Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD. Methods: RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465. Results: A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues. Conclusion: Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.

Epidermal Growth Factor Receptor Mutation Mechanisms in Nonsmall Cell Lung Cancer by Transcriptome Sequencing.

Background: This study intended to investigate the mechanisms underlying the epidermal growth factor receptor (EGFR) mutations in nonsmall cell lung cancer (NSCLC). Materials and Methods: Lung cancer tissue samples were collected from 20 patients with NSCLC (6 EGFR mutation types assigned into 2 categories and 14 EGFR wild types assigned to 4 categories). The samples were subjected to transcriptome sequencing, followed by identification of the differentially expressed mRNAs (DEMs), differentially expressed lncRNAs (DELs), and differentially expressed circRNAs (DECs) between the mutation and nonmutation groups. Function analysis and microRNA (miRNA) prediction for DEMs were performed. The correlations between long noncoding RNA (lncRNA)/circular RNA (circRNA) and messenger RNA (mRNA) were analyzed. In addition, the targeting lncRNA and circRNA of miRNA were predicted. Finally, competing endogenous RNA (ceRNA) network was constructed, and survival analysis for the mRNAs involved in the network was performed. Results: In total, 323 DEMs, 284 DELs, and 224 DECs were identified between EGFR mutation and nonmutation groups. The DEMs were significantly involved in gene ontology functions related to cilium morphogenesis and assembly. ceRNA networks were constructed based on the DEMs, DELs, DECs, and predicted miRNAs. Survival analysis showed that four genes in the ceRNA network, including ABCA3, ATL2, VAMP1, and APLN, were significantly associated with prognosis. The four genes were involved in several ceRNA pathways, including RP1-191J18/circ_000373/miR-520a-5p/ABCA3, RP5-1014D13/let-7i-5p/ATL2, circ_000373/miR-1293/VAMP1, and RP1-191J18/circ_000373/miR-378a-5p/APLN. Conclusion: EGFR mutations in NSCLC may be associated with cilium dysfunction and complex ceRNA regulatory mechanisms. The key RNAs in the ceRNA network may be used as promising biomarkers for predicting EGFR mutations in NSCLC.

A comparison of mRNA and circRNA expression between squamous cell carcinoma and adenocarcinoma of the lungs.

Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the two major subtypes of non-small-cell lung cancer (NSCLC). This study aimed to compare mRNA and circRNA expression patterns between LUSC and LUAD. Cancer tissues from 8 LUSC patients and 12 LUAD patients were collected to obtain mRNA and circRNA expression profiles. The differentially expressed mRNAs (DEmRNAs) and circRNAs (DE-circRNAs) between LUSC and LUAD were screened. Afterwards, miRNA-DEcircRNA pairs and miRNA-DEmRNA pairs were predicted to construct a competing endogenous RNAs (ceRNAs) network, followed by functional enrichment analysis and survival analysis. In total, 635 DEmRNAs and 245 DEcircRNAs were obtained. The ceRNA analysis revealed that genes, such as EPHA2, EPHA7, NTRK2, CDK6, hsa_circ_027570, hsa_circ_006089, and hsa-circ_035997, had distinct expression patterns between LUSC and LUAD. Also, functional enrichment analysis indicated that DEmRNAs were mainly enriched in ERK1 and ERK2 cascade. Survival analyses suggested that STXBP1 and PMEPA1 were associated the prognosis of with both LUAD and LUSC, whereas EPHA2 and CDK6 might serve as prognostic factors for LUSC and LUAD, respectively. In conclusion, genes such as EPHA2, EPHA7, NTRK2, and CDK6 had different patterns in the two major histological subtypes of NSCLC. Notably, EPHA2 and CDK6 might be considered as potential therapeutic targets for LUSC and LUAD, respectively.

STEAP1 facilitates metastasis and epithelial-mesenchymal transition of lung adenocarcinoma via the JAK2/STAT3 signaling pathway.

Six-transmembrane epithelial antigen of prostate-1 (STEAP1) is a relatively newly identified gene target from prostate cancer, breast cancer, and gastric cancer. However, functions of STEAP1 in lung adenocarcinoma (LUAD) are still unknown. In the present study, we explored the molecular and cellular mechanisms of STEAP1 in LUAD. Western blot and Q-PCR were conducted to detect the protein and mRNA expressions respectively. The cell proliferation was tested by CCK8 assay. The effects of STEAP1 on the metastasis and epithelial-mesenchymal transition (EMT) of LUAD were evaluated by EdU assay, wound healing assay, and transwell migratory assay. H1650, H358, HCC827, H1299, H23, A549, H1693 were selected as human LUAD cell lines in the study. Results have shown that STEAP1 expression was up-regulated in LUAD cells compared with normal lung epithelial cells. Knockdowning of STEAP1 suppressed the proliferation, migration, and invasion of LUAD epithelial cells. Importantly, after comparing the proliferation, migration, and invasion of LUAD to the corresponding control groups treated in STAT3 inhibitor ADZ1480, we found that STEAP1 regulates EMT via Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) signaling pathway. In conclusion, STEAP1 can serve as a therapeutic target, and it may have important clinical implications for LUAD treatment.

Long non­coding RNA00887 reduces the invasion and metastasis of non­small cell lung cancer by causing the degradation of miRNAs.

Long non­coding RNAs (lncRNAs) can act as carcinogenic or cancer suppressive factors during the pathogenesis, invasion and metastasis of non­small cell lung cancer (NSCLC). The current study explored the role of long intergenic non­protein coding RNA 00887 (LINC00887) and competing endogenous RNAs (ceRNAs). It was revealed that LINC00887 interacts with several microRNAs (miRs), which regulates downstream genes such as fibronectin 1, MET proto­oncogene, receptor tyrosine kinase and mothers against decapentaplegic homolog 4, which are associated with the spread of lung cancer. The experimental results also suggested that LINC00887 can stimulate miR­613, miR­206 and miR­1­2 to become competing endogenous RNAs, which may regulate the epithelial­mesenchymal transition of NSCLC cells through the transforming growth factor­â signal transduction pathway, and therefore promote the migration of cells and the acquisition of stem cell characteristics. Therefore, it can be concluded that high levels of LINC00887 can accelerate the malignant transformation ability of NSCLC cells.

Biomarker discovery and identification from non-small cell lung cancer sera.

Currently, serum biomarkers might usually be thought not to be used for early detection of lung cancer by some researchers. In this study, we used a highly optimized ClinProt-matrix-assisted laser desorption/ionization time-of flight mass spectrometer (MALDI-TOF-MS) to screen non-small cell lung carcinoma (NSCLC) markers in serum. A training set of spectra derived from 45 NSCLC patients, 24 patients with benign lung diseases (BLDs) and 21 healthy individuals, was used to develop a proteomic pattern that discriminated cancer from non-cancer effectively. A test set, including 74 cases (29 NSCLC patients and 45 controls), was used to validate this pattern. After cross-validation, the classifier showed sensitivity and specificity, 86.20% and 80.00%, respectively. Remarkably, 100% of early stage serum samples could be correctly classified as lung cancer. Furthermore, the differential peptides of 1865Da and 4209Da were identified as element of component 3 and eukaryotic peptide chain release factor GTP-binding subunit ERF, respectively. The patterns we described and peptides we identified may have clinical utility as surrogate markers for detection and classification of NSCLC.

Study of differential proteins in lung adenocarcinoma using laser capture microdissection combined with liquid chip-mass spectrometry technology.

BACKGROUND: In recent years the proportion of lung adenocarcinoma (adCA) which occurs in lung cancer patients has increased. Using laser capture microdissection (LCM) combined with liquid chip-mass spectrometry technology, we aimed to screen lung cancer biomarkers by studying the proteins in the tissues of adCA. METHODS: We used LCM and magnetic bead based weak cation exchange (MB-WCX) to separate and purify the homogeneous adCA cells and normal cells from six cases of fresh adCA and matched normal lung tissues. The proteins were analyzed and identified by matrix assisted laser desorption/ionization time-of-fight mass spectrometry (MALDI-OF-MS). We screened for the best pattern using a radial basic function neural network algorithm. RESULTS: About 2.895 × 10(6) and 1.584 × 10(6) cells were satisfactorily obtained by LCM from six cases of fresh lung adCA and matched normal lung tissues, respectively. The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. Comparing the differentially expressed proteins between the lung adCA and the matched normal lung group, 221 and 239 protein peaks, respectively, were found in the mass-to-charge ration (M/Z) between 800 Da and 10 000 Da. According to t test, the expression of two protein peaks at 7521.5 M/Z and 5079.3 M/Z had the largest difference between tissues. They were more weakly expressed in the lung adCA compared to the matched normal group. The two protein peaks could accurately separate the lung adCA from the matched normal lung group by the sample distribution chart. A discriminatory pattern which can separate the lung adCA from the matched normal lung tissue consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by a radial basic function neural network algorithm with a sensitivity of 100% and a specificity of 100%. CONCLUSIONS: Differential proteins in lung adCA were screened using LCM combined with liquid chip-mass spectrometry technology, and a biomarker model was established. It is possible that this technology is going to become a powerful tool in screening and early diagnosis of lung adCA.

Serum peptidome profiling in patients with lung cancer.

Serum peptide profiling is a promising approach for classification of cancer versus noncancer samples. In this study, we aimed to search for discriminating peptide patterns in serum samples between lung cancer patients and healthy controls. The magnetic beads-based weak cation-exchange chromatography followed by matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used in this study to identify patients with lung cancer. In total, serum samples from 64 lung cancer patients (32 for training set and 32 for testing set), 64 healthy controls (32 for training set and 32 for testing set), and 10 COPD patients (for disease control) were analyzed in this study. The mass spectra data analyzed with ClinProTools software was used to distinguish between cancer patients and healthy individuals based on three different algorithm models (GA, SNN, and QC). In the training set, patients with lung cancer could be identified with the mean sensitivity of 98.9% and specificity of100%. Similar results could be obtained from testing set, showing 87% sensitivity and 84.8% specificity. Screening for serum peptide patterns using MALDI-TOF MS showed high sensitivity and specificity in identifying patients with lung cancer.

Use of anchorchip-time-of-flight spectrometry technology to screen tumor biomarker proteins in serum for small cell lung cancer.

BACKGROUND: The purpose of this study is to discover potential biomarkers in serum for the detection of small cell lung cancer (SCLC). METHODS: 74 serum samples including 30 from SCLC patients and 44 from healthy controls were analyzed using ClinProt system combined with matrix-assisted laser desorption/ionization time-of-flight masss spectrometry (MALDI-TOF-MS). ClinProt software and genetic algorithm analysis selected a panel of serum markers that most efficiently predicted which patients had SCLC. RESULTS: The diagnostic pattern combined with 5 potential biomarkers could differentiate SCLC patients from healthy persons, with a sensitivity of 90%, specificity of 97.73%. Remarkably, 88.89% of stage I/II patients were accurately assigned to SCLC. CONCLUSIONS: Anchorchip-time-of-flight spectrometry technology will provide a highly accurate approach for discovering new biomarkers for the detection of SCLC.

Detection of lung adenocarcinoma using magnetic beads based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry serum protein profiling.

BACKGROUND: Recently, due to the rapid development of proteomic techniques, great advance has been made in many scientific fields. We aimed to use magnetic beads (liquid chip) based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) technology to screen distinctive biomarkers for lung adenocarcinoma (adCA), and to establish the diagnostic protein profiles. METHODS: Using weak cation exchange magnetic beads (MB-WCX) to isolate and purify low molecular weight proteins from sera of 35 lung adCA, 46 benign lung diseases (BLDs) and 44 healthy individuals. The resulting spectra gained by anchor chip-MALDI-TOF-MS were analyzed by ClinProTools and a pattern recognition genetic algorithm (GA). RESULTS: In the working mass range of 800 - 10 000 Da, 99 distinctive peaks were resolved in lung adCA versus BLDs, while 101 peaks were resolved in lung adCA versus healthy persons. The profile gained by GA that could distinguish adCA from BLDs was comprised of 4053.88, 4209.57 and 3883.33 Da with sensitivity of 80%, specificity of 93%, while that could separate adCA from healthy control was comprised of 2951.83 Da and 4209.73 Da with sensitivity of 94%, specificity of 95%. The sensitivity provided by carcinoembryonic antigen (CEA) in this experiment was significantly lower than our discriminatory profiles (P < 0.005). We further identified a eukaryotic peptide chain release factor GTP-binding subunit (eRF3b) (4209 Da) and a complement C3f (1865 Da) that may serve as candidate biomarkers for lung adCA. CONCLUSION: Magnetic beads based MALDI-TOF-MS technology can rapidly and effectively screen distinctive proteins/polypeptides from sera of lung adCA patients and controls, which has potential value for establishing a new diagnostic method for lung adCA.

[Application of liquid chip-mass spectrometry technology for screening serum biomarker proteins in lung squamous cell carcinoma].

OBJECTIVE: To screen the serum biomarker proteins of lung squamous cell carcinoma (SCCs) by liquid chip-mass spectrometry technology. METHODS: All serum samples, including 34 SCCs, 46 benign lung diseases (BLDs) and 44 healthy individuals, were analyzed by CLINPROT system in order to study the serum protein expression profiles. Then the discriminatory proteins were detected by FlexAnalysis 3.0 software. Biomarkers were identified by liquid chromatography-tandem mass spectrometry (LCMS/MS). RESULTS: Comparing the differential serum expression proteins between SCCs and healthy individuals, and SCCs and BLDs respectively. Ninety-six differential protein peaks [mass-to-charge ration (M/Z) between 800 and 10 000] were found between SCCs and healthy individuals. In these protein peaks, the expression of protein peaks at 4054.13 M/Z and 4267.46 M/Z had the largest difference between them. The two protein peaks could accurately separate SCCs from healthy individuals by the frame of axes. Similarly, 99 differential protein peaks were automatically detected between SCCs and BLDs. In these protein peaks, the expression of protein peaks at 5065.27 M/Z and 4054.02 M/Z had the largest difference between them. The two protein peaks could accurately separate SCCs from BLDs by the frame of axes. Identified by LC-MS/MS, 1778 M/Z and 1865 M/Z might be assayed jointly and corresponded to complements C3 fragment or C3f precursor. CONCLUSIONS: Differential protein expressions existed between SCCs versus healthy individuals and SCCs versus BLD patients. It is feasible to screen the diagnostic serum biomarkers of SCC with a high sensitivity and specificity by using CLINPROT system.

Analysis of the expression protein profiles of lung squamous carcinoma cell using shot-gun proteomics strategy.

The aim of this study is to globally screen and identify the expression protein profiles of lung squamous carcinoma cell (SqCC) using shot-gun proteomics strategy and to further analyze function of individual proteins by bioinformatics, which may likely result in the identification of new biomarkers and provide helpful clues for pathogenesis, early diagnosis, and progression of lung SqCC. The specific tumor cells were isolated and collected from the tissues of six patients with lung SqCC by laser capture microdissection (LCM). Total proteins from the LCM cells were extracted, digested with trypsin. The sequence information of resulting peptides was acquired by high-performance liquid chromatography (HPLC) and tandem mass spectrometry (TMS). The global protein profiles of lung SqCC cell were identified with BioworksTM software in IPI human protein database. Cellular component, molecular function, and biological process of the all proteins were analyzed using gene ontology (GO). About 720,000 tumor cells were satisfactorily collected from tissues of six patients with lung SqCC by LCM and the homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. The high resolution profiles including HPLC, full mass spectrum, and tandem mass spectrum were successfully obtained. Database searching of the resulting bimolecular sequence information identified 1982 proteins in all samples. The bioinformatics of these proteins, including amino acids sequence, fraction of coverage, molecular weight, isoelectric point, etc., were analyzed in detail. Among them, the function of most proteins was recognized by using GO. Five candidate proteins, Prohibitin (PHB), Mitogen-activated protein kinase (MAPK), Heat shock protein27 (HSP27), Annexin A1(ANXA1), and High mobility group protein B1 (HMGB1), might play an important role in SqCC genesis, progression, recurrence, and metastasis according to relative literatures. We have successfully isolated the interesting cells and effectively solved the heterogeneous problem of lung SqCC using LCM. The globally expressional proteins of lung SqCC cell were identified by shot-gun proteomics strategy. The five proteins might be hopefully used as markers of lung SqCC.

[The value of (18)F-FDG PET/CT in differential diagnosis of benign and malignant pulmonary lesions: a Meta-analysis.].

OBJECTIVE: Using Meta analysis to evaluate the value of (18)F-FDG PET/CT ((18)fluorine-fluorodeoxyglucose Positron emission tomography/computed tomography) in differentiating between benign and malignant pulmonary lesions. METHODS: Relevant documentations from PubMed and other 5 databases from 1980 to 2008 were searched, and the eligible literatures according to the inclusive criteria were selected. The statistical information and quality of science were assessed and classified. The data were analyzed using Meta-Disc1.4 software. The diagnostic value of PET/CT in distinguishing benign from malignant pulmonary lesions was evaluated by the pooled sensitivity, specificity, the likelihood ratio (LR) and summary receiver operating characteristic curve (SROC curve) statistical indicators. RESULTS: Seven literatures were collected including 5 in English and 2 in Chinese, and 795 cases were included in the study. Heterogeneity test showed that the homogeneity of the study was good. By using deterministic models to analyze the data, the value of the weighted sensitivity was 95% (93% - 97%), the specificity was 77% (71% - 82%), the positive likelihood ratio was 4.12, negative likelihood ratio was 0.08, and the SROC area under the curve (area under curve, AUC) was 94%. CONCLUSION: PET/CT is of high diagnostic value in differentiation between benign and malignant lung lesions, but large sized, multicenter, prospective studies are needed to assess its clinical value more accurately.

[Use of laser capture microdissection and surface-enhanced laser desorption ionization time-of-flight mass spectrometry to screen differential proteins in lung adenocarcinoma and lung squamous carcinoma].

OBJECTIVE: To screen biomarkers for classification in lung adenocarcinoma and lung squamous carcinoma by using laser capture microdissection (LCM) and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and support vector machine (SVM). METHODS: Six specimens of lung adenocarcinoma tissues and seven specimens of lung squamous carcinoma tissues obtained during operation were made into frozen sections and stained by improved H-E solution. About 1.2 x 10(5) of homogeneous adenocarcinoma cells and 1.4 x 10(5) of homogeneous lung squamous carcinoma cells were collected using LCM. Then SELDI profiles based on PBS II(+)SELDI-TOF-MS (IMAC protein chip) and the data were analyzed by support vector machine (SVM). RESULTS: Eighty seven differential protein peaks were found and top ten of them were identified as candidate biomarkers. The expression levels of 6 proteins among them with the molecular weights of 3333, 3592, 3848, 5036, 5191, and 5211 respectively in the lung squamous cancer tissues were weaker than those in the adenocarcinoma tissues, and the expression levels of 4 proteins with the molecular weights of 2505, 4004, 4847, and 11 412 in the lung squamous carcinoma tissues were stronger than those in the adenocarcinoma tissues. The expression of the protein with the molecular weight of 4847 in the squamous cancer was significantly stronger than that in the adenocarcinoma (p + 0.032). A discriminatory pattern consisting of 3 proteins with the molecular weights of 4847, 11 412, and 3592 was established with a sensitivity of 100% and a specificity of 100% respectively in separating adenocarcinoma from squamous carcinoma. CONCLUSION: There is a difference in protein component between adenocarcinoma and squamous carcinoma. LCM combined with SELDI-TOF-MS help screen a biomarker pattern to distinguish lung adenocarcinoma from lung squamous carcinoma with high sensitivity and specificity.

Study of distinct protein profiles for early diagnosis of NSCLC using LCM and SELDI-TOF-MS.

No biomarker has been available to detect early lung cancer so far. The aim of this study is to screen biomarker patterns for early diagnosis of non-small cell lung cancer (NSCLC) using laser capture microdissection (LCM) and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The 3 groups of the interested cells from 13 NSCLC tissues, 11 normal lung tissues (out of the 13 NSCLC patients), and 6 benign lung diseased tissues (BLD) were successfully separated by LCM, respectively, and the homogeneities of each type of the cell populations in the three groups were estimated to be over 95%. One-hundred- and twenty-three M/Z peaks were found in the NSCLCs and normal lungs, and between the two groups the relative intensity of 98 M/Z peaks was significantly different (P < 0.05) using SELDI-TOF-MS. The diagnostic pattern constructed using support vector machine (SVM) including three proteins, M/Z 4282, 3201, and 4252 Da, respectively, showed maximum Youden Index (YI). The pattern was validated by leave-one-out cross validation (LOOCV) and the results showed that the sensitivity was 100.0%, specificity 90.9%, and positive predictive value (PPV) 92.9%. In the NSCLCs and BLDs 188 M/Z peaks were determined and 54 showed statistically difference (P < 0.05). The sensitivity, specificity, and PPV of the diagnostic pattern consisting of two proteins, M/Z 3204 and 3701 Da, were all 100.0%. So, by using LCM we have successfully purified the interested cells and solved the problem of heterogeneity of lung cancer tissue. SELDI protein chip coupled with SVM could effectively screen the differentially expressional protein profiles and eventually establish biomarker patterns with high sensitivity and specificity.