Serum and blood based biomarkers for lung cancer screening: a systematic review

Comprehensive Serum Glycopeptide Spectra Analysis Combined with Machine Learning for Early Detection of Lung Cancer: A Case-Control Study

BACKGROUND

Lung cancer is among the most prevalent and fatal cancers worldwide. Traditional diagnostic methods, such as computed tomography, are not ideal for screening due to their high cost and radiation exposure. In contrast, blood-based diagnostics, as non-invasive approaches, are expected to reduce patient burden, thereby increasing screening participation and ultimately improving survival rates. However, conventional tumor markers have shown limited effectiveness in early detection.

METHODS

We recruited 199 patients with lung cancer and 590 healthy volunteers. Nine tumor markers (CEA, CA19-9, CYFRA, AFP, PSA, CA125, CA15-3, SCC antigen, and NCC-ST439) were analyzed, along with enriched glycopeptides (EGPs) derived from serum proteins using liquid chromatography-mass spectrometry. Machine learning models, including decision trees and deep learning approaches, were employed to develop a predictive model for accurately distinguishing lung cancer from healthy controls based on tumor markers and EGP profiles.

RESULTS

We found that α1-antitrypsin with fully sialylated biantennary glycan, attached to asparagine 271 (AT271-FSG), and α2-macroglobulin with fully sialylated biantennary glycan, attached to asparagine 70 (MG70-FSG), could significantly distinguish between patients with lung cancer and healthy individuals. Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA), integrating nine conventional tumor markers and 1688 EGPs using a machine learning model, enhanced diagnostic accuracy and achieved an ROC-AUC score of 0.935. It also identified stage I cases with an ROC-AUC of 0.914, indicating the possibility of early-stage detection. The PPV reached 2.8%, which was sufficient for practical application.

CONCLUSIONS

This method represents a significant advancement in cancer diagnostics, combining multiple biomarkers with cutting-edge machine learning to improve the early detection of lung cancer.

Hsa_circ_0072088 promotes non-small cell lung cancer progression through modulating miR-1270/TOP2A axis

According to the data released by the International Agency for Research on Cancer (IARC) in 2020, lung cancer ranks second among newly diagnosed malignant tumors globally. As a special class of non-coding RNA, circRNA has become a new hotspot in the field of biomarker research. With the continuous deepening of molecular-level investigations, the underlying mechanisms of circRNA are being gradually unveiled. The more widely studied mechanism is the competitive endogenous RNA mechanism of circRNA. Studies related to circRNA expression were searched in GEO database and statistically analyzed using the "limma" package and weighted gene co-expression network analysis. The expression of circRNA, microRNA and mRNA in cells and tissues were examined via qRT-PCR. MTS assay was used to measure cell proliferation, Transwell assay was used to measure cell migration, and apoptosis assay was carried out to detect cell apoptosis. Additionally, a dual-luciferase reporter assay was further executed to explore the targeted binding relationships between circRNA-microRNA and microRNA-mRNA. It was discovered that hsa_circRNA_103809 was differentially highly expressed in non-small cell lung cancer cells, whereas miR-1270 was differentially lowly expressed. The knockdown of circ_0072088 inhibited the cell proliferation and migration, while promoting cell apoptosis. The same biological function was found with the overexpression of miR-1270. The rescue experiment further validated that circ_0072088 could regulate the biological function of cells by influencing miR-1270. Finally, the targeted binding relationship was verified by dual luciferase reporting experiment. In conclusion, circ_0072088 is differentially highly expressed in non-small cell lung cancer and can affect the progression of non-small cell lung cancer through the circ_0072088/miR-1270/TOP2A axis.

Historic p87 Is Diagnostic for Lung Cancer Preceding Clinical Presentation by at Least 4 Years

Lung cancer remains the most common cancer worldwide, with a limited prognosis despite personalized treatment regimens. Low-dose computed tomography (CT) scanning as a means of early diagnosis has been disappointing due to the high false positive rate. Other non-invasive means of testing need to be developed that offer both timely diagnosis and predict prognosis. Methods: In the course of stool testing in large-scale testing of 2922 patients at increased risk of CRC, we were able to ascertain 112 patients documented to have prospectively been diagnosed with lung cancer. Stool and colonic effluents were tested for p87 with anti-adenoma antibody (Adnab-9) reactivity by ELISA and Western blot. Survival data were obtained where available. Results: Of 112 cancers, approximately 27.6% were squamous (SSC), 17.9% were adenocarcinoma, 8% were small, 6.25% were large cell, 3.57% were designated non-small cell cancer (NSCLC), 0.89% were indeterminate, 0.89% were lepidic spread, 3.57% had metastasis, and in 31.25%, data were unavailable. In total, 49.1% of the lung cancer patients had fecal Adnab-9 testing. Overall, 60% had positive testing compared to 38%, which was significant (OR2.19 [1.06-4.53]; p = 0.045). Cancers with higher lethality were less likely to test positive (approximately 8.5% each for both small and large cell lung cancers) and higher, with 56% for SCC and 25% for adenocarcinoma (0% NSCLC). In the larger groups, overall survival was worse in those testing positive: 474 testing positives versus 844 days in SCC and 54 testing positive versus 749 days in adenocarcinoma patients. Most importantly, the time from a positive test to the clinical diagnosis ranged from 2.72 years for small cell, 3.13 for adenocarcinoma, 5.07 for NSCLC, 6.07 for SSC, and 6.24 for large cell cancer. In excluded cases where cancer in the lung was believed to be metastatic, 83.3% of cancers were positive. Conclusions: At a projected real-world sensitivity of 0.60 and specificity of 0.60, and the ability to predate diagnosis by up to 4.7 years overall, this test could help direct lung cancer screening. In addition, the Adnab-9 testing selectively detects worse tumor types (87.5%) and those with worse prognoses amongst the more common, favorable phenotypes, thus making early diagnosis possible in those patients who stand to benefit most from this strategy. Metastatic lung cancer, also detected by the test, should be identified by the follow-up imaging studies and, therefore, would not be considered to be a major pitfall.

Multi-omics model is an effective means to diagnose benign and malignant pulmonary nodules

In response to the high false positive rate of traditional Low-Dose Computed Tomography (LDCT) in diagnosing pulmonary malignant nodules, this study aimed to investigate the effectiveness of scoring of blood-based non-invasive biological metabolite detection combined with artificial intelligent scoring of non-invasive imaging in the clinical diagnosis of Pulmonary Nodules (PNs). In this retrospective study, risk scoring was performed in patients positive for pulmonary nodules and subsequently, PNs were sampled by invasive procedures for pathological examinations. The pathological classification was used as the gold standard, and statistical and machine learning methods showed, that in 210 patients (23 benign PN and 187 malignant PN), the risk score of Metabonomics, radiomics, and multi-omics had different levels of performance in different risk groups based on various predictive models. The Area Under the receiver operating Characteristic Curve (AUC) of the multi-omics model was 0.823. The present results indicate that a multi-omics model is more effective than a single model in the non-invasive diagnosis of pulmonary malignant nodules.

Biomarkers Suitable for Early Detection of Intrathoracic Cancers in Primary Care: A Systematic Review

Intrathoracic cancers, including lung cancer, mesothelioma, and thymoma, present diagnostic challenges in primary care. Biomarkers could resolve some challenges. We synthesized evidence on biomarker performance for intrathoracic cancer detection in low-prevalence settings. A search in Embase and MEDLINE included studies that recruited participants with suspected intrathoracic cancer and reported on at least one diagnostic measure for a validated, noninvasive biomarker. Studies were excluded if participants were recruited based on a preestablished diagnosis. A total of 52 studies were included, reporting on 108 individual biomarkers and panels. Carcinoembryonic antigen, CYFRA 21-1, and VEGF were evaluated for lung cancer and mesothelioma. For lung cancer, carcinoembryonic antigen and CYFRA 21-1 were the most studied, with AUCs of 0.48 to -0.90 and 0.48 to -0.83, respectively. Pro-gastrin-releasing peptide (Pro-GRP) and neuron-specific enolase (NSE) had the highest negative predictive values (NPV) (98.2% and 96.9%, respectively), whereas Early Cancer Detection Test - Lung (Early CDT) and miRNA signature classifier panels showed NPVs of 99.3% and 99.0%, respectively, in smokers. For mesothelioma, fibrillin-3 and mesothelin plus osteopontin had AUCs of 0.93 and 0.91, respectively. Thymoma panels (binding AcHR + StrAb and binding AcHR + modulating AcHR + StrAb) had 100% NPVs in patients with myasthenia gravis. The review highlights the performance of some biomarkers. However, few were evaluated in low-prevalence settings. Further evaluation is necessary before implementing these biomarkers for intrathoracic cancers in primary care.

Five year mortality in an RCT of a lung cancer biomarker to select people for low dose CT screening

The role of biomarkers in risk-based early detection of lung cancer may enable screening to become cost effective and widely accessible. EarlyCDT-Lung is an example of such a blood-based autoantibody biomarker which may improve accessibility to Low dose Computed Tomography (LDCT) screening for those at highest risk. We randomized 12 208 individuals aged 50-75 at high risk of developing lung cancer to either the test or to standard clinical care. Outcomes were ascertained from Register of Deaths and Cancer Registry. Cox proportional hazards models were used to estimate the hazard ratio of the rate of deaths from all causes and lung cancer. Additional analyses were performed for cases of lung cancer diagnosed within two years of the initial test. After 5 years 326 lung cancers were detected (2.7% of those enrolled). The total number of deaths reported from all causes in the intervention group was 344 compared to 388 in the control group. There were 73 lung cancer deaths in the intervention arm and 90 in the controls (Adjusted HR 0.789 (0.636, 0.978). An analysis of cases of lung cancer detected within 2 years of randomization in the intervention group showed that there were 34 deaths from all causes and 29 from lung cancer. In the control group there were 56 deaths with 49 from lung cancer. In those diagnosed with lung cancer within 2 years of randomization the hazard ratio for all cause mortality was 0.615 (0.401,0.942) and for lung cancer 0.598 (0.378, 0.946). Further large-scale studies of the role of biomarkers to target lung cancer screening, in addition to LDCT, are likely to provide additional value.

Association of DNA methylation of RASSF1A and SHOX2 with lung cancer risk: A systematic review and meta-analysis

BACKGROUND

This study estimates the research upon the potential worth of Ras association domain family member 1 A (RASSF1A) and short stature homeobox 2 (SHOX2) DNA methylation in lung cancer (LC) diagnosis.

METHODS

Open-published research was searched through PubMed, EMBASE, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, and Chinese Biology Medicine Literature Database. Data on true positives, false positives, false negatives, and true negatives were extracted.

RESULTS

This meta-analysis included 22 studies encompassing 4109 subjects (2427 LC patients and 1682 controls). The combined sensitivity, specificity, and area under the curve for RASSF1A and SHOX2 DNA methylation were 0.77 (95% CI: 0.71-0.81), 0.90 (95% CI: 0.87-0.92), and 0.92 (95% CI: 0.87-0.92), respectively. The pooled positive likelihood ratio and negative likelihood ratio were 7.5 (5.9-9.7) and 0.26 (0.21-0.32). The combined diagnostic odds ratio was 29 (95% CI: 20-41).

CONCLUSION

RASSF1A and SHOX2 DNA methylation may emerge as potential diagnostic biomarkers for early-stage LC.

Circulating RKIP and pRKIP in Early-Stage Lung Cancer: Results from a Pilot Study

Background: Lung cancer (LC) is the leading cause of cancer-related deaths. Although low-dose computed tomography (LD-CT) reduces mortality, its clinical use is limited by cost, radiation, and false positives. Therefore, there is an urgent need for non-invasive and cost-effective biomarkers. The Raf Kinase Inhibitor Protein (RKIP) plays a crucial role in cancer development and progression and may also contribute to regulating the tumor-immune system axis. This protein has recently been described in biological fluids. Therefore, we conducted a pilot case-control study to assess RKIP and phosphorylated RKIP (pRKIP) levels in the urine and blood of LC patients. Methods: A novel enzyme linked immunosorbent assay (ELISA) assay was used to measure RKIP and pRKIP levels in urine and blood samples of two cohorts of LC patients and healthy controls (HSs). Furthermore, the biomarkers levels were correlated with tumor characteristics. Results: Serum, but not urine, levels of RKIP were significantly elevated in LC patients, distinguishing them from low- and high-risk healthy subjects with 93% and 74% accuracy, respectively. The RKIP/pRKIP ratio (RpR score) showed an accuracy of 90% and 79% in distinguishing LC patients from HS and HR-HS, respectively. Additionally, the RpR score correlated better with dimension, stage, and lymph node involvement in the tumor group. Conclusions: The serum RKIP and pRKIP profile may be a promising novel biomarker for early-stage LC.

Diagnostic potential of protein serum biomarkers for distinguishing small and non-small cell lung cancer in patients with suspicious lung lesions

BACKGROUND

Biomarkers play a role in identifying, managing, and predicting cancer outcomes. In lung cancer, they are used at various time points. Doubts remain regarding their accuracy for differential diagnosis and histological subtyping. A diagnostic test study was conducted. It included malignant lesions and controls with benign lesions. Before lung biopsy, all patients had the following biomarkers measured in serum (Pro-GRP,NSE,CYFRA21-1,SCC-Ag,CEA).

METHODS

The predictive capacity of serum biomarkers was evaluated to discriminate between lung cancer and benign pathology. The accuracy was also assessed for distinguishing between SCLC and NSCLC and explored their ability to perform histological subtyping.

RESULTS

93 patients were included, 60 with lung cancer, 33 with benign pathology. Pro-GRP and NSE were elevated in SCLC compared with NSCLC or nonmalignant disease. The most accurate for differentiating between malignant and benign pathology were CEA and CYFRA21-1. Pro-GRP had a poor predictive capacity for distinguishing NSCLC from SCLC. However, combined with CEA and CYFRA21-1, performance improved. For SCLC, the diagnostic capacity of Pro-GRP increased by combining with biomarkers, such as NSE/CYFRA21-1.

CONCLUSIONS

Biomarkers lacked the sensitivity and specificity for independent differential diagnosis or histological subtyping. However, the observed patterns in biomarker levels associated with specific histological subtypes suggest potential utility in a multi-biomarker approach or in conjunction with other diagnostic tools. This insight could guide future research to improve diagnostic accuracy and personalized treatment strategies in lung cancer.

A novel microRNA miR-4433a-3p as a potential diagnostic biomarker for lung adenocarcinoma

Background

Lung adenocarcinoma is one of the leading causes of cancer-related deaths because of the lack of early specific clinical indicators. MicroRNAs (miRNAs) have become the focus in lung cancer diagnosis. Further studies are required to explore miRNA expression in the serum of lung adenocarcinoma patients and their correlation with therapy and analyse specific messenger RNA targets to improve the specificity and sensitivity of early diagnosis.

Methods

The Toray 3D-Gene miRNA array was used to compare the expression levels of various miRNAs in the sera of patients with lung adenocarcinoma and healthy volunteers. Highly expressed miRNAs were selected for further analysis. To verify the screening results, serum and pleural fluid samples were analysed using qRT-PCR. Serum levels of the miRNAs and their correlation with the clinical information of patients with lung adenocarcinoma were analysed. The functions of miRNAs were further analysed using the Kyoto Encyclopedia of Gene and Genomes and Gene Ontology databases.

Results

Microarray analysis identified 60 and 50 miRNAs with upregulated and downregulated expressions, respectively, in the serum of patients with lung adenocarcinoma compared to those in healthy individuals. Using qRT-qPCR to detection of miRNAs expression in the serum or pleural effusion of patients with early and advanced lung adenocarcinoma, we found that miR-4433a-3p could be used as a diagnostic marker and therapeutic evaluation indicator for lung adenocarcinoma. Serum of miR-4433a-3p levels significantly correlated with the clinical stage. miR-4433a-3p may be more suitable than other tumour markers for the early diagnosis and evaluation of therapeutic effects in lung adenocarcinoma. miR-4433a-3p may affect tumour growth and metastasis by acting on target genes (PIK3CD, UBE2J2, ICMT, PRDM16 and others) and regulating tumour-related signalling pathways (MAPK signal pathway, Ras signalling pathway and others).

Conclusion

miR-4433a-3p may serve as a biomarker for the early diagnosis of lung adenocarcinoma and monitoring of therapeutic effects.

Evaluating the comprehensive diagnosis efficiency of lung cancer, including measurement of SHOX2 and RASSF1A gene methylation

Methylation of the promoters of SHOX2 and RASSF1A (LungMe®) exhibits promise as a potential molecular biomarker for diagnosing lung cancer. This study sought to assess the aberrant methylation of SHOX2 and RASSF1A in broncho-exfoliated cells (BEC) and compare it with conventional cytology, histology examination, immunohistochemistry, and serum tumor markers to evaluate the overall diagnostic efficiency for lung cancer. This study recruited 240 patients, including 185 malignant cases and 55 benign cases. In our observation, we noted a slight reduction in the detection sensitivity, however, the ΔCt method exhibited a significant enhancement in specificity when compared to Ct judgment. Consequently, the ΔCt method proves to be a more appropriate approach for interpreting methylation results. The diagnostic sensitivity of cytology and histology was in ranged from 20.0%-35.1% and 42.9%-80%, respectively, while the positive detection rate of LungMe® methylation ranged from 70.0% to 100%. Additionally, our findings indicate a higher prevalence of SHOX2( +) among patients exhibiting medium and high expression of Ki67 (P < 0.01), as opposed to those with low expression of Ki67, but RASSF1A methylation did not show this phenomenon (P = 0.35). Furthermore, CEA, SCCA, and CYFRA21-1 showed positive detection rates of 48.8%, 26.2%, and 55.8%, respectively. Finally, we present a comprehensive lung cancer diagnostic work-up, including LumgMe® methylation. The combined analysis of SHOX2 and RASSF1A methylation serves as a powerful complement and extension to conventional methods, enhancing the accuracy of a lung cancer diagnosis with satisfactory sensitivity and specificity.

Metabolomics and lipidomics in non-small cell lung cancer

Due to its insidious nature, lung cancer remains a leading cause of cancer-related deaths worldwide. Therefore, there is an urgent need to identify sensitive/specific biomarkers for early diagnosis and monitoring. The current study was designed to provide a current metabolic profile of non-small cell lung cancer (NSCLC) by systematically reviewing and summarizing various metabolomic/ lipidomic studies based on NSCLC blood samples, attempting to find biomarkers in human blood that can predict or diagnose NSCLC, and investigating the involvement of key metabolites in the pathogenesis of NSCLC. We searched all articles on lung cancer published in Elsevier, PubMed, Web of Science and the Cochrane Library between January 2012 and December 2022. After critical selection, a total of 31 studies (including 2768 NSCLC patients and 9873 healthy individuals) met the inclusion criteria, and 22 were classified as "high quality". Forty-six metabolites related to NSCLC were repeatedly identified, involving glucose metabolism, amino acid metabolism, lipid metabolism and nucleotide metabolism. Pyruvic acid, carnitine, phenylalanine, isoleucine, kynurenine and 3-hydroxybutyrate showed upward trends in all studies, citric acid, glycine, threonine, cystine, alanine, histidine, inosine, betaine and arachidic acid showed downward trends in all studies. This review summarizes the existing metabolomic/lipidomic studies related to the identification of blood biomarkers in NSCLC, examines the role of key metabolites in the pathogenesis of NSCLC, and provides an important reference for the clinical diagnosis and treatment of NSCLC. Due to the limited size and design heterogeneity of the existing studies, there is an urgent need for standardization of future studies, while validating existing findings with more studies.

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

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

Combining Low-Dose Computer-Tomography-Based Radiomics and Serum Metabolomics for Diagnosis of Malignant Nodules in Participants of Lung Cancer Screening Studies

Radiomics is an emerging approach to support the diagnosis of pulmonary nodules detected via low-dose computed tomography lung cancer screening. Serum metabolome is a promising source of auxiliary biomarkers that could help enhance the precision of lung cancer diagnosis in CT-based screening. Thus, we aimed to verify whether the combination of these two techniques, which provides local/morphological and systemic/molecular features of disease at the same time, increases the performance of lung cancer classification models. The collected cohort consists of 1086 patients with radiomic and 246 patients with serum metabolomic evaluations. Different machine learning techniques, i.e., random forest and logistic regression were applied for each omics. Next, model predictions were combined with various integration methods to create a final model. The best single omics models were characterized by an AUC of 83% in radiomics and 60% in serum metabolomics. The model integration only slightly increased the performance of the combined model (AUC equal to 85%), which was not statistically significant. We concluded that radiomics itself has a good ability to discriminate lung cancer from benign lesions. However, additional research is needed to test whether its combination with other molecular assessments would further improve the diagnosis of screening-detected lung nodules.

Exploring the Potential of Non-Coding RNAs as Liquid Biopsy Biomarkers for Lung Cancer Screening: A Literature Review

Lung cancer represent the leading cause of cancer mortality, so several efforts have been focused on the development of a screening program. To address the issue of high overdiagnosis and false positive rates associated to LDCT-based screening, there is a need for new diagnostic biomarkers, with liquid biopsy ncRNAs detection emerging as a promising approach. In this scenario, this work provides an updated summary of the literature evidence about the role of non-coding RNAs in lung cancer screening. A literature search on PubMed was performed including studies which investigated liquid biopsy non-coding RNAs biomarker lung cancer patients and a control cohort. Micro RNAs were the most widely studied biomarkers in this setting but some preliminary evidence was found also for other non-coding RNAs, suggesting that a multi-biomarker based liquid biopsy approach could enhance their efficacy in the screening context. However, further studies are needed in order to optimize detection techniques as well as diagnostic accuracy before introducing novel biomarkers in the early diagnosis setting.

Metabolic biomarkers in lung cancer screening and early diagnosis (Review)

Late diagnosis is one of the major contributing factors to the high mortality rate of lung cancer, which is now the leading cause of cancer-associated mortality worldwide. At present, low-dose CT (LDCT) screening in the high-risk population, in which lung cancer incidence is higher than that of the low-risk population is the predominant diagnostic strategy. Although this has efficiently reduced lung cancer mortality in large randomized trials, LDCT screening has high false-positive rates, resulting in excessive subsequent follow-up procedures and radiation exposure. Complementation of LDCT examination with biofluid-based biomarkers has been documented to increase efficacy, and this type of preliminary screening can potentially reduce potential radioactive damage to low-risk populations and the burden of hospital resources. Several molecular signatures based on components of the biofluid metabolome that can possibly discriminate patients with lung cancer from healthy individuals have been proposed over the past two decades. In the present review, advancements in currently available technologies in metabolomics were reviewed, with particular focus on their possible application in lung cancer screening and early detection.

Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model

Objective

This study aimed to investigate the role of spectral detector computed tomography (SDCT) quantitative parameters and their derived quantitative parameters combined with lesion morphological information in the differential diagnosis of solid SPNs.

Methods

This retrospective study included basic clinical data and SDCT images of 132 patients with pathologically confirmed SPNs (102 and 30 patients in the malignant and benign groups, respectively). The morphological signs of SPNs were evaluated and the region of interest (ROI) was delineated from the lesion to extract and calculate the relevant SDCT quantitative parameters, and standardise the process. Differences in qualitative and quantitative parameters between the groups were statistically analysed. A receiver operating characteristic (ROC) curve was constructed to evaluate the efficacy of the corresponding parameters in the diagnosis of benign and malignant SPNs. Statistically significant clinical data, CT signs and SDCT quantitative parameters were analysed using multivariate logistic regression to determine the independent risk factors for predicting benign and malignant SPNs, and the best multi-parameter regression model was established. Inter-observer repeatability was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots.

Results

Malignant SPNs differed from benign SPNs in terms of size, lesion morphology, short spicule sign, and vascular enrichment sign (P< 0.05). The SDCT quantitative parameters and their derived quantitative parameters of malignant SPNs (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, NZeff) were significantly higher than those of benign SPNs (P< 0.05). In the subgroup analysis, most parameters could distinguish between benign and adenocarcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, and NZeff), and between benign and squamous cell carcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, NEF40keV, NEF70keV, λ, and NIC). However, there were no significant differences between the parameters in the adenocarcinoma and squamous cell carcinoma groups. ROC curve analysis indicated that NIC, NEF70keV, and NEF40keV had higher diagnostic efficacy for differentiating benign and malignant SPNs (area under the curve [AUC]:0.869, 0.854, and 0.853, respectively), and NIC was the highest. Multivariate logistic regression analysis showed that size (OR=1.138, 95% CI 1.022-1.267, P=0.019), Δ70keV (OR=1.060, 95% CI 1.002-1.122, P=0.043), and NIC (OR=7.758, 95% CI 1.966-30.612, P=0.003) were independent risk factors for the prediction of benign and malignant SPNs. ROC curve analysis showed that the AUC of size, Δ70keV, NIC, and a combination of the three for differential diagnosis of benign and malignant SPNs were 0.636, 0.846, 0.869, and 0.903, respectively. The AUC for the combined parameters was the largest, and the sensitivity, specificity, and accuracy were 88.2%, 83.3% and 86.4%, respectively. The SDCT quantitative parameters and their derived quantitative parameters in this study exhibited satisfactory inter-observer repeatability (ICC: 0.811-0.997).

Conclusion

SDCT quantitative parameters and their derivatives can be helpful in the differential diagnosis of benign and malignant solid SPNs. The quantitative parameter, NIC, is superior to the other relevant quantitative parameters and when NIC is combined with lesion size and Δ70keV value for comprehensive diagnosis, the efficacy could be further improved.

Secondary prevention and treatment innovation of early stage non-small cell lung cancer: Impact on diagnostic-therapeutic pathway from a multidisciplinary perspective

Lung cancer (LC) is the leading cause of cancer-related death worldwide, mostly because the lack of a screening program so far. Although smoking cessation has a central role in LC primary prevention, several trials on LC screening through low-dose computed tomography (LDCT) in a high risk population showed a significant reduction of LC related mortality. Most trials showed heterogeneity in terms of selection criteria, comparator arm, detection nodule method, timing and intervals of screening and duration of the follow-up. LC screening programs currently active in Europe as well as around the world will lead to a higher number of early-stage Non Small Cell Lung Cancer (NSCLC) at the diagnosis. Innovative drugs have been recently transposed from the metastatic to the perioperative setting, leading to improvements in terms of resection rates and pathological responses after induction chemoimmunotherapy, and disease free survival with targeted agents and immune checkpoint inhibitors. The present review summarizes available evidence about LC screening, highlighting potential pitfalls and benefits and underlining the impact on the diagnostic therapeutic pathway of NSCLC from a multidisciplinary perspective. Future perspectives in terms of circulating biomarkers under evaluation for patients' risk stratification as well as a focus on recent clinical trials results and ongoing studies in the perioperative setting will be also presented.

Clonal Hematopoiesis and Risk of Incident Lung Cancer

PURPOSE

To prospectively examine the association between clonal hematopoiesis (CH) and subsequent risk of lung cancer.

METHODS

Among 200,629 UK Biobank (UKBB) participants with whole-exome sequencing, CH was identified in a nested case-control study of 832 incident lung cancer cases and 3,951 controls (2006-2019) matched on age and year at blood draw, sex, race, and smoking status. A similar nested case-control study (141 cases/652 controls) was conducted among 27,975 participants with whole-exome sequencing in the Mass General Brigham Biobank (MGBB, 2010-2021). In parallel, we compared CH frequency in published data from 5,003 patients with solid tumor (2,279 lung cancer) who had pretreatment blood sequencing performed through Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets.

RESULTS

In UKBB, the presence of CH was associated with increased risk of lung cancer (cases: 12.5% v controls: 8.7%; multivariable-adjusted odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74). The association remained robust after excluding participants with chronic obstructive pulmonary disease. No significant interactions with known risk factors, including polygenic risk score and C-reactive protein, were identified. In MGBB, we observed similar enrichment of CH in lung cancer (cases: 15.6% v controls: 12.7%). The meta-analyzed OR (95% CI) of UKBB and MGBB was 1.35 (1.08 to 1.68) for CH overall and 1.61 (1.19 to 2.18) for variant allele frequencies ≥ 10%. In Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets, CH with a variant allele frequency ≥ 10% was enriched in pretreatment lung cancer compared with other tumors after adjusting for age, sex, and smoking (OR for lung v breast cancer: 1.61; 95% CI, 1.03 to 2.53).

CONCLUSION

Independent of known risk factors, CH is associated with increased risk of lung cancer.

Biomarkers in Lung Cancer Screening: a Narrative Review

Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA, and DNA methylation. Finally, we will describe future directions in the field of biomarker development.

Lung cancer screening in primary care

ABSTRACT

This article reviews the evidence supporting low-dose CT to screen for lung cancer, and the risks, costs, and challenges of implementing broad-based screening for eligible patients. Increased familiarity with lung cancer screening guidelines by primary care and specialty clinicians presents an opportunity to improve lung cancer screening rates and to save lives from the most common cause of cancer death in the United States.

Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.

EarlyCDT Lung blood test for risk classification of solid pulmonary nodules: systematic review and economic evaluation

BACKGROUND

EarlyCDT Lung (Oncimmune Holdings plc, Nottingham, UK) is a blood test to assess malignancy risk in people with solid pulmonary nodules. It measures the presence of seven lung cancer-associated autoantibodies. Elevated levels of these autoantibodies may indicate malignant disease. The results of the test might be used to modify the risk of malignancy estimated by existing risk calculators, including the Brock and Herder models.

OBJECTIVES

The objectives were to determine the diagnostic accuracy, clinical effectiveness and cost-effectiveness of EarlyCDT Lung; and to develop a conceptual model and identify evidence requirements for a robust cost-effectiveness analysis.

DATA SOURCES

MEDLINE (including Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE Daily and Ovid MEDLINE), EMBASE, Cochrane Central Register of Controlled Trials, Science Citation Index, EconLit, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment database, NHS Economic Evaluation Database ( NHS EED ) and the international Health Technology Assessment database were searched on 8 March 2021.

REVIEW METHODS

A systematic review was performed of evidence on EarlyCDT Lung, including diagnostic accuracy, clinical effectiveness and cost-effectiveness. Study quality was assessed with the quality assessment of diagnostic accuracy studies-2 tool. Evidence on other components of the pulmonary nodule diagnostic pathway (computerised tomography surveillance, Brock risk, Herder risk, positron emission tomography-computerised tomography and biopsy) was also reviewed. When feasible, bivariate meta-analyses of diagnostic accuracy were performed. Clinical outcomes were synthesised narratively. A simulation study investigated the clinical impact of using EarlyCDT Lung. Additional reviews of cost-effectiveness studies evaluated (1) other diagnostic strategies for lung cancer and (2) screening approaches for lung cancer. A conceptual model was developed.

RESULTS

A total of 47 clinical publications on EarlyCDT Lung were identified, but only five cohorts (695 patients) reported diagnostic accuracy data on patients with pulmonary nodules. All cohorts were small or at high risk of bias. EarlyCDT Lung on its own was found to have poor diagnostic accuracy, with a summary sensitivity of 20.2% (95% confidence interval 10.5% to 35.5%) and specificity of 92.2% (95% confidence interval 86.2% to 95.8%). This sensitivity was substantially lower than that estimated by the manufacturer (41.3%). No evidence on the clinical impact of EarlyCDT Lung was identified. The simulation study suggested that EarlyCDT Lung might potentially have some benefit when considering intermediate risk nodules (10-70% risk) after Herder risk analysis. Two cost-effectiveness studies on EarlyCDT Lung for pulmonary nodules were identified; none was considered suitable to inform the current decision problem. The conceptualisation process identified three core components for a future cost-effectiveness assessment of EarlyCDT Lung: (1) the features of the subpopulations and relevant heterogeneity, (2) the way EarlyCDT Lung test results affect subsequent clinical management decisions and (3) how changes in these decisions can affect outcomes. All reviewed studies linked earlier diagnosis to stage progression and stage shift to final outcomes, but evidence on these components was sparse.

LIMITATIONS

The evidence on EarlyCDT Lung among patients with pulmonary nodules was very limited, preventing meta-analyses and economic analyses.

CONCLUSIONS

The evidence on EarlyCDT Lung among patients with pulmonary nodules is insufficient to draw any firm conclusions as to its diagnostic accuracy or clinical or economic value.

FUTURE WORK

Prospective cohort studies, in which EarlyCDT Lung is used among patients with identified pulmonary nodules, are required to support a future assessment of the clinical and economic value of this test. Studies should investigate the diagnostic accuracy and clinical impact of EarlyCDT Lung in combination with Brock and Herder risk assessments. A well-designed cost-effectiveness study is also required, integrating emerging relevant evidence with the recommendations in this report.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42021242248.

FUNDING

This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 49. See the NIHR Journals Library website for further project information.

High FHL2 mRNA expression and its prognostic value in lung cancer

BACKGROUND

Lung cancer is the most frequent cancer globally with a high number of cancer-related deaths. The 4-and-a-half LIM domain protein 2 (FHL2) is an oncogenic gene, which promotes the proliferation, invasion, and metastasis of cancer cells. In this study, we aimed to demonstrate that lung cancer patients with high FHL2 expression have worse overall survival (OS) and relapse-free survival (RFS).

METHODS

TCGA was used to study FHL2 mRNA expression. Nomograms were used to predict the relationship between FHL2 expression levels and survival. The qRT-PCR was used to detect the FHL2 expression in lung cancer cells. In vitro experiments including CCK-8 assay, wound healing, and Transwell assay were performed.

RESULTS

This study comprised RNA-Seq gene expression data and clinical features for 1018 lung cancer patients. FHL2 was found to be overexpressed in lung cancer tissues. FHL2 demonstrated moderate diagnostic ability for lung cancer (AUC = 0.857). Kaplan-Meier curves and Cox regression analysis revealed the higher FHL2 expression with the poorer OS and RFS (P < 0.001). The nomogram results indicated that FHL2 could be used to predict the survival of lung cancer patients. GSEA analysis results show that high expression of FHL2 is related to glycolysis and unfolded protein reflection. FHL2 was highly expressed in lung cancer cells and related to their proliferation, migration, and invasion ability.

CONCLUSIONS

The high expression level of FHL2 in lung cancer can be used as an independent predictor of prognosis in clinical practice.

Development and validation of a screening model for lung cancer using machine learning: A large-scale, multi-center study of biomarkers in breath

Objectives

Lung cancer (LC) is the largest single cause of death from cancer worldwide, and the lack of effective screening methods for early detection currently results in unsatisfactory curative treatments. We herein aimed to use breath analysis, a noninvasive and very simple method, to identify and validate biomarkers in breath for the screening of lung cancer.

Materials and methods

We enrolled a total of 2308 participants from two centers for online breath analyses using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). The derivation cohort included 1007 patients with primary LC and 1036 healthy controls, and the external validation cohort included 158 LC patients and 107 healthy controls. We used eXtreme Gradient Boosting (XGBoost) to create a panel of predictive features and derived a prediction model to identify LC. The optimal number of features was determined by the greatest area under the receiver‐operating characteristic (ROC) curve (AUC).

Results

Six features were defined as a breath-biomarkers panel for the detection of LC. In the training dataset, the model had an AUC of 0.963 (95% CI, 0.941–0.982), and a sensitivity of 87.1% and specificity of 93.5% at a positivity threshold of 0.5. Our model was tested on the independent validation dataset and achieved an AUC of 0.771 (0.718–0.823), and sensitivity of 67.7% and specificity of 73.0%.

Conclusion

Our results suggested that breath analysis may serve as a valid method in screening lung cancer in a borderline population prior to hospital visits. Although our breath-biomarker panel is noninvasive, quick, and simple to use, it will require further calibration and validation in a prospective study within a primary care setting.

Different clinical characteristics and survival between surgically resected pure and combined small cell lung cancer

BACKGROUND

Small cell lung cancer (SCLC) is the most malignant and common form of neuroendocrine lung cancer with pure (P-SCLC) and combined subtypes (C-SCLC). However, little is known about the differences between these two groups and in this study we aimed to provide a more comprehensive insight into SCLC.

METHODS

Data from 580 postoperative patients with pathologically confirmed SCLC in Shanghai Chest Hospital from January 2010 to December 2020 were collected retrospectively. The clinical characteristics and prognosis were analyzed.

RESULTS

A total of 357 P-SCLC patients and 223 C-SCLC patients were included. The results indicated that P-SCLC appeared to have a higher proportion of being located in the middle lobe than C-SCLC. The incidences of P-SCLC in patients with visceral pleural invasion (VPI) and in stage II were higher than C-SCLC, while C-SCLC was more likely to be accompanied by higher incidences of epidermal growth factor receptor (EGFR) mutation, anaplastic lymphoma kinase (ALK) rearrangement, and higher levels of CEA, SCCA and CYFRA21-1 than P-SCLC. The most common were SCLC combined with large cell neuroendocrine components among 223 C-SCLCs. Survival analysis confirmed a more favorable disease-free survival (DFS) (p = 0.016) and overall survival (OS) (p = 0.024) in patients with P-SCLCs compared with C-SCLCs. Histological type, tumor location, pN stage, adjuvant chemotherapy, serum NSE and CA125 levels were independent risk factors for survival rate in SCLC. In addition, adjuvant chemotherapy was beneficial in improving stage I P-SCLC and C-SCLC DFS and OS rates, and similar results were not seen in adjuvant radiation therapy.

CONCLUSIONS

Patients with C-SCLC have a poorer prognosis than P-SCLC patients. We determined that large cell neuroendocrine carcinoma was the most common additional component of C-SCLC, and patients with this component appeared to have a longer DFS and OS than other combined components.

Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis

Artificial Intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems. AI is poised to change medical practice, and oncology is not an exception to this trend. As the matter of fact, lung cancer has the highest morbidity and mortality worldwide. The leading cause is the complexity of associating early pulmonary nodules with neoplastic changes and numerous factors leading to strenuous treatment choice and poor prognosis. AI can effectively enhance the diagnostic efficiency of lung cancer while providing optimal treatment and evaluating prognosis, thereby reducing mortality. This review seeks to provide an overview of AI relevant to all the fields of lung cancer. We define the core concepts of AI and cover the basics of the functioning of natural language processing, image recognition, human-computer interaction and machine learning. We also discuss the most recent breakthroughs in AI technologies and their clinical application regarding diagnosis, treatment, and prognosis in lung cancer. Finally, we highlight the future challenges of AI in lung cancer and its impact on medical practice.

Carboxypeptidase N2 as a Novel Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma

Carboxypeptidase N2 (CPN2) is a plasma metallo-protease that cleaves basic amino acids from the C-terminal of peptides and proteins. Emerging evidence showed that carboxypeptidases perform many diverse functions in the body and play key roles in tumorigenesis. However, the clinical significance and biological functions of CPN2 in lung adenocarcinoma remain unclear. Our study aimed to explore the potential role and functions of CPN2 in lung adenocarcinoma. The results showed that the transcription level of CPN2 was significantly increased in the tumor tissues of lung adenocarcinoma patients compared to the adjacent normal tissues in The Cancer Genome Atlas cohort (P < 0.05). The survival plots showed that the overall survival of patients with a high expression of CPN2 was significantly lower than that of patients with a low expression of CPN2, both in the Kaplan-Meier database and the clinical sample cohort (P < 0.05). The tissue microarray analysis found that CPN2 protein expression was significantly positively correlated with node status and tumor stage as well as tumor malignancy (P < 0.05). Further univariate and multivariate Cox regression analyses showed that CPN2 may act as an independent prognostic factor in patients with lung adenocarcinoma (P < 0.05). In addition, the analysis of co-expression genes from LinkedOmics showed that CPN2 was positively associated with many genes of fibrillar collagen family members and the PI3K-Akt pathway. The gene set enrichment analysis showed that a higher expression of CPN2 may participate in mTOR, TGF-BETA, NOTCH, TOLL-like-receptor, WNT, and MAPK signaling pathway in lung adenocarcinoma. Notably, the knockdown of CPN2 significantly inhibited the ability of cell proliferation, clone formation, invasion, and migration. Our findings suggested that the upregulation of CPN2 is associated with a worse clinical outcome in lung adenocarcinoma and cancer-related pathways, which laid the foundation for further research on CPN2 during carcinogenesis.

Bone Marrow Mesenchymal Stem Cells (BMSCs)-Originated miR-1298 Impedes the Aggressiveness of Non-Small Cell Lung Cancer by Hindering the Chemokine Receptor 4 (CXCR4)-Induced Epithelial-Mesenchymal Transition (EMT) Process

Exosomes are a subclass of extracellular vesicles, which are produced and secreted by various cells including bone marrow mesenchymal stem cells (BMSCs). BMSCs-originated exosomes can provide a beneficial microenvironment and manipulate tumor growth. However, whether BMSCs-derived miR-1298 exerts roles in NSCLC remains unclear. miR-1298 level was quantified in NSCLC tumor tissues and para-cancerous tissues and NSCLC cell lines. Cells were transfected with miR-136 mimics/miR-136 inhibitors or treatment with BMSCs-originated exosomes to measure cell biological behaviors. Our results found a diminished miR-1298 expression in NSCLC tumor specimens and cell lines. Meanwhile, miR-1298 overexpression or miR-1298 derived from BMSC-originated exosomes can restrain the proliferating feature of NSCLC cells, and impedes cell aggressiveness via hindering EMT process. Additionally, CXCR4 was a target of miR-1298. In conclusion, miR-1298 is served as a tumor suppressor gene in NSCLC and can retard the proliferating and invading behaviors of NSCLC cells by targeting CXCR4 expression, indicating that it might be a novel therapeutic target for treating NSCLC.

Cost-effectiveness of Low-Dose Computed Tomography With a Plasma-Based Biomarker for Lung Cancer Screening in China

IMPORTANCE

China, which has one-third of the worldwide smoking population, has a substantial cancer burden, with lung cancer being the leading cause of cancer-related death. The effectiveness of lung cancer screening for mortality reduction has been confirmed, but the cost-effectiveness of diverse screening modalities remains unclear.

OBJECTIVE

To compare the cost-effectiveness of low-dose computed tomography (LDCT) with a biomarker (micro-RNA signature classifier [MSC]) with that of LDCT alone by screening interval and cumulative smoking exposure.

DESIGN, SETTING, AND PARTICIPANTS

In this economic evaluation, a comparative cost-effectiveness analysis used Markov state transition models that simulated the 1947 to 1971 China birth cohort. Simulated individuals in 8 cohorts of 10 000 entered the study between ages 50 and 74 years and were followed up until death or age 79 years, corresponding to a study period from January 1, 2021, to December 31, 2050. The model was run with a cycle length of 1 year. All the transition probabilities were validated, and health utility values were extracted from published literature. Cost parameters were derived from the databases of local medical insurance bureaus.

MAIN OUTCOMES AND MEASURES

Primary outcomes included life-years, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) with future costs and outcomes discounted by 5%. Screening strategies with a mean ICER less than Chinese yuan (CNY) 212 676 per QALY gained were deemed to be cost-effective. The cost-effectiveness of 7 alternative screening strategies with a screening starting age of 50 years, minimum cumulative smoking exposure of 20 vs 30 pack-years, and screening interval of annual vs 1 time was estimated, including the 2021 China guideline-recommended strategy (LDCT, annual, 30 pack-years) and the 2018 China guideline-recommended strategy (LDCT, annual, 20 pack-years).

RESULTS

In a simulated population of 80 000 individuals, the conjunctive LDCT and MSC screening strategy was estimated to obtain an ICER of CNY -793 995.17 to 254 417.46 (minimum cumulative smoking exposure, 20-30 pack-years) per QALY gained compared with LDCT screening alone. China's 2021 guideline-recommended strategy was not cost-effective compared with the 2018 guideline-recommended strategy, with higher costs and fewer QALYs gained; the QALY loss ranged from 0.02 to 0.15 per person and the increase in cost ranged from CNY 945.89 to CNY 5131.29 per person. LDCT and MSC screening beginning at age 70 to 74 years in individuals with a 20 pack-year smoking history was the most cost-effective strategy, with an ICER of CNY -793 995.17 per QALY gained. Lowering the minimum cumulative smoking exposure for screening from 30 to 20 pack-years and maintaining annual screening were associated with greater cost savings regardless of the screening tool.

CONCLUSIONS AND RELEVANCE

This economic evaluation found that China's 2018 recommendation for lung cancer screening was more cost-effective than the 2021 recommendation. Moreover, the cost-effectiveness of lung cancer screening was improved when MSC was included with LDCT. These findings may be useful for the modification of guidelines for lung cancer screening.

Application of Artificial Intelligence in Lung Cancer

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient's prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm

Cancer driver gene is a type of gene with abnormal alterations that initiate or promote tumorigenesis. Driver genes can be used to reveal the fundamental pathological mechanisms of tumorigenesis. These genes may have pathological changes at different omics levels. Thus, identifying cancer driver genes involving two or more omics levels is essential. In this study, a computational investigation was conducted on lung cancer driver genes. Four omics levels, namely, epigenomics, genomics, transcriptomics, and post-transcriptomics, were involved. From the driver genes at each level, the Laplacian heat diffusion algorithm was executed on a protein–protein interaction network for discovering latent driver genes at this level. A following screen procedure was performed to extract essential driver genes, which contained three tests: permutation, association, and function tests, which can exclude false-positive genes and screen essential ones. Finally, the intersection operation was performed to obtain novel driver genes involving two omic levels. The analyses on obtained genes indicated that they were associated with fundamental pathological mechanisms of lung cancer at two corresponding omics levels.

Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer

This study was to evaluate the diagnostic value of deep learning-optimized chest CT in the patients with lung cancer. 90 patients who were diagnosed with lung cancer by surgery or puncture in hospital were selected as the research subjects. The Mask Region Convolutional Neural Network (Mask-RCNN) model was a typical end-to-end image segmentation model, and Dual Path Network (DPN) was used in nodule detection. The results showed that the accuracy of DPN algorithm model in detecting lung lesions in lung cancer patients was 88.74%, the accuracy of CT diagnosis of lung cancer was 88.37%, the sensitivity was 82.91%, and the specificity was 87.43%. Deep learning-based CT examination combined with serum tumor detection, factoring into Neurospecific enolase (N S E), cytokeratin 19 fragment (CYFRA21), Carcinoembryonic antigen (CEA), and squamous cell carcinoma (SCC) antigen, improved the accuracy to 97.94%, the sensitivity to 98.12%, and the specificity to 100%, all showing significant differences (P < 0.05). In conclusion, this study provides a scientific basis for improving the diagnostic efficiency of CT imaging in lung cancer and theoretical support for subsequent lung cancer diagnosis and treatment.

Assessment of background levels of autoantibodies as a prognostic marker for severe SARS-CoV-2 infection

Background:

Patients with more severe forms of SARS-CoV-2 exhibit activation of immunological cascades. Participants (current or ex-smokers with at least 20 years pack history) in a trial (Early Diagnosis of Lung Cancer, Scotland [ECLS]) of autoantibody detection to predict lung cancer risk had seven autoantibodies measured 5 years before the pandemic. This study compared the response to Covid infection in study participants who tested positive and negative to antibodies to tumour-associated antigens: p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGE A4 and SOX2.

Methods:

Autoantibody data from the ECLS study was deterministically linked to the EAVE II database, a national, real-time prospective cohort using Scotland’s health data infrastructure, to describe the epidemiology of SARS-CoV-2 infection, patterns of healthcare use and outcomes. The strength of associations was explored using a network algorithm for exact contingency table significance testing by permutation.

Results:

There were no significant differences discerned between SARS-CoV-2 test results and EarlyCDT-Lung test results (p = 0.734). An additional analysis of intensive care unit (ICU) admissions detected no significant differences between those who tested positive and negative. Subgroup analyses showed no difference in COVID-19 positivity or death rates amongst those diagnosed with chronic obstructive pulmonary disease (COPD) with positive and negative EarlyCDT results.

Conclusions:

This hypothesis-generating study demonstrated no clinically valuable or statistically significant associations between EarlyCDT positivity in 2013-15 and the likelihood of SARS-CoV-2 positivity in 2020, ICU admission or death in all participants (current or ex-smokers with at least 20 years pack history) or in those with COPD or lung cancer.

Identification of circRNA_001846 as putative non-small cell lung cancer biomarker

CircRNAs play diverse roles in the regulation of oncogenic processes, yet the roles of these circRNAs in non-small cell lung cancer (NSCLC) remain to be fully clarified. Herein, patterns of circRNA expression in NSCLC tissues and paracancerous tissues were assessed, and the relationships between these circRNAs and NSCLC patient clinical findings were assessed. NSCLC tissues were evaluated using a circRNA microarray approach, with Quantitative real‑time polymerase chain reaction (qPCR) qPCR being used to validate candidate circRNA expression levels in NSCLC patients peripheral blood samples. GEO2R was used to analyze the array data. SPSS23.0, GraphPad Prism, and Sigmaplot were utilized for statistical analyses. Overall, 114 circRNAs that were differentially expressed in NSCLC tissue relative to paracancerous tissue levels were identified, of which 77 and 37 were downregulated and upregulated, respectively. CircRNA_001846 were then chosen based on its differential expression score. Consistent with array findings, serum samples from NSCLC patients exhibited circRNA_001846 upregulation relative to those from healthy individuals. The circRNA_001846 was associated with tumor differentiation, lymph node metastasis, and node metastasis stage. Receiver operating characteristic (ROC) curves analyses revealed that levels of circRNA_001846 in the serum were capable of differentiating between individuals diagnosed with NSCLC and controls at a cut off of 3.9496, yielding respective sensitivity and specificity values of 78.2% and 81.1%, with an area under the curve (AUC) value of 0.872. When combined with carcinoembryonic antigen (CEA), this circRNA achieved an improved AUC value of 0.925. CircRNA_001846 may represent a promising diagnostic biomarker for NSCLC.

Analysis of the Repertoires of Circulating Autoantibodies' Specificities as a Tool for Identification of the Tumor-Associated Antigens: Current Problems and Solutions

Circulating autoantibodies against tumor-associated autoantigens (TAA) may serve as valuable biomarkers for a wide range of diagnostic purposes. Modern immunology offers a large variety of methods for in-depth comparative analysis of the repertoires of circulating antibodies' antigenic specificities in health and disease. Nevertheless, this research field so far has met somewhat limited clinical success, while numerous data on the repertoires of circulating autoantibodies' specificities in cancer patients are poorly integrated into the contemporary picture of the immunological and molecular landscapes of human tumors. This review is an attempt to identify and systematize the key and essentially universal conceptual and methodological limitations of analyses of the repertoires of circulating antibodies' antigenic specificities in cancer (expression bias, redundancy of TAA repertoires, identification of natural IgG, the absence of the pathogenetically relevant context in the experimental systems used to detect TAA), as well as to discuss potential and already known methodological improvements that may significantly increase the detectability of the pathogenetically relevant and diagnostically significant bona fide TAA.

Next Generation Sequencing Technology in Lung Cancer Diagnosis

Simple Summary

Lung cancer is still one of the most commonly diagnosed and deadliest cancers in the world. Its diagnosis at an early stage is highly necessary and will improve the standard of care of this disease. The aim of this article is to review the importance and applications of next generation sequencing in lung cancer diagnosis. As observed in many studies, next generation sequencing has been proven as a very helpful tool in the early detection of different types of cancers, including lung cancer, and has been used in the clinic, mainly due to its many advantages, such as low cost, speed, efficacy, low quantity usage of biological samples, and diversity.

Abstract

Lung cancer is still one of the most commonly diagnosed cancers, and one of the deadliest. The high death rate is mainly due to the late stage of diagnosis and low response rate to therapy. Previous and ongoing research studies have tried to discover new reliable and useful cbiomarkers for the diagnosis and prognosis of lung cancer. Next generation sequencing has become an essential tool in cancer diagnosis, prognosis, and evaluation of the treatment response. This article aims to review the leading research and clinical applications in lung cancer diagnosis using next generation sequencing. In this scope, we identified the most relevant articles that present the successful use of next generation sequencing in identifying biomarkers for early diagnosis correlated to lung cancer diagnosis and treatment. This technique can be used to evaluate a high number of biomarkers in a short period of time and from small biological samples, which makes NGS the preferred technique to develop clinical tests for personalized medicine using liquid biopsy, the new trend in oncology.

Clinical Significance and Biological Function of miR-1274a in Non-small Cell Lung Cancer

Since the discovery of microRNAs (miRNAs) as a class of important regulatory molecules, miRNAs are involved in the occurrence and development of tumors. In this paper, we aimed to identify the role of miR-1274a in non-small cell lung cancer (NSCLC). The miR-1274a expression levels in four NSCLC cells and tissues from 125 patients were determined by qRT-PCR assays. Kaplan-Meier survival curves and Cox regression analysis were used to examine the prognostic significance of miR-1274a in NSCLC patients. The CCK-8 and Transwell assays were performed to evaluate the cell proliferation, invasion, and migration ability of NSCLC cells. The miR-1274a expression levels were significantly higher in NSCLC tissues than in adjacent normal tissues, and overexpression of miR-1274a had a poor prognosis in NSCLC patients. Functional studies in two NSCLC cell lines have shown that overexpression of miR-1274a could promote cell proliferation, migration, and invasion. miR-1274a expression levels are upregulated in NSCLC tissues, and a high expression is associated with a poor prognosis in patients with NSCLC. Moreover, miR-1274a promotes cell proliferation, migration, and invasion. Based on our findings, miR-1274a may act as a tumor miRNA in the occurrence and development of NSCLC.

Current and Future Development in Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related deaths in North America and other developed countries. One of the reasons lung cancer is at the top of the list is that it is often not diagnosed until the cancer is at an advanced stage. Thus, the earliest diagnosis of lung cancer is crucial, especially in screening high-risk populations, such as smokers, exposure to fumes, oil fields, toxic occupational places, etc. Based on the current knowledge, it looks that there is an urgent need to identify novel biomarkers. The current diagnosis of lung cancer includes different types of imaging complemented with pathological assessment of biopsies, but these techniques can still not detect early lung cancer developments. In this review, we described the advantages and disadvantages of current methods used in diagnosing lung cancer, and we provide an analysis of the potential use of body fluids as carriers of biomarkers as predictors of cancer development and progression.

Diagnostic and prognostic value of microRNA-2355-3p and contribution to the progression in lung adenocarcinoma

The aim of this study was to delve into the clinical significance and biological function of miR-2355-3p in LUAD. Tissues and blood samples from 116 LUAD patients and blood samples of 90 healthy volunteers were collected. The relative expression of miR-2355-3p was evaluated by quantitative real-time polymerase chain reaction (qRT-PCR). The receiver operating curve (ROC) was plotted for diagnostic value estimation. Kaplan–Meier survival curves were constructed, and multivariate survival analyses were performed for prognostic value estimation. A luciferase reporter assay was carried out to confirm the interaction of miR-2355-3p and ZCCHC14. The CCK-8 and transwell assays were conducted to explore the function of miR-2355-3p on LUAD cells. Rescue experiments were performed to verify the interaction. miR-2355-3p showed an upregulated expression in the samples of LUAD patients. For diagnostic value estimation, the AUC was 0.905 with a sensitivity was 84.5% and specificity of 83.3%. For the estimation of prognostic value, the P-value of log-rank test on K-M curves was 0.002 and 0.006 for overall survival and progression survival, respectively. Based on multivariate Cox regression analysis, miR-2355-3p was a powerful prognostic tool with a P-value of 0.027. ZCCHC14 has binding sites with miR-2355-3p, an expression level, and luciferase activity negatively correlated with miR-2355-3p expression. Knockdown of miR-2355-3p inhibited proliferation, migration, and invasion of LUAD cells, but ZCCHC14 can rescue this inhibition. miR-2355-3p has the potential to be a diagnostic marker and prognostic marker for LUAD. Inhibition of miR-2355-3p in LUAD cells can suppress the progression of LUAD at least partly by direct targeting ZCCHC14.

The Lipid Composition of Serum-Derived Small Extracellular Vesicles in Participants of a Lung Cancer Screening Study

Simple Summary

Molecular components of extracellular vesicles present in serum are potential biomarkers of lung cancer, however, none of them have been validated in the context of an actual early detection of lung cancer. Here, we compared the lipid profiles of vesicles obtained from participants in a lung cancer screening study, including patients with screening-detected cancer and individuals with benign pulmonary nodules or without pathological changes. A few lipids whose levels were different between compared groups were detected, including ceramide Cer(42:1) upregulated in vesicles from cancer patients. Furthermore, a high heterogeneity of lipid profiles of extracellular vesicles was observed, which impaired the performance of classification models based on specific compounds.

Abstract

Molecular components of exosomes and other classes of small extracellular vesicles (sEV) present in human biofluids are potential biomarkers with possible applicability in the early detection of lung cancer. Here, we compared the lipid profiles of serum-derived sEV from three groups of lung cancer screening participants: individuals without pulmonary alterations, individuals with benign lung nodules, and patients with screening-detected lung cancer (81 individuals in each group). Extracellular vesicles and particles were purified from serum by size-exclusion chromatography, and a fraction enriched in sEV and depleted of low-density lipoproteins (LDLs) was selected (similar sized vesicles was observed in all groups: 70–100 nm). The targeted mass-spectrometry-based approach enabled the detection of 352 lipids, including 201 compounds used in quantitative analyses. A few compounds, exemplified by Cer(42:1), i.e., a ceramide whose increased plasma/serum level was reported in different pathological conditions, were upregulated in vesicles from cancer patients. On the other hand, the contribution of phosphatidylcholines with poly-unsaturated acyl chains was reduced in vesicles from lung cancer patients. Cancer-related features detected in serum-derived sEV were different than those of the corresponding whole serum. A high heterogeneity of lipid profiles of sEV was observed, which markedly impaired the performance of classification models based on specific compounds (the three-state classifiers showed an average AUC = 0.65 and 0.58 in the training and test subsets, respectively).

Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts

Serum metabolome is a promising source of molecular biomarkers that could support early detection of lung cancer in screening programs based on low-dose computed tomography. Several panels of metabolites that differentiate lung cancer patients and healthy individuals were reported, yet none of them were validated in the population at high-risk of developing cancer. Here we analyzed serum metabolome profiles in participants of two lung cancer screening studies: MOLTEST-BIS (Poland, n = 369) and SMAC-1 (Italy, n = 93). Three groups of screening participants were included: lung cancer patients, individuals with benign pulmonary nodules, and those without any lung alterations. Concentrations of about 400 metabolites (lipids, amino acids, and biogenic amines) were measured by a mass spectrometry-based approach. We observed a reduced level of lipids, in particular cholesteryl esters, in sera of cancer patients from both studies. Despite several specific compounds showing significant differences between cancer patients and healthy controls within each study, only a few cancer-related features were common when both cohorts were compared, which included a reduced concentration of lysophosphatidylcholine LPC (18:0). Moreover, serum metabolome profiles in both noncancer groups were similar, and differences between cancer patients and both groups of healthy participants were comparable. Large heterogeneity in levels of specific metabolites was observed, both within and between cohorts, which markedly impaired the accuracy of classification models: The overall AUC values of three-state classifiers were 0.60 and 0.51 for the test (MOLTEST) and validation (SMAC) cohorts, respectively. Therefore, a hypothetical metabolite-based biomarker for early detection of lung cancer would require adjustment to lifestyle-related confounding factors that putatively affect the composition of serum metabolome.

A Bayesian approach for diagnostic accuracy of malignant peripheral nerve sheath tumors: a systematic review and meta-analysis

Background

Malignant peripheral nerve sheath tumors (MPNST) carry a dismal prognosis and require early detection and complete resection. However, MPNSTs are prone to sampling errors and biopsies or resections are cumbersome and possibly damaging in benign peripheral nerve sheath tumor (BPNST). This study aimed to systematically review and quantify the diagnostic accuracy of noninvasive tests for distinguishing MPNST from BPNST.

Methods

Studies on accuracy of MRI, FDG-PET (fluorodeoxyglucose positron emission tomography), and liquid biopsies were identified in PubMed and Embase from 2000 to 2019. Pooled accuracies were calculated using Bayesian bivariate meta-analyses. Individual level-patient data were analyzed for ideal maximum standardized uptake value (SUV_max) threshold on FDG-PET.

Results

Forty-three studies were selected for qualitative synthesis including data on 1875 patients and 2939 lesions. Thirty-five studies were included for meta-analyses. For MRI, the absence of target sign showed highest sensitivity (0.99, 95% CI: 0.94-1.00); ill-defined margins (0.94, 95% CI: 0.88-0.98); and perilesional edema (0.95, 95% CI: 0.83-1.00) showed highest specificity. For FDG-PET, SUV_max and tumor-to-liver ratio show similar accuracy; sensitivity 0.94, 95% CI: 0.91-0.97 and 0.93, 95% CI: 0.87-0.97, respectively, specificity 0.81, 95% CI: 0.76-0.87 and 0.79, 95% CI: 0.70-0.86, respectively. SUV_max ≥3.5 yielded the best accuracy with a sensitivity of 0.99 (95% CI: 0.93-1.00) and specificity of 0.75 (95% CI: 0.56-0.90).

Conclusions

Biopsies may be omitted in the presence of a target sign and the absence of ill-defined margins or perilesional edema. Because of diverse radiological characteristics of MPNST, biopsies may still commonly be required. In neurofibromatosis type 1, FDG-PET scans may further reduce biopsies. Ideal SUV_max threshold is ≥3.5.

Ongoing challenges in implementation of lung cancer screening

Lung cancer is the leading cause of cancer deaths in Europe and around the world. Although available therapies have undergone considerable development in the past decades, the five-year survival rate for lung cancer remains low. This sobering outlook results mainly from the advanced stages of cancer most patients are diagnosed with. As the population at risk is relatively well defined and early stage disease is potentially curable, lung cancer outcomes may be improved by screening. Several studies already show that lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality. However, for a successful implementation of LCS programmes, several challenges have to be overcome: selection of high-risk individuals, standardization of nodule classification and measurement, specific training of radiologists, optimization of screening intervals and screening duration, handling of ancillary findings are some of the major points which should be addressed. Last but not least, the psychological impact of screening on screened individuals and the impact of potential false positive findings should not be neglected. The aim of this review is to discuss the different challenges of implementing LCS programmes and to give some hints on how to overcome them. Finally, we will also discuss the psychological impact of screening on quality of life and the importance of smoking cessation.

Serum Exosomes and Their miRNA Load-A Potential Biomarker of Lung Cancer

Early detection of lung cancer in screening programs is a rational way to reduce mortality associated with this malignancy. Low-dose computed tomography, a diagnostic tool used in lung cancer screening, generates a relatively large number of false-positive results, and its complementation with molecular biomarkers would greatly improve the effectiveness of such programs. Several biomarkers of lung cancer based on different components of blood, including miRNA signatures, were proposed. However, only a few of them have been positively validated in the context of early cancer detection yet, which imposes a constant need for new biomarker candidates. An emerging source of cancer biomarkers are exosomes and other types of extracellular vesicles circulating in body fluids. Hence, different molecular components of serum/plasma-derived exosomes were tested and showed different levels in lung cancer patients and healthy individuals. Several studies focused on the miRNA component of these vesicles. Proposed signatures of exosome miRNA had promising diagnostic value, though none of them have yet been clinically validated. These signatures involved a few dozen miRNA species overall, including a few species that recurred in different signatures. It is worth noting that all these miRNA species have cancer-related functions and have been associated with lung cancer progression. Moreover, a few of them, including known oncomirs miR-17, miR-19, miR-21, and miR-221, appeared in multiple miRNA signatures of lung cancer based on both the whole serum/plasma and serum/plasma-derived exosomes.

Non-Coding RNAs in Cancer Diagnosis and Therapy: Focus on Lung Cancer

Over the last several decades, clinical evaluation and treatment of lung cancers have largely improved with the classification of genetic drivers of the disease, such as EGFR, ALK, and ROS1. There are numerous regulatory factors that exert cellular control over key oncogenic pathways involved in lung cancers. In particular, non-coding RNAs (ncRNAs) have a diversity of regulatory roles in lung cancers such that they have been shown to be involved in inducing proliferation, suppressing apoptotic pathways, increasing metastatic potential of cancer cells, and acquiring drug resistance. The dysregulation of various ncRNAs in human cancers has prompted preclinical studies examining the therapeutic potential of restoring and/or inhibiting these ncRNAs. Furthermore, ncRNAs demonstrate tissue-specific expression in addition to high stability within biological fluids. This makes them excellent candidates as cancer biomarkers. This review aims to discuss the relevance of ncRNAs in cancer pathology, diagnosis, and therapy, with a focus on lung cancer.

Humoral immune response to epidermal growth factor receptor in lung cancer

The aim of this study was to explore the potential value of autoantibody to epidermal growth factor receptor (EGFR) in the diagnosis of lung cancer (LC) and its relation with EGFR mutations. Enzyme-linked immunosorbent assay (ELISA) was performed to detect the level of autoantibody to EGFR in sera from 254 LC patients and 222 normal controls (NCs). Besides, the mRNA and protein levels of EGFR were investigated in Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) database, respectively. The level of autoantibody to EGFR (anti-EGFR) in LC even different types of LC was obviously higher than that in NC (P < 0.05). The area under the curve (AUC) of anti-EGFR was 0.695 (95% CI 0.645-0.742) when comparing LC patients with NC, while the AUC of carcinoembryonic antigen (CEA) was 0.681 (95% CI 0.629-0.730). Moreover, by integrating anti-EGFR with CEA to diagnose LC, the AUC was up to 0.784 (95% CI 0.737-0.826). However, the expression level of autoantibody to EGFR had no difference between LC patients with and without EGFR gene mutation (P > 0.05). EGFR mRNA expression level was obviously upregulated in squamous cell carcinoma (SCC) tissues compared with normal tissues (P < 0.05), but not in adenocarcinoma (ADC) (P > 0.05). The study confirmed that anti-EGFR could be a potential biomarker for LC diagnosis; additionally, it could improve the diagnostic value of CEA in clinical work.