 75 with a significant smoking history. Implementing a screening program in Italy requires the careful consideration of optimal strategies, population selection, result management, and the integration of smoking cessation. Resource limitations and tailored interventions for subpopulations with low-risk perception and non-adherence rates should be addressed with multidisciplinary expertise.

Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer

BACKGROUND

For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme.

METHODS

TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set.

RESULTS

Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%.

CONCLUSIONS

Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.

ERS International Congress 2022: highlights from the Thoracic Oncology Assembly

Thoracic malignancies are associated with a substantial public health burden. Lung cancer is the leading cause of cancer-related mortality worldwide, with significant impact on patients' quality of life. Following 2 years of virtual European Respiratory Society (ERS) Congresses due to the COVID-19 pandemic, the 2022 hybrid ERS Congress in Barcelona, Spain allowed peers from all over the world to meet again and present their work. Thoracic oncology experts presented best practices and latest developments in lung cancer screening, lung cancer diagnosis and management. Early lung cancer diagnosis, subsequent pros and cons of aggressive management, identification and management of systemic treatments' side-effects, and the application of artificial intelligence and biomarkers across all aspects of the thoracic oncology pathway were among the areas that triggered specific interest and will be summarised here.