Diagnostic Biomarker For Lung Cancer Research Articles

The diagnostic and prognostic value of exosomal microRNAs in lung cancer: a systematic review.

Studies have shown that many exosomal microRNAs (miRNAs) can be used as non-invasive biomarkers of lung cancer, but their diagnostic and prognostic values need to be further clarified. We conducted a systematic literature search in Web of Science, PubMed, and ScienceDirect databases, obtained relevant articles and extracted data, and used statistical methods and statistical software to comprehensively evaluate the diagnostic and prognostic value of exosomal miRNAs in lung cancer. PROSPERO CRD42023447398. In terms of diagnosis, two exosomal miRNAs (miR-486-5p and miR-451a) were reported with the highest frequency in lung cancer patients, both of which had good diagnostic value. Compared with the control group, the pooled sensitivities of miR-486-5p and miR-451a were 0.80 (95% CI: 0.73-0.86) and 0.76 (95% CI: 0.60-0.87), specificities: 0.93 (95% CI: 0.63-0.99) and 0.85 (95% CI: 0.72-0.92), and AUCs: 0.85 (95% CI: 0.81-0.88) and 0.88 (95% CI: 0.84-0.90), for the respective miRNAs. For prognosis, in lung cancer patients with abnormally expressed exosomal miRNAs, miR-1290 was associated with PFS outcome; miR-382, miR-1246, miR-23b-3p, miR-21-5p, and miR-10b-5p were associated with OS outcome; miR-21 and miR-4257 were associated with DFS outcome; miR-125a-3p and miR-625-5p were associated with PFS and OS outcomes; miR-216b and miR-451a were associated with OS and DFS outcomes. Exosomal miRNAs are valuable biomarkers in lung cancer patients. Exosomal miR-486-5p and miR-451a can be used as new diagnostic biomarkers for lung cancer. Dysregulated exosomal miRNAs could serve as indicators of survival outcomes in lung cancer patients.

Circle-map profiling of extrachromosomal circular DNA as diagnostic biomarkers for lung cancer.

Circle-map profiling of extrachromosomal circular DNA as diagnostic biomarkers for lung cancer.

Plasma exosomal miR-1290 and miR-29c-3p as diagnostic biomarkers for lung cancer

BackgroundEnhancing the diagnostic efficacy of early-stage lung cancer is crucial for improving prognosis. The objective of this study was to ascertain dependable exosomal miRNAs as biomarkers for the diagnosis of lung cancer. MethodsExosomal miRNA candidates were identified through miRNA sequencing and subsequently validated in various case-control sets using real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR). The correlation between the expression of exosomal miRNAs and the clinicopathological features of lung cancer was investigated. To assess the diagnostic efficacy of exosomal miRNAs for lung cancer, the receiver operating characteristic (ROC) curve analysis was conducted. The optimal cutoff value of exosomal miRNAs was determined in the testing cohort and subsequently confirmed in the validation cohort. ResultsThe results showed that the expression of exosomal miR-1290 was significantly elevated, while that of miR-29c-3p was significantly decreased in the plasma of lung cancer patients, especially in those with early-stage lung cancer, compared to individuals with benign lung conditions (P < 0.01). Exosomal miR-1290 and miR-29c-3p demonstrated superior diagnostic efficacy compared to conventional tumor biomarkers in distinguishing between lung cancer and benign lung diseases, as evidenced by their respective area under the curve (AUC) values of 0.934 and 0.868. Furthermore, exosomal miR-1290 and miR-29c-3p exhibited higher diagnostic efficiency in early-stage lung cancer than traditional tumor markers, with AUC values of 0.947 and 0.895, respectively. Notably, both exosomal miR-1290 and miR-29c-3p displayed substantial discriminatory capacity in distinguishing between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), as indicated by their respective AUC values of 0.810 and 0.842. ConclusionsThe findings of this study provided evidence that exosomal miR-1290 and miR-29c-3p hold significant potential as biomarkers for the early detection of lung cancer, as well as for differentiating between NSCLC and SCLC.

Experimental and clinical validation of diagnostic and prognostic biomarkers identified by integrated study of microRNA and mRNA expression patterns in lung cancer

Lung cancer remains a leading cause of cancer-related mortality worldwide, with a complex molecular pathogenesis that is still not fully understood. Given the challenging task of distinguishing between subtypes of lung cancer (LUAD and LUSC), this study aimed to evaluate the potential of microRNAs (miRNAs) as biomarkers for differentiating these subtypes, by leveraging cancer genomics data. We used bioinformatics analysis for miRNA target prediction. In this regard, a miRNA-mRNA interaction network was defined and pathway analysis was subsequently established. To verify the results of the bioinformatic analysis, we conducted clinical and pre-clinical experiments. We carried out survival analysis, comparing gene expression and mortality rates between two major subtypes of lung cancer, LUSC and LUAD, from 45 lung cancer patients. Additionally, in vitro and in vivo studies were performed using human lung cancer cell lines to alter the expression of miR-21 and miR-326 using plasmid models. Finally, we conducted numerous assays to evaluate cell viability, cell migration and invasion, apoptosis, and cell cycle detection. We observed 102 miRNAs and 1,974 mRNAs as having differential expression in LUAD, whereas 143 miRNAs and 2,821 mRNAs were shown to have such an effect in LUSC. We found that hsa-mir-375 might significantly distinguish LUAD from LUSC, and our clinical validation demonstrated that the level of hsa-mir-375 was significantly higher in LUAD than in LUSC. In terms of finding a diagnostic marker, we discovered that a high level of hsa-mir-21 and/or hsa-mir-326 correlated with a worse prognosis. Our subsequent pre-clinical experiment showed that high expression of hsa-mir-21 and/or hsa-mir-326 significantly increased the aggressiveness of lung cancer and that inhibition of these miRNAs could potentially decrease the tumorigenic behavior of lung cancer cell lines. In conclusion, hsa-mir-21 and hsa-mir-326 might be potential diagnostic biomarkers for lung cancer and hsa-mir-375 might be a potential LUAD and LUSC distinguishing biomarker.

Extracellular vesicle-derived circHIPK3: Novel diagnostic biomarker for lung cancer

PurposeLung cancer (LC) is a common malignancy worldwide. A great number of circular RNAs (circRNAs) have been identified that serve crucial roles in cancer development. Extracellular vesicles (EVs) and their contents have been shown to be biomarkers for the diagnosis and prognosis of LC. Thus, we intended to clarify the functional role of EVs-derived circRNA homology domain interacting protein kinase 3 (EVs-circHIPK3) and its underlying mechanism of action. Material and methodsBioinformatics analysis was performed to validate the potential of partially circulating HIPK3 in LC diagnosis. EVs were isolated by polyethylene glycol (PEG) precipitation from plasma of 52 LC patients and 30 healthy controls. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was employed to evaluate the expressions of candidate circRNAs (circHIPK3) and microRNA-637 (miR-637, a target of circHIPK3). ResultsCircHIPK3 is significantly up-regulated in LC, while miR-637 expression is significantly reduced (p ​< ​0.05). Receiver operating characteristic (ROC) curve analysis, based on the expression of EVs-circHIPK3, allowed us to distinguish LC from healthy controls (area under the curve, AUC 0.897). ConclusionsTaken together, our study shows that EV-derived circHIPK3 can serve as a promising biomarker for LC patient diagnosis. However, the downstream mRNA of the circHIPK3/miR-637 axis requires further exploration to enrich our understanding of circHIPK3's mechanism in LC.

The value of folate receptor-positive circulating tumour cells as a diagnostic biomarker for lung cancer: a systematic review and meta-analysis.

To conduct a systematic review and meta-analysis evaluating the diagnostic value of folate receptor-positive (FR+) circulating tumour cells (CTCs) as a potential tumour marker for lung cancer diagnosis. The PubMed, Embase, and Web of Science databases were searched for relevant articles published between database inception and November 2022. Eligible studies were selected based on inclusion and exclusion criteria. Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve (AUC) were pooled with 95% confidence intervals (CI), using RevMan 5.4 and STATA 17.0 software to assess the diagnostic value of FR+CTC for lung cancer. After screening, 11 studies involving 3469 subjects were eligible for inclusion. The pooled sensitivity and specificity were 0.79 (95% CI 0.76, 0.82) and 0.84 (95% CI 0.81, 0.96), respectively, and the pooled positive and negative likelihood ratios were 4.90 (95% CI 4.25, 5.65) and 0.25 (95% CI 0.22, 0.29), respectively. The pooled diagnostic odds ratio was 19.70 (95% CI 16.06, 24.16). The AUC of the pooled summary receiver operating characteristic curve was 0.89 (95% CI 0.85, 0.91). Sensitivity analysis showed that this result was stable after one-by-one study elimination. Folate receptor-positive CTCs may have good diagnostic value in lung cancer.

Promoter methylation levels of RASSF1 and ATIC genes are associated with lung cancer in Iranian patients.

Epigenetic alterations like methylation of tumor suppressor genes or oncogenes, in respiratory epithelium have been associated with lung cancer. Hypermethylation of genes promoter is an epigenetic event, and is responsible to tumor suppressor genes inactivation as well as oncogenes activation. This study aimed to assess the role of methylation status in promoter of RASSF1 and ATIC genes their potential implication in the pathogenesis of lung tumor in Iranian patients. In this study, we collected 100 tissue samples (50lung cancer tissues and 50 adjacent non-cancerous lung tissues) from Iranian lung cancer patients. The genomic DNA was extracted, and methylation status of both RASSF1 andATIC genes was investigated by methylation-sensitive high-resolution melting (MS-HRM) assay technique and Real-Time PCR. Cancer Genome Atlas (TCGA) dataset wasalso analyzed for further validation of the gene's methylation. Methylation of RASSF1 gene promoter was significantly higher in lung tumor tissues. However, promoter methylation levels of ATIC gene was significantly lower in lung tumor tissues. These results were additionally confirmed by TCGA analysis. Promoter methylation of both RASSF1 and ATIC genes was significantly associated with lymph node metastasis, and clinical stage of lung cancer. Thereceiver operating characteristic (ROC) curve analysis indicated a high accuracy of promoter methylation in these genes as a diagnostic biomarker for lung cancer. Methylation levels of both RASSF1 and ATIC genes promoters were associated with lung cancer pathogenesis in Iranian population, and may be a suitable biomarker for diagnosis and prognosis of lung cancer in early stage of tumorigenesis.

Autoantibodies as Diagnostic Biomarkers for Lung Cancer

Lung cancer is one of the leading causes of death worldwide, with two main types of the disease - non-small cell lung malignancies and small cell lung cancer - accounting for approximately 85% and 15% of mortalities respectively. Like other diseases and infections, lung cancer can also be diagnosed by tumor markers that are produced by Lung cancer patients. Antibodies from five classes of antibodies are IgG, IgM, IgA, IgD, and IgE are produced against their own antigens known as autoantibodies, have their role in stopping or minimizing cancer, and play a vital role in diagnostic analysis of lung cancer. To diagnose lung cancer, a panel of six antibodies p53, CAGE, GBU4–5, NY-ESO-1, Annexin 1, and SOX2 has been studied to monitor lung cancer with a sensitivity of 38% and a specificity of 88 percent. Patients with lung cancer can produce p53 antibodies, an altered form of tumor suppressor known as Tp53. Other antibodies like NY-ESO 1, Annexin A1 and SOX2 can also be used as diagnostic biomarkers for the detection of lung cancer. In this review, we study all possible antitumor or Autoantibodies of lung cancer and their use in earlier detection of a lung cancer diagnosis.

Translation of a tissue epigenetic signature to circulating free DNA suggests BCAT1 as a potential noninvasive diagnostic biomarker for lung cancer

Lung cancer patients are diagnosed at late stages when curative treatments are no longer possible; thus, molecular biomarkers for noninvasive detection are urgently needed. In this sense, we previously identified and validated an epigenetic 4-gene signature that yielded a high diagnostic performance in tissue and invasive pulmonary fluids. We analyzed DNA methylation levels using the ultrasensitive digital droplet PCR in noninvasive samples in a cohort of 83 patients. We demonstrated that BCAT1 is the candidate that achieves high diagnostic efficacy in circulating DNA derived from plasma (area under the curve: 0.85). Impact of potentially confounding variables was also explored.

Mitochondria-derived small RNAs as diagnostic biomarkers in lung cancer patients through a novel ratio-based expression analysis methodology

Small non-coding RNAs are potential diagnostic biomarkers for lung cancer. Mitochondria-derived small RNA (mtRNA) is a novel regulatory small non-coding RNA that only recently has been identified and cataloged. Currently, there are no reports of studies of mtRNA in human lung cancer. Currently, normalization methods are unstable, and they often fail to identify differentially expressed small non-coding RNAs (sncRNAs). In order to identify reliable biomarkers for lung cancer screening, we used a ratio-based method using mtRNAs newly discovered in human peripheral blood mononuclear cells. In the discovery cohort (AUC = 0.981) and independent validation cohort (AUC = 0.916) the prediction model of eight mtRNA ratios distinguished lung cancer patients from controls. The prediction model will provide reliable biomarkers that will allow blood-based screening to become more feasible and will help make lung cancer diagnosis more accurate in clinical practice.

Screening of Serum miRNAs as Diagnostic Biomarkers for Lung Cancer Using the Minimal-Redundancy-Maximal-Relevance Algorithm and Random Forest Classifier Based on a Public Database

Background: Lung cancer is one of the deadliest cancers, early diagnosis of which can efficiently enhance patient’s survival. We aimed to screening out the serum miRNAs as diagnostic biomarkers for patients with lung cancer. Methods: A total of 416 remarkably differentially expressed miRNAs were acquired using the limma package, and next feature ranking was derived by the minimal-redundancy-maximal-relevance method. An incremental feature selection algorithm of a random forest (RF) classifier was utilized to choose the top 5 miRNA combination with the optimum predictive performance. The performance of the RF classifier of top 5 miRNAs was analyzed using the receiver operator characteristic (ROC) curve. Afterward, the classification effect of the 5-miRNA combination was validated through principal component analysis and hierarchical clustering analysis. Analysis of top 5 miRNA expressions between lung cancer patients and normal people was performed based on GSE137140 dataset, and their expression was validated by qPCR. The hierarchical clustering analysis was used to analyze the similarity of 5 miRNAs expression profiles. ROC analysis was undertaken on each miRNA. Results: We acquired top 5 miRNAs finally, with the Matthews correlation coefficient value as 0.988 and the area under the curve (AUC) value as 0.996. The 5 feature miRNAs were capable of distinguishing most cancer patients and normal people. Furthermore, except for the lowly expressed miR-6875-5p in lung cancer tissue, the other 4 miRNAs all expressed highly in cancer patients. Performance analysis revealed that their AUC values were 0.92, 0.96, 0.94, 0.95, and 0.93, respectively. Conclusion: By and large, the 5 feature miRNAs screened here were anticipated to be effective biomarkers for lung cancer.

Evaluation of EpCAM-specific exosomal lncRNAs as potential diagnostic biomarkers for lung cancer using droplet digital PCR.

Accumulating evidence demonstrated that long non-coding RNAs (lncRNAs) derived from exosomes had the potential to be diagnostic markers for lung cancer. However, the diagnostic value of lncRNAs from epithelial cell adhesion molecule (EpCAM)-positive exosomes remains unclear. In the study, serum EpCAM-positive exosomes were isolated with magnetic beads, and their role in lung cancer was investigated in vitro and in vivo. The copy numbers of lncRNAs RP11-77G23.5 and PHEX-AS1 in EpCAM-specific exosomes were quantified by droplet digital PCR (ddPCR). The diagnostic value of RP11-77G23.5 and PHEX-AS1 was tested in the training cohort and verified in the validation cohort. We found that EpCAM-specific exosomes could promote lung cancer development in vitro and in vivo. RP11-77G23.5 and PHEX-AS1 were significantly elevated in EpCAM-specific exosomes from lung cancer patients and could distinguish malignant from benign lung tumors. The amounts of RP11-77G23.5 were statistically higher in the subtype of lung adenocarcinoma (LUAC) than that of lung squamous cell carcinoma (LUSC), showing its capability to subtype LUAC and LUSC, while PHEX-AS1 exhibited distinct expression signatures between lower and higher tumor stages, and without and with distant metastasis, indicating its association with lung cancer progression. In conclusion, the EpCAM-specific exosomal lncRNAs RP11-77G23.5 and PHEX-AS1 may be promising diagnostic biomarkers for lung cancer. KEY MESSAGES: Serum EpCAM-positive exosomes promote lung cancer development in vitro and in vivo. Two EpCAM-specific exosomal lncRNAs can be simultaneously detected by RT-ddPCR. EpCAM-specific exosomal RP11-77G23.5 has the potential to subtype LUAC and LUSC. EpCAM-specific exosomal PHEX-AS1 is associated with lung cancer progression.

Standardization of DNA amount for bisulfite conversion for analyzing the methylation status of LINE-1 in lung cancer.

Highly methylated Long Interspersed Nucleotide Elements 1 (LINE-1) constitute approximately 20% of the human genome, thus serving as a surrogate marker of global genomic DNA methylation. To date, there is still lacking a consensus about the precise location in LINE-1 promoter and its methylation threshold value, making challenging the use of LINE-1 methylation as a diagnostic, prognostic markers in cancer. This study reports on a technical standardization of bisulfite-based DNA methylation analysis, which ensures the complete bisulfite conversion of repeated LINE-1 sequences, thus allowing accurate LINE-1 methylation value. In addition, the study also indicated the precise location in LINE-1 promoter of which significant variance in methylation level makes LINE-1 methylation as a potential diagnostic biomarker for lung cancer. A serial concentration of 5-50-500 ng of DNA from 275 formalin-fixed paraffin-embedded lung tissues were converted by bisulfite; methylation level of two local regions (at nucleotide position 300–368 as LINE-1.1 and 368–460 as LINE-1.2) in LINE-1 promoter was measured by real time PCR. The use of 5 ng of genomic DNA but no more allowed to detect LINE-1 hypomethylation in lung cancer tissue (14.34% versus 16.69% in non-cancerous lung diseases for LINE-1.1, p < 0.0001, and 30.28% versus 32.35% for LINE-1.2, p < 0.05). Our study thus highlighted the optimal and primordial concentration less than 5 ng of genomic DNA guarantees the complete LINE-1 bisulfite conversion, and significant variance in methylation level of the LINE-1 sequence position from 300 to 368 allowed to discriminate lung cancer from non-cancer samples.

Increased microRNA-30a levels in bronchoalveolar lavage fluid as a diagnostic biomarker for lung cancer.

BackgroundMicroRNA-30a (miRNA-30a) levels have been shown to increase in the plasma of lung cancer patients. Herein, we evaluated the miRNA-30a levels in the bronchoalveolar lavage fluid (BALF) of lung cancer patients as a potential biomarker for lung cancer diagnosis.MethodsBALF miRNA-30a expression of 174 subjects was quantified using quantitative real-time reverse transcription-polymerase chain reaction and compared between lung cancer patients and control patients with benign lung diseases. Moreover, its diagnostic value was evaluated by performing receiver operating characteristic (ROC) curve analysis.ResultsThe relative BALF miRNA-30a expression was significantly higher in the lung cancer patients than in the controls (0.74 ± 0.55 versus 0.07 ± 0.48, respectively, p < 0.001) as well as in lung cancer patients with stage I–IIA disease than in those with stage IIB–IV disease (0.98 ± 0.64 versus 0.66 ± 0.54, respectively, p < 0.05). Additionally, miRNA-30a distinguished benign lung diseases from lung cancers, with an area under the ROC curve (AUC) of 0.822. ROC analysis also revealed an AUC of 0.875 for the Youden index-based optimal cut-off points for stage I–IIA adenocarcinoma. Thus, increased miRNA-30a levels in BALF may be a useful biomarker for non-small-cell lung cancer diagnosis.

Evaluation of Cell Surface Vimentin Positive Circulating Tumor Cells as a Diagnostic Biomarker for Lung Cancer.

BackgroundCirculating tumor cells (CTCs) represent a collection of heterogeneous cells. Studies have shown epithelial CTCs and folate receptor (FR) positive CTCs could be used as diagnostic biomarkers for lung cancer (LC). This study aimed to determine whether cell surface vimentin (CSV) positive CTCs could be used as a biomarker for LC as well.Methods78 treatment-naïve non-small-cell lung cancer (NSCLC) patients, 21 patients with benign lung diseases (BLD) and 9 healthy donors (HD) were enrolled in this study. CTC detection was performed using CytoSorter® mesenchymal CTC kit (CSV). The correlation between CSV positive CTCs (CSV-CTCs) and LC patients’ clinicopathological characteristics would be evaluated, and diagnostic performances of CSV-CTCs and serum tumor markers for LC would be compared.ResultsCTC detection rates (average CTC count: range) in LC patients, patients with BLD and HD were 83.33% (2.47: 0-8), 47.62% (0.5: 0-3) and 0% (0: 0), respectively. CSV-CTCs could be used to differentiate LC patients from the patients with BLD and HD (P < 0.0001). CSV-CTCs were correlated with cancer stage, lymph node involvement and distant metastasis (P = 0.0062, 0.0014 and 0.0021, respectively). With a CTC cut-off value of 2, CSV-CTCs would have a sensitivity and specificity of 0.67 and 0.87, respectively, for diagnosing LC. CSV-CTC positive rates showed statistical differences among HD, BLD patients and LC patients at different cancer stages (P < 0.0001). Furthermore, CSV-CTC positive rates were positively correlated with tumor size, lymph node involvement and distant metastasis (P = 0.0163, 0.0196 and 0.03, respectively). CSV-CTCs had a better diagnostic performance than serum tumor makers, such as carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cancer antigen 125 (CA125) and CA153.ConclusionWhen CTC cut-off is set to 2 CTCs per 7.5 mL of blood, CSV-CTCs can be considered as an acceptable biomarker for diagnosing LC with a sensitivity and specificity of 0.67 and 0.87, respectively.

Homeobox-A13 acts as a functional prognostic and diagnostic biomarker via regulating P53 and Wnt signaling pathways in lung cancer.

The prognosis of lung cancer patients is poor without useful prognostic and diagnostic biomarker. To search for novel prognostic and diagnostic markers, we previously found homeobox-A13 (HOXA13) as a promising candidate in lung cancer. To determine the precisely clinical feature, prognostic and diagnostic value, possible role and mechanism of HOXA13. Gene-expression was explored by real-time quantitative-PCR, western-blot and tissue-microarray. The associations were analyzed by Chi-square test, Kaplan-Meier and Cox-regression. The roles and mechanisms were evaluated by MTS, EdU, transwell, xenograft tumor and luciferase-reporter assays. HOXA13 expression is increased in tumors, and correlated with age of patients. HOXA13 expression is associated with unfavorable overall survival and relapse-free survival of patients in four cohorts. Interestingly, HOXA13 has different prognostic significance in adenocarcinoma (ADC) and squamous-cell carcinoma (SCC), and is a sex- and smoke-related prognostic factor only in ADC. Importantly, HOXA13 can serve as a diagnostic biomarker for lung cancer, especially for SCC. HOXA13 can promote cancer-cell proliferation, migration and invasion in vitro, and facilitate tumorigenicity and tumor metastasis in vivo. HOXA13 acts the oncogenic roles on tumor growth and metastasis by regulating P53 and Wnt/β-catenin signaling activities in lung cancer. HOXA13 is a new prognostic and diagnostic biomarker associated with P53 and Wnt/β-catenin signaling pathways.

Early lung cancer diagnostic biomarker discovery by machine learning methods

Early diagnosis has been proved to improve survival rate of lung cancer patients. The availability of blood-based screening could increase early lung cancer patient uptake. Our present study attempted to discover Chinese patients’ plasma metabolites as diagnostic biomarkers for lung cancer. In this work, we use a pioneering interdisciplinary mechanism, which is firstly applied to lung cancer, to detect early lung cancer diagnostic biomarkers by combining metabolomics and machine learning methods. We collected total 110 lung cancer patients and 43 healthy individuals in our study. Levels of 61 plasma metabolites were from targeted metabolomic study using LC-MS/MS. A specific combination of six metabolic biomarkers note-worthily enabling the discrimination between stage I lung cancer patients and healthy individuals (AUC = 0.989, Sensitivity = 98.1%, Specificity = 100.0%). And the top 5 relative importance metabolic biomarkers developed by FCBF algorithm also could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction. This research will provide strong support for the feasibility of blood-based screening, and bring a more accurate, quick and integrated application tool for early lung cancer diagnostic. The proposed interdisciplinary method could be adapted to other cancer beyond lung cancer.

Emerging role of phytochemicals in targeting predictive, prognostic, and diagnostic biomarkers of lung cancer

Lung-cancer is the foremost cause of cancer in humans worldwide, of which 80–85% cases are composed of non-small cell lung carcinoma. All treatment decisions depend on the pattern of biomarkers selection to enhance the response to the targeted therapies. Although advanced treatments are available for lung-cancer, the disease treatment remains not adequate. There are several synthetic chemotherapeutic agents available for the treatment of lung cancer. However, due to their toxic effect, survival rate is still 15–18%. Besides, medicinal plants are a huge reservoir of natural products that provide protective effects against lung cancer. Likewise, successful studies of potential phytochemicals in targeting lung-cancer biomarkers have created a novel paradigm for the discovery of potent drugs against lung-cancer. Hence, to defeat severe toxicity and resistance towards the synthetic drugs, detailed studies are required regarding the available phytochemicals and targets responsible for the treatment of lung-cancer. The present review provides a comprehensive information about the lung-cancer biomarkers under the classification of predictive, prognostic, and diagnostic type. Moreover, it discusses and enlists the phytochemicals with mode of action against different biomarkers, effective doses in in vitro, in vivo, and clinical studies, the limitations associated with usage of phytochemicals as a drug to prevent/cure lung-cancer and the latest techniques employed to overcome such issues.

Identification and validation of HELLS (Helicase, Lymphoid-Specific) and ICAM1 (Intercellular adhesion molecule 1) as potential diagnostic biomarkers of lung cancer.

Although lung cancer is one of the greatest threats to human health, its signaling pathway and related genes are still unknown. This study integrates data from three groups of people to study potential key candidate genes and pathways related to lung cancer. Expression profiles (GSE18842, GSE19188 and GSE27262), including 162 tumor tissue and 135 adjacent normal lung tissue samples, were integrated and analyzed. Differentially expressed genes (DEGs) and candidate genes were identified, their expression pathways were analyzed, and the diethylene glycol-related protein–protein interaction (PPI) network was analyzed. We identified 232 shared DEGs (40 upregulated and 192 down-regulated) from the three GSE datasets. The DEGs were clustered according to function and signaling pathway for significant enrichment analysis. In total, 129 nodes/DEGs were identified from the DEG PPI network complex. An improved prognosis was associated with increased Helicase, Lymphoid-Specific (HELLS) and decreased Intercellular adhesion molecule 1 (ICAM1) mRNA expression in lung cancer patients. In conclusion, we used integrated bioinformatics analysis to identify candidate genes and pathways in lung cancer to show that HELLS and ICAM1 might be the key genes related to tumorigenesis or tumor progression in lung cancer. Additional studies are needed to further explore the involved functional mechanisms.

In silico discovery of potential diagnostic biomarkers of lung cancer

In silico discovery of potential diagnostic biomarkers of lung cancer