Lung Cancer Subtyping: A Short Review

As of 2022, lung cancer is the most commonly diagnosed cancer worldwide, with the highest mortality rate. There are three main histological types of lung cancer, and it is more important than ever to accurately identify the subtypes since the development of personalized, type-specific targeted therapies that have improved mortality rates. Traditionally, the gold standard for the confirmation of histological subtyping is tissue biopsy and histopathology. This, however, comes with its own challenges, which call for newer sampling techniques and adjunctive tools to assist in and improve upon the existing diagnostic workflow. This review aims to list and describe studies from the last decade (n = 47) that investigate three such potential omics techniques-namely (1) transcriptomics, (2) proteomics, and (3) metabolomics, as well as immunohistochemistry, a tool that has already been adopted as a diagnostic adjunct. The novelty of this review compared to similar comprehensive studies lies with its detailed description of each adjunctive technique exclusively in the context of lung cancer subtyping. Similarities between studies evaluating individual techniques and markers are drawn, and any discrepancies are addressed. The findings of this study indicate that there is promising evidence that supports the successful use of omics methods as adjuncts to the subtyping of lung cancer, thereby directing clinician practice in an economical and less invasive manner.

Potential factors of cytokeratin fragment 21-1 and cancer embryonic antigen for mediastinal lymph node metastasis in lung cancer

Objective: Lung cancer is a common malignant tumor, characterized by being difficult to detect and lacking specific clinical manifestations. This study aimed to find out the risk factors of mediastinal lymph node metastasis and explore the correlation between serum tumor markers and mediastinal lymph node metastasis and lung cancer prognosis.

Methods: A retrospective study of 3,042 lung cancer patients (330 patients with mediastinal lymph node metastasis and 2,712 patients without mediastinal lymph node metastasis) collected from the First Affiliated Hospital of Nanchang University from April 1999 to July 2020. The patients were divided into two groups, namely, mediastinal lymph node metastasis group and non-mediastinal lymph node metastasis group. Student’s t test, non-parametric rank sum test and chi-square test were used to describe whether there is a significant difference between the two groups. We compared the serum biomarkers of the two groups of patients, including exploring serum alkaline phosphatase (ALP), calcium hemoglobin (HB), alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), CA125, CA-199, CA -153, cytokeratin fragment 19 (CYFRA 21-1), total prostate specific antigen (TPSA), neuron-specific enolase (NSE) levels and the incidence and prognosis of lung cancer mediastinal lymph node metastasis. Binary logistic regression analysis was used to determine its risk factors, and receiver operating curve (ROC) analysis was used to evaluate its diagnostic value for mediastinal lymph node metastasis.

Results: Binary logistic regression analysis showed that carcinoembryonic antigen and CYFRA 21-1 were independent risk factors for mediastinal lymph node metastasis in patients with lung cancer (p < 0.001 and p = 0.002, respectively). The sensitivity and specificity of CEA for the diagnosis of mediastinal lymph node metastasis were 90.2 and 7.6%, respectively; CYFRA 21-1 were 0.6 and 99.0%, respectively.

Conclusion: Serum CEA and CYFRA 21-1 have predictive value in the diagnosis of mediastinal lymph node metastasis in patients with lung cancer.

A novel gene selection algorithm for cancer classification using microarray datasets

Background

Microarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to the large number of genes involved. This fact is known as a curse of dimensionality, which is a challenging problem. Gene selection is a promising approach that addresses this problem and plays an important role in the development of efficient cancer classification due to the fact that only a small number of genes are related to the classification problem. Gene selection addresses many problems in microarray datasets such as reducing the number of irrelevant and noisy genes, and selecting the most related genes to improve the classification results.

Methods

An innovative Gene Selection Programming (GSP) method is proposed to select relevant genes for effective and efficient cancer classification. GSP is based on Gene Expression Programming (GEP) method with a new defined population initialization algorithm, a new fitness function definition, and improved mutation and recombination operators. . Support Vector Machine (SVM) with a linear kernel serves as a classifier of the GSP.

Results

Experimental results on ten microarray cancer datasets demonstrate that Gene Selection Programming (GSP) is effective and efficient in eliminating irrelevant and redundant genes/features from microarray datasets. The comprehensive evaluations and comparisons with other methods show that GSP gives a better compromise in terms of all three evaluation criteria, i.e., classification accuracy, number of selected genes, and computational cost. The gene set selected by GSP has shown its superior performances in cancer classification compared to those selected by the up-to-date representative gene selection methods.

Conclusion

Gene subset selected by GSP can achieve a higher classification accuracy with less processing time.

Current and Prospective Protein Biomarkers of Lung Cancer

Lung cancer is a malignant lung tumor with various histological variants that arise from different cell types, such as bronchial epithelium, bronchioles, alveoli, or bronchial mucous glands. The clinical course and treatment efficacy of lung cancer depends on the histological variant of the tumor. Therefore, accurate identification of the histological type of cancer and respective protein biomarkers is crucial for adequate therapy. Due to the great diversity in the molecular-biological features of lung cancer histological types, detection is impossible without knowledge of the nature and origin of malignant cells, which release certain protein biomarkers into the bloodstream. To date, different panels of biomarkers are used for screening. Unfortunately, a uniform serum biomarker composition capable of distinguishing lung cancer types is yet to be discovered. As such, histological analyses of tumor biopsies and immunohistochemistry are the most frequently used methods for establishing correct diagnoses. Here, we discuss the recent advances in conventional and prospective aptamer based strategies for biomarker discovery. Aptamers like artificial antibodies can serve as molecular recognition elements for isolation detection and search of novel tumor-associated markers. Here we will describe how these small synthetic single stranded oligonucleotides can be used for lung cancer biomarker discovery and utilized for accurate diagnosis and targeted therapy. Furthermore, we describe the most frequently used in-clinic and novel lung cancer biomarkers, which suggest to have the ability of differentiating between histological types of lung cancer and defining metastasis rate.

Tumour-associated antigens and their anti-cancer applications

So far, a number of tumour-associated antigens (TAAs), such as heat shock proteins, alpha-fetoprotein, carcino-embryonic antigen and others have been identified in a variety of malignant tumours. Differences in the expression levels of TAAs in cancers compared with normal cells have led to these antigens being investigated as diagnostic and prognostic biomarkers or exciting targets in cancer treatment. Here, we systematically list the current representative TAAs to shed some light on current approaches and challenges for their anti-cancer application in cancer therapy. In this review, we discuss the ongoing pre-clinical studies and clinical development of TAAs in human cancers, and the potential application of these TAAs in the diagnosis and prognosis for cancer treatment.

Using the New CellCollector to Capture Circulating Tumor Cells from Blood in Different Groups of Pulmonary Disease: A Cohort Study

Circulating tumor cells (CTCs) are promising biomarkers for clinical application. Cancer screening with Low-Dose Computed Tomography (LDCT) and CTC detections in pulmonary nodule patients has never been reported. The aim of this study was to explore the effectiveness of the combined methods to screen lung cancer. Out of 8313 volunteers screened by LDCT, 32 ground-glass nodules (GGNs) patients and 19 healthy volunteers were randomly selected. Meanwhile, 15 lung cancer patients also enrolled. CellCollector, a new CTC capturing device, was applied for CTCs detection. In GGNs group, five CTC positive patients with six CTCs were identified, 15.6% were positive (range, 1–2). In lung cancer group, 73.3% of the analyzed CellCollector cells were positive (range, 1–7) and no “CTC-like” events were detected in healthy group. All CTCs detected from GGNs group were isolated from the CellCollector functional domain and determined by whole genomic amplification for next-generation sequencing(NGS) analysis. NGS data showed that three cancer-related genes contained mutations in five CTC positive patients, including KIT, SMARCB1 and TP53 genes. In four patients, 16 mutation genes existed. Therefore, LDCT combined with CTC analysis by an in vivo device in high-risk pulmonary nodule patients was a promising way to screen early stage lung cancer.

Prediction of NSCLC recurrence from microarray data with GEP

Lung cancer is one of the deadliest diseases in the world. Non-small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.

Combination of circulating tumor cells with serum carcinoembryonic antigen enhances clinical prediction of non-small cell lung cancer

Circulating tumor cells (CTCs) have emerged as a potential biomarker in the diagnosis, prognosis, treatment, and surveillance of lung cancer. However, CTC detection is not only costly, but its sensitivity is also low, thus limiting its usage and the collection of robust data regarding the significance of CTCs in lung cancer. We aimed to seek clinical variables that enhance the prediction of CTCs in patients with non-small cell lung cancer (NSCLC). Clinical samples and pathological data were collected from 169 NSCLC patients. CTCs were detected by CellSearch and tumor markers were detected using the Luminex xMAP assay. Univariate analyses revealed that histology, tumor stage, tumor size, invasiveness, tumor grade and carcinoembryonic antigen (CEA) were associated with the presence of CTCs. However, the level of CTCs was not associated with the degree of nodal involvement (N) or tumor prognostic markers Ki-67, CA125, CA199, Cyfra21-1, and SCCA. Using logistic regression analysis, we found that the combination of CTCs with tumor marker CEA has a better disease prediction. Advanced stage NSCLC patients with elevated CEA had higher numbers of CTCs. These data suggest a useful prediction model by combining CTCs with serum CEA in NSCLC patients.

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.

Recurrent Syncope Associated with Lung Cancer

Syncope is an important problem in clinical practice with many possible causes that might be misdiagnosed. We present an unusual case of syncope, which has a normal chest X-ray. Exercise EKG and coronary angioplasty results confirmed the existence of serious coronary heart disease. The patient was treated with coronary stent transplantation. However, scope occurred again and the elevated tumor makers cytokeratin-19-fragment and neuron-specific enolase revealed the bronchogenic carcinoma, which was confirmed by enhanced CT examination. The treatment of carcinoma by chemotherapy was indeed sufficient for prompt elimination of the syncope symptoms.

Pemetrexed induces G1 phase arrest and apoptosis through inhibiting Akt activation in human non small lung cancer cell line A549

Pemetrexed is an antifolate agent which has been used for treating malignant pleural mesothelioma and non small lung cancer in the clinic as a chemotherapeutic agent. In this study, pemetrexed inhibited cell growth and induced G1 phase arrest in the A549 cell line. To explore the molecular mechanisms of pemetrexed involved in cell growth, we used a two-dimensional polyacrylamide gel electrophoresis (2-DE) proteomics approach to analyze proteins changed in A549 cells treated with pemetrexed. As a result, twenty differentially expressed proteins were identified by ESI-Q-TOF MS/MS analysis in A549 cells incubated with pemetrexed compared with non-treated A549 cells. Three key proteins (GAPDH, HSPB1 and EIF4E) changed in pemetrexed treated A549 cells were validated by Western blotting. Accumulation of GAPDH and decrease of HSPB1 and EIF4E which induce apoptosis through inhibiting phosphorylation of Akt were noted. Expression of p-Akt in A549 cells treated with pemetrexed was reduced. Thus, pemetrexed induced apoptosis in A549 cells through inhibiting the Akt pathway.