Current status of circulating protein biomarkers to aid the early detection of lung cancer

Lung Cancer Screening-Trends and Current Studies

Lung cancer is the leading cause of death among all the oncological diseases worldwide. This applies to both women and men; however, the incidence and mortality among women is on the rise. In 2020, lung cancer was responsible for 1.8 million deaths (18%). More than 90% of lung cancer cases and 77.1% of lung cancer deaths occur in countries with high and very high HDI (human development index) values. The aim of our study is to the present trends and most recent studies aimed at lung cancer screening. In the face of the persistently high mortality rate, conducting research aimed at extending already-implemented diagnostic algorithms and behavioural interventions focused on smoking cessation is recommended.

Biosynthesized metallic nanoparticles: A new era in cancer therapy

Cancer remains a global health crisis, claiming countless lives throughout the years. Traditional cancer treatments like chemotherapy and radiation often bring about severe side effects, underscoring the pressing need for innovative, more efficient, and less toxic therapies. Nanotechnology has emerged as a promising technology capable of producing environmentally friendly anticancer nanoparticles. Among various nanoparticle types, metal-based nanoparticles stand out due to their exceptional performance and ease of use in methods of imaging. The widespread accessibility of biological precursors for synthesis based on plants of metal nanoparticles has made large-scale, eco-friendly production feasible. This evaluation provides a summary of the green strategy for synthesizing metal-based nanoparticles and explores their applications. Moreover, this review delves into the potential of phyto-based metal nanoparticles in combating cancer, shedding light on their probable mechanisms of action. These insights are invaluable for enhancing both biomedical and environmental applications. The study also touches on the numerous potential applications of nanotechnology in the field of medicine. Consequently, this research offers a concise and well-structured summary of nanotechnology, which should prove beneficial to researchers, engineers, and scientists embarking on future research endeavors.

Liquid biopsy for early detection of lung cancer

Lung cancer is the leading cause of cancer-related mortality worldwide. Early cancer detection plays an important role in improving treatment success and patient prognosis. In the past decade, liquid biopsy became an important tool for cancer diagnosis, as well as for treatment selection and response monitoring. Liquid biopsy is a broad term that defines a non-invasive test done on a sample of blood or other body fluid to look for cancer cells or other analytes that can include DNA, RNA, or other molecules released by tumor cells. Liquid biopsies mainly include circulating tumor DNA, circulating RNA, microRNA, proteins, circulating tumor cells, exosomes, and tumor-educated platelets. This review summarizes the progress and clinical application potential of liquid biopsy for early detection of lung cancer.

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.

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.

Diagnostic and prognostic value of serum miR-145 and vascular endothelial growth factor in non-small cell lung cancer

Previous studies have reported the diagnostic and prognostic value of serum microRNA (miR)-145 and vascular endothelial growth factor (VEGF) levels in various types of cancer; however, their clinical use in non-small cell lung cancer (NSCLC) remains unclear. The present study included 215 patients, 106 with NSCLC and 109 with other lung diseases (OLDs). miR-145 expression levels were determined using reverse transcription-quantitative PCR (RT-qPCR) and VEGF levels were measured using an ELISA. The diagnostic performance was assessed using a receiver operating characteristic curve and area under the curve (AUC) analysis. A Kaplan-Meier survival curve and Cox regression analysis were employed to evaluate the prognostic significance of the markers. The biological function of miR-145 was examined in A549 and H1792 cell lines. The effects of miR-145 on cell proliferation of NSCLC cells were evaluated by flow cytometry, and the expression levels of miR-145 and cell cycle-related genes were determined by RT-qPCR. The results revealed that miR-145 and VEGF exhibited fair diagnostic performance [AUC, 0.61 (95% CI, 0.55-0.68) and AUC, 0.64 (95% CI, 0.57-0.71), respectively]. Combining age and smoking status with miR-145 and VEGF provided the best model for differentiating patients with NSCLC from those with OLDs (AUC, 0.76; 95% CI, 0.69-0.83). Furthermore, low serum miR-145 levels were associated with poor overall survival [hazard ratio (HR), 0.48; 95% CI, 0.27-0.85], whereas high VEGF levels were not associated with poor overall survival (HR, 1.47; 95% CI, 0.81-2.68). In addition, the results of the in vitro experiments indicated that miR-145 decreased cell proliferation via the induction of cell cycle arrest. In conclusion, the findings of the present study suggested that combining miR-145 and VEGF levels with clinical risk factors may be a potential diagnostic scheme for NSCLC. In addition, serum miR-145 may be used as a prognostic marker. These results indicated that miR-145 may function as a tumor suppressor in NSCLC.

Green Metallic Nanoparticles for Cancer Therapy: Evaluation Models and Cancer Applications

Metal-based nanoparticles are widely used to deliver bioactive molecules and drugs to improve cancer therapy. Several research works have highlighted the synthesis of gold and silver nanoparticles by green chemistry, using biological entities to minimize the use of solvents and control their physicochemical and biological properties. Recent advances in evaluating the anticancer effect of green biogenic Au and Ag nanoparticles are mainly focused on the use of conventional 2D cell culture and in vivo murine models that allow determination of the half-maximal inhibitory concentration, a critical parameter to move forward clinical trials. However, the interaction between nanoparticles and the tumor microenvironment is not yet fully understood. Therefore, it is necessary to develop more human-like evaluation models or to improve the existing ones for a better understanding of the molecular bases of cancer. This review provides recent advances in biosynthesized Au and Ag nanoparticles for seven of the most common and relevant cancers and their biological assessment. In addition, it provides a general idea of the in silico, in vitro, ex vivo, and in vivo models used for the anticancer evaluation of green biogenic metal-based nanoparticles.

Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement

IMPORTANCE

Lung cancer is the second most common cancer and the leading cause of cancer death in the US. In 2020, an estimated 228 820 persons were diagnosed with lung cancer, and 135 720 persons died of the disease. The most important risk factor for lung cancer is smoking. Increasing age is also a risk factor for lung cancer. Lung cancer has a generally poor prognosis, with an overall 5-year survival rate of 20.5%. However, early-stage lung cancer has a better prognosis and is more amenable to treatment.

OBJECTIVE

To update its 2013 recommendation, the US Preventive Services Task Force (USPSTF) commissioned a systematic review on the accuracy of screening for lung cancer with low-dose computed tomography (LDCT) and on the benefits and harms of screening for lung cancer and commissioned a collaborative modeling study to provide information about the optimum age at which to begin and end screening, the optimal screening interval, and the relative benefits and harms of different screening strategies compared with modified versions of multivariate risk prediction models.

POPULATION

This recommendation statement applies to adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years.

EVIDENCE ASSESSMENT

The USPSTF concludes with moderate certainty that annual screening for lung cancer with LDCT has a moderate net benefit in persons at high risk of lung cancer based on age, total cumulative exposure to tobacco smoke, and years since quitting smoking.

RECOMMENDATION

The USPSTF recommends annual screening for lung cancer with LDCT in adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years. Screening should be discontinued once a person has not smoked for 15 years or develops a health problem that substantially limits life expectancy or the ability or willingness to have curative lung surgery. (B recommendation) This recommendation replaces the 2013 USPSTF statement that recommended annual screening for lung cancer with LDCT in adults aged 55 to 80 years who have a 30 pack-year smoking history and currently smoke or have quit within the past 15 years.

High-throughput assessment of the antibody profile in ovarian cancer ascitic fluids

The identification of effective biomarkers for early diagnosis, prognosis, and response to treatments remains a challenge in ovarian cancer (OC) research. Here, we present an unbiased high-throughput approach to profile ascitic fluid autoantibodies in order to obtain a tumor-specific antigen signature in OC. We first reported the reactivity of immunoglobulins (Igs) purified from OC patient ascites towards two different OC cell lines. Using a discovery set of Igs, we selected tumor-specific antigens from a phage display cDNA library. After biopanning, 700 proteins were expressed as fusion protein and used in protein array to enable large-scale immunoscreening with independent sets of cancer and noncancerous control. Finally, the selected antigens were validated by ELISA. The initial screening identified eight antigenic clones: CREB3, MRPL46, EXOSC10, BCOR, HMGN2, HIP1R, OLFM4, and KIAA1755. These antigens were all validated by ELISA in a study involving ascitic Igs from 153 patients (69 with OC, 34 with other cancers and 50 without cancer), with CREB3 showing the highest sensitivity (86.95%) and specificity (98%). Notably, we were able to identify an association between the tumor-associated (TA) antibody response and the response to a first-line tumor treatment (platinum-based chemotherapy). A stronger association was found by combining three antigens (BCOR, CREB3, and MRLP46) as a single antibody signature. Measurement of an ascitic fluid antibody response to multiple TA antigens may aid in the identification of new prognostic signatures in OC patients and shift attention to new potentially relevant targets.

Cancer biomarker development and two-dimensional difference gel electrophoresis (2D-DIGE)

Cancer results from the accumulation of genomic alterations. As the genome is functionally translated to the proteome and regulates tumor cell behavior, proteomics studies are expected to further the current understanding of the molecular mechanisms underlying carcinogenesis and cancer progression. Biomarkers are potential tools to classify cancers for therapy, predict responses to treatments, and support treatment-related decision-making. Biomarker development has been actively pursued in oncology by proteomic approaches. Two-dimensional difference gel electrophoresis (2D-DIGE) is a proteomics technique based on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). In 2D-DIGE, protein samples are labeled with distinct fluorescent dyes before fractionation via 2D-PAGE. 2D-DIGE offers advantages to identify biomarker candidates, including reproducibility, high sensitivity, comprehensiveness, and high throughput. 2D-DIGE has contributed to the establishment of tissue biomarkers, which potentially facilitate precision medicine. 2D-DIGE is thus expected to yield major advancements in cancer biomarker identification and development.

Experimental study of peripheral-blood pro-surfactant protein B for screening non-small cell lung cancer

Purpose:

To evaluate the possibility of using peripheral-blood presurfactant protein B (Pro-SFTPB) for screening non-small cell lung cancer (NSCLC).

Methods:

A total of 873 healthy volunteers and 165 lung cancer patients hospitalized in the Fifth People's Hospital of Dalian were tested Pro-SFTPB once every half year from January 2014 to September 2015. The healthy volunteers were also conducted spiral computed tomography (CT) examination once every year. The data were then com-pared and statistically analyzed.

Results:

The positive expression rate of Pro-SFTPB in NSCLC was significantly higher than that in healthy volunteers, and significantly higher in lung adenocarcinoma than in squamous cell carcinoma; additionally, the expression rate was increased with the in-crease of smoking index, and the intergroup differences showed statistical signifi-cance (p≤0.05). The positive rate of newly diagnosed lung cancer was 29.55%, higher than healthy volunteers (22.34%), but there was no significant difference (p>0.05).

Conclusion:

Pro-SFTPB is over expressed in non-small cell lung cancer, especially in lung adeno-carcinoma, but it can't be used as a clinical screening tool for lung cancer.

Liquid biopsy for early detection of lung cancer

PURPOSE OF REVIEW

The possibility of complete recovery for a lung cancer patient depends on very early diagnosis, as it allows total surgical resection. Screening for this cancer in a high-risk population can be performed using a radiological approach, but this holds a certain number of limitations. Liquid biopsy could become an alternative and complementary screening approach to chest imaging for early diagnosis of lung cancer.

RECENT FINDINGS

Several circulating biomarkers indicative of lung cancer can be investigated in blood, such as circulating tumor cells, circulating free nucleic acids (RNA and DNA) and proteins. However, none of these biomarkers have yet been adopted in routine clinical practice and studies are ongoing to confirm or not the usefulness and practical interest in routine early diagnosis and screening for lung cancers.

SUMMARY

Several potential circulating biomarkers for the early detection of lung cancer exist. When coupled to thoracic imaging, these biomarkers must give diagnosis of a totally resectable lung cancer and potentially provide new recommendations for surveillance by imagery of high-risk populations without a detectable nodule. Optimization of the specificity and sensitivity of the detection methods as well as standardization of the techniques is essential before considering for daily practice a liquid biopsy as an early diagnostic tool, or possibly as a predictive test, of lung cancer.

Integration of multiple "OMIC" biomarkers: A precision medicine strategy for lung cancer

More than half of all new lung cancer diagnoses are made in patients with locally advanced or metastatic disease, at which point therapeutic options are scarce. It is anticipated, however, that the widespread use of Low-Dose Computed Tomography (LDCT) screening, will lead to a greater proportion of lung cancers being diagnosed at an early, operable, stage. Still, the overall rate of recurrence for surgically treated Stage I lung cancer patients is up to 30% within 5 years of diagnosis. Thus, the identification and clinical application of biomarkers of early stage lung cancer are a pressing medical need. The integrative analysis of "omic," clinical and epidemiological data for single patients is a core principle of precision medicine. Through rigorous bioinformatics and statistical analyses we have identified biomarkers of early-stage lung cancer based on DNA methylation, expression of mRNA and miRNA, inflammatory cytokines, and urinary metabolites. Beyond a more comprehensive understanding of the molecular taxonomy of lung cancer, these biomarkers can have very practical implications in the context of unmet clinical needs of early stage lung cancer patients: First, current guidelines for LDCT screening broadly include individuals based on age and history of heavy smoking. Tumor-derived circulating biomarkers in the blood and urine associated with lung cancer risk could narrow and prioritize individuals for LDCT screening. Second, a high number of nodules are identified by LDCT, of which fewer than 5% are finally diagnosed as lung cancer. Biomarkers may help discriminate malignant nodules from benign or indolent lesions. Third, the expected rise in the numbers of lung cancer patients diagnosed at an early stage will necessitate new treatment options. Circulating, urinary and tissue-based biomarkers that molecularly categorize Stage I patients after tumor resection can help identify high-risk patients who may benefit from adjuvant chemotherapy or innovative immunotherapy regimens.

Biomarkers along the continuum of care in lung cancer

Blood-based biomarkers are valuable diagnostic tools for the management of lung cancer patients. They support not only differential diagnosis and histological subtyping, but are also applied for estimation of prognosis, stratification for specific therapies, monitoring of therapy response, surveillance monitoring and early detection of residual or progressive disease. Early diagnosis of lung cancer in high risk populations (screening) is a promising future indication but poses high medical and economic challenges to marker performance. The five mostly used classical 'tumor markers' show characteristic profiles of sensitivity and specificity for non-small cell lung cancer (NSCLC) like cytokeratin 19-fragments (CYFRA 21-1), carcino-embryonic antigen (CEA) and squamous cancer cell antigen (SCCA) as well as for small cell lung cancer (SCLC) like progastrin-releasing peptide (ProGRP) and neuron-specific enolase (NSE). Combined use and pattern recognition approaches enable highly accurate diagnosis, subtyping and therapy monitoring. For the interpretation of serial measurements on an individual level, marker-specific algorithms have to be developed. So-called companion diagnostics identify druggable molecular changes in signaling pathways of tumor tissue that can be addressed by targeted therapies. New highly sensitive technologies enable the convenient and serial molecular characterization on circulating tumor DNA (ctDNA) in the blood, too. This approach is helpful when biopsies are not available and to overcome tumor molecular heterogeneity and plasticity. As only a portion of patients have such druggable molecular changes, future strategies will imply the combined use of classical and new ctDNA-based biomarkers to optimize the management of lung cancer patients during the course of disease.

Autoantibodies: Opportunities for Early Cancer Detection

Cancer cells can induce an immunological response resulting in the production of tumor-associated (TA) autoantibodies. These serum immunobiomarkers have been detected for a range of cancers at an early stage before the development of clinical symptoms. Their measurement is minimally invasive and cost effective using established technologies. TA autoantibodies are present in a clinically significant number of individuals and could supplement current screening modalities to aid early diagnosis of high-risk populations and assist the clinical management of patients. Here we review their production, discovery, and validation as biomarkers for cancer and their current and future potential as clinical tools.

Precision targeted therapy of ovarian cancer

The review is aimed at describing modern approaches to detection as well as precision and personalized treatment of ovarian cancer. Modern methods and future directions of nanotechnology-based targeted and personalized therapy are discussed.

Circulating Tumor Cell and Cell-free Circulating Tumor DNA in Lung Cancer

Circulating tumor cells (CTCs) are tumor cells that are separated from the primary site or metastatic lesion and disseminate in blood circulation. CTCs are considered to be part of the long process of cancer metastasis. As a 'liquid biopsy', CTC molecular examination and investigation of single cancer cells create an important opportunity for providing an understanding of cancer biology and the process of metastasis. In the last decade, we have seen dramatic development in defining the role of CTCs in lung cancer in terms of diagnosis, genomic alteration determination, treatment response and, finally, prognosis prediction. The aims of this review are to understand the basic biology and to review methods of detection of CTCs that apply to the various types of solid tumor. Furthermore, we explored clinical applications, including treatment monitoring to anticipate therapy resistance as well as biomarker analysis, in the context of lung cancer. We also explored the potential use of cell-free circulating tumor DNA (ctDNA) in the genomic alteration analysis of lung cancer.

Tandem mass spectrometry-based newborn screening strategy could be used to facilitate rapid and sensitive lung cancer diagnosis

Objective

Newborn screening (NBS) helps in the early detection of inborn errors of metabolism (IEM). The most effective NBS strategy prevailing in clinics is tandem mass spectrometry (MS/MS) analysis using dried blood spot (DBS) samples. Taking lung cancer (LC) as an example, this study tried to explore if this technique could be of any assistance for the discovery of tumor metabolite markers.

Materials and methods

Twenty-six acylcarnitines and 23 amino acids, which are commonly used in IEM screening, were quantified using DBS samples from 222 LC patients, 118 benign lung disease (LD) patients, and 96 healthy volunteers (CONT). Forty-four calculated ratios based on the abovementioned metabolites were also included using MS/MS quantification results.

Results

This pilot study led to the findings of 65 significantly changed amino acids, acylcarnitines, and some of their ratios for the LC, LD, and CONT groups. Among the differential parameters, 12 items showed reverse changing trends between the LC and LD groups compared to the CONT group. Regression analysis demonstrated that six of them – Arg, Pro, C10:1, Arg/Orn, Cit/Arg, and C5-OH/C0 – could be used to diagnose LC with a sensitivity of 91.3% and a specificity of 92.7%.

Conclusion

This study demonstrated the DBS-based MS/MS strategy was a promising tool for the discovery of tumor metabolite markers. Remarkably, this MS/MS analysis could be finished in several minutes, implying that it was a proper measure complementary to the traditional serum protein biomarker quantitation strategy for cancerous disease diagnosis and screening purposes.

New ultrasensitive resonant photonic platform for label-free biosensing

A multi-analyte biosensing platform with ultra-high resolution ( = 0.2 ng/mL),-which is appropriate for the detection in the human serum of a wide range of biomarkers, e.g. those allowing the lung cancer early diagnosis, has been designed. The platform is based on a new configuration of planar ring resonator. The very strong light-matter interaction enabled by the micro-cavity allows a record limit-of-detection of 0.06 pg/mm(2), five times better than the state-of-the-art. The device with footprint = 2,200 μm(2) for each ring, due to its features, has the potential to be integrated in lab-on-chip microsystems for large-scale screenings of people with high risk of developing cancer.

Cancer proteomics: developments in technology, clinical use and commercialization

In the last two decades, advances in genomic, transcriptomic and proteomic methods have enabled us to identify and classify cancers by their molecular profiles. Many anticipate that a molecular taxonomy of cancer will not only lead to more effective subtyping of cancers but also earlier diagnoses, more informative prognoses and more targeted treatments. This article reviews recent technological developments in the field of proteomics, recent discoveries in proteomic cancer biomarker research and trends in clinical use. Readers are also informed of examples of successful commercialization, and the future of proteomics in cancer diagnostics.

A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer

BACKGROUND

Lung cancer is an important and common cancer that constitutes a major public health problem, but early detection of small cell lung cancer can significantly improve the survival rate of cancer patients. A number of serum biomarkers have been used in the diagnosis of lung cancers; however, they exhibit low sensitivity and specificity.

METHODS

We used biochemical methods to measure blood levels of lactate dehydrogenase (LDH), C-reactive protein (CRP), Na+, Cl-, carcino-embryonic antigen (CEA), and neuron specific enolase (NSE) in 145 small cell lung cancer (SCLC) patients and 155 non-small cell lung cancer and 155 normal controls. A gene expression programming (GEP) model and Receiver Operating Characteristic (ROC) curves incorporating these biomarkers was developed for the auxiliary diagnosis of SCLC.

RESULTS

After appropriate modification of the parameters, the GEP model was initially set up based on a training set of 115 SCLC patients and 125 normal controls for GEP model generation. Then the GEP was applied to the remaining 60 subjects (the test set) for model validation. GEP successfully discriminated 281 out of 300 cases, showing a correct classification rate for lung cancer patients of 93.75% (225/240) and 93.33% (56/60) for the training and test sets, respectively. Another GEP model incorporating four biomarkers, including CEA, NSE, LDH, and CRP, exhibited slightly lower detection sensitivity than the GEP model, including six biomarkers. We repeat the models on artificial neural network (ANN), and our results showed that the accuracy of GEP models were higher than that in ANN. GEP model incorporating six serum biomarkers performed by NSCLC patients and normal controls showed low accuracy than SCLC patients and was enough to prove that the GEP model is suitable for the SCLC patients.

CONCLUSION

We have developed a GEP model with high sensitivity and specificity for the auxiliary diagnosis of SCLC. This GEP model has the potential for the wide use for detection of SCLC in less developed regions.