Lung Squamous Carcinoma Tissues Research Articles

Graph Perceiver Network for Lung Tumor and Bronchial Premalignant Lesion Stratification from Histopathology

Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. In this context, we present a novel computational approach, the Graph Perceiver Network (GRAPE-Net), leveraging hematoxylin and eosin (H&E) stained whole slide images (WSIs) to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues. GRAPE-Net outperforms existing frameworks in classification accuracy predicting LUSC, lung adenocarcinoma (LUAD), and non-tumor (normal) lung tissue on The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets containing lung resection tissues while efficiently generating pathologist-aligned, class-specific heatmaps. The network was further tested using endobronchial biopsies from two data cohorts, containing normal to carcinoma in situ histology, and it demonstrated a unique capability to differentiate carcinoma in situ lung squamous PMLs based on their progression status to invasive carcinoma. The network may have utility in stratifying PMLs for chemoprevention trials or more aggressive follow-up.

A prognostic risk model based on DNA methylation levels of genes and lncRNAs in lung squamous cell carcinoma.

BackgroundRecurrence is a risk factor for the prognosis of lung squamous carcinoma (LUSC). DNA methylation levels of RNAs are also associated with LUSC prognosis. This study aimed to construct a prognostic model with high performance in predicting LUSC prognosis using the methylation levels of lncRNAs and genes.MethodsThe differentially expressed RNAs (DERs) and differentially methylated RNAs (DMRs) between the recurrent and non-recurrent LUSC tissues in The Cancer Genome Atlas (TCGA; training dataset) were identified. Weighted correlation network analysis was performed to identify co-methylation networks. Differentially methylated genes and lncRNAs with opposite expression-methylation levels were used for the screening of prognosis-associated RNAs. The prognostic model was constructed and its performance was validated in the GSE39279 dataset.ResultsA total of 664 DERs and 981 DMRs (including 972 genes) in recurrent LUSC tissues were identified. Three co-methylation modules, including 226 differentially methylated genes, were significantly associated with LUSC. Among prognosis-associated RNAs, 18 DERs/DMRs with opposite methylation-expression levels were included in the methylation prognostic risk model. LUSC patients with high risk scores had a poor prognosis compared with patients who had low risk scores (TCGA: HR = 3.856, 95% CI [2.297–6.471]; GSE39279: HR = 3.040, 95% CI [1.435–6.437]). This model had a high accuracy in predicting the prognosis (AUC = 0.903 and 0.800, respectively), equivalent to the nomogram model inclusive of clinical variables.ConclusionsReferring to the methylation levels of the 16-RNAs might help to predict the survival outcomes in LUSC.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma.

Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586–0.671) in the TCGA cohort and 0.648 (95% CI: 0.535–0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC.

Application of immune cell infiltration in the diagnosis and prognosis of non-small cell lung cancer

Immune cell infiltration is of great significance for the diagnosis and prognosis of cancer. In this study, we collected gene expression data of non-small cell lung cancer (NSCLC) and normal tissues included in TCGA database, obtained the proportion of 22 immune cells by CIBERSORT tool, and then evaluated the infiltration of immune cells. Subsequently, based on the proportion of 22 immune cells, a classification model of NSCLC tissues and normal tissues was constructed using machine learning methods. The AUC, sensitivity and specificity of classification model built by random forest algorithm reached 0.987, 0.98 and 0.84, respectively. In addition, the AUC, sensitivity and specificity of classification model of lung adenocarcinoma and lung squamous carcinoma tissues constructed by random forest method 0.827, 0.75 and 0.77, respectively. Finally, we constructed a prognosis model of NSCLC by combining the immunocyte score composed of 8 strongly correlated features of 22 immunocyte features screened by LASSO regression with clinical features. After evaluation and verification, C-index reached 0.71 and the calibration curves of three years and five years were well fitted in the prognosis model, which could accurately predict the degree of prognostic risk. This study aims to provide a new strategy for the diagnosis and prognosis of NSCLC based on the classification model and prognosis model established by immune cell infiltration.

LncRNA-ATB Promotes Lung Squamous Carcinoma Cell Proliferation, Migration, and Invasion by Targeting microRNA-590-5p/NF90 Axis.

Lung cancer with highest morbidity and mortality seriously threatens human health worldwide. Long noncoding RNAs (lncRNAs) exert important biological functions by acting as microRNA, which is implicated in tumorigenesis and cancer development. Previous work has reported that lncRNA-ATB expression was significantly upregulated in lung adenocarcinoma tissues and promoted tumor progression; however, the mechanisms of lncRNA-ATB in lung squamous carcinoma (LSC) are still fairly elusive. In our study, lncRNA-ATB expression also markedly increases in LSC tissues and cell lines in comparison to the adjacent normal tissues and normal lung epithelial cells, respectively. Functional experiments indicate that lncRNA-ATB overexpression improves the proliferative, migratory, and invasive capabilities of normal lung epithelial cells compared with control group. Furthermore, the migratory and invasive abilities are strikingly inhibited in lncRNA-ATB silenced LSC cells. Mechanistically, lncRNA-ATB directly binds to microRNA-590-5p and downregulates microRNA-590-5p level, leading to the upregulation of NF-90 expression. In addition, lncRNA-ATB overexpression promotes the epithelial-mesenchymal transition process, where lncRNA-ATB overexpression facilitates the expression of mesenchymal phenotype related molecules N-cadherin and vimentin, while restrains the expression of epithelial phenotype related proteins E-cadherin and CK-19, compared to the control. Conversely, microRNA-590-5p mimics can reverse the results caused by lncRNA-ATB overexpression. Taken together, our initial data suggest that lncRNA-ATB overexpression may promote the progression of LSC by modulating the microRNA-590-5p/NF-90 axis.

Claudin-5, -7, and -18 suppress proliferation mediated by inhibition of phosphorylation of Akt in human lung squamous cell carcinoma

Abnormal expression of claudin (CLDN) subtypes has been reported in various solid cancers. However, it is unknown which subtype plays a key role in the regulation of proliferation in cancer cells. The expression of CLDN3-5, 7, and 18 in human lung squamous carcinoma tissues was lower than that in normal tissue. Here, we examined which combination of exogenous CLDNs expression inhibits proliferation and the molecular mechanism using human lung squamous RERF-LC-AI cells. Real-time polymerase chain reaction and western blotting showed that CLDN3-5, 7, and 18 are little expressed in RERF-LC-AI cells. In the exogenously transfected cells, CLDN5, 7, and 18 were distributed in the cell–cell contact areas concomitant with ZO-1, a tight junctional scaffolding protein, whereas CLDN3 and 4 were not. Cell proliferation was individually and additively suppressed by CLDN5, 7, and 18. The expression of these CLDNs showed no cytotoxicity compared with mock cells. CLDN5, 7, and 18 increased p21 and decreased cyclin D1, resulting in the suppression of cell cycle G1-S transition. The expression of these CLDNs inhibited phosphorylation of Akt without affecting phosphorylated ERK1/2. Furthermore, these CLDNs inhibited the nuclear localization of Akt and its association with 3-phosphoinositide-dependent protein kinase-1 (PDK1). The suppression of G1-S transition caused by CLDN5, 7, and 18 was rescued by the expression of constitutively active-Akt. We suggest that the reduction of CLDN5, 7, and 18 expression loses the suppressive ability of interaction between PDK1 and Akt and causes sustained phosphorylation of Akt, resulting in the disordered proliferation in lung squamous carcinoma cells.

Expression and Clinical Significance of Annexin-1 in Lung Squamous Carcinoma Tissues

Objective To investigate the expression and significance of Annexin-1(Anx-A1) mRNA and protein in lung squamous carcinoma tissues.Methods Quantitative real-time PCR(qRT-PCR) and Western blot were used to detect Anx-A1 mRNA and protein levels in 22 cases of lung squamous carcinoma and corresponding adjacent normal tissues.The relationship between expressional levels and pathological characters was analyzed.Results The expression levels of Anx-A1 mRNA and protein in lung squamous carcinoma were significantly higher than those in adjacent normal tissues.The expression level of Anx-A1 in poor differentiated tissues was higher than that in middle and high differentiation tissues of lung squamous cell carcinoma(P0.05).No expression difference between stage Ⅲ tumor tissues and stageⅠ～Ⅱ tissues was detected(P0.05).Conclusion Up-regulated expressions of Anx-A1 mRNA and protein in lung squamous cell carcinoma tissue might contribute to lung carcinogenesis.

Expression and Significance of Stem Cell Markers CK19, Notch3, CD133, P75NTR, STRO-1 and ABCG2 in Pulmonary Squamous Carcinomas.

Increasing reports showed that some tumor stem cells were selfrenewal and multi-lineage differentiated in tumors, similar to the normal stem cells in human body. The aim of this study is to observe the expression of stem cell markers in lung squamous carcinoma tissues. Fifty-four lung cancer specimens from surgery were analyzed for CK19, Notch3, CD133, P75NTR, STRO-1 and ABCG2 expression by using S-P immunohistochemistry. In addition, ten normal lung tissue samples were included as control. CK19, Notch3, CD133 and ABCG2 were expressed in 54 Lung cancer tissues, without expression of P75NTR and STRO-1. The expression rate of CK19, Notch3, CD133 and ABCG2 was 66.67% (36/54), 87.04% (47/54), 50% (27/54), and 61.11% (33/54) respectively. The levels of expression of Notch3, CD133 and ABCG2 were significantly lower in high differentiation group than those in moderate and low differentiation group (P <0.05). The levels of expression of CK19, CD133 and ABCG2 were significantly higher in lymph node metastasis group than those in non-metastasis group (P <0.05). The percentage of total positive cells of four stem cell markers in serial tissue sections was lower than 2%. There was expression of some stem cell markers in pulmonary squamous carcinomas, and there was relationship between expression degree with differentiation degree and lymph node metastasis.

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

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). 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). 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. 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.

Comparative proteome analysis of human lung squamous carcinoma tissue

Objective: To establish the two-dimensional electrophoresis profiles with high resolution and reproducibility from human lung squamous carcinoma tissue and paired normal tumor-adjacent bronchial epithelial tissue, and to identify differential expression tumor-associated proteins by using proteome analysis. Methods: Comparative proteome analysis with 20 human lung squamous carcinoma tissues and the paired normal bronchial epithelial tissues adjacent to tumors was carried out. The total proteins of human lung squamous carcinoma tissue and paired normal tumor-adjacent bronchial epithelial tissue were separated by means of immobilized pH gradient-based two-dimensional gel electrophoresis (2-DE) and silver staining. The differential expression proteins were analyzed and then identified by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS). Results: (1) Well-resolved, reproducible 2-DE patterns of human lung squamous carcinoma and adjacent normal bronchial epithelial tissues were obtained. For tumor tissue, average spots of 3 gels were 1567±46, and 1436±54 spots were matched with an average matching rate of 91.6%. For control, average spots of 3 gels were 1349±58, and 1228±35 spots were matched with an average matching rate of 91.03%. The average position deviation of matched spots was 0.924±0.128 mm in IEF direction, and 1.022±0.205 mm in SDS-PAGE direction; (2) A total of 1178±56 spots were matched between the electrophoretic maps of 20 human lung squamous carcinoma tissues and paired normal tumor-adjacent bronchial epithelial tissues. Seventy-six differentially expressed proteins were screened; (3) Sixty-eight differential proteins were identified by PMF, some proteins were the products of oncogenes, and others involved in the regulation of cell cycle and signal transduction; (4) In order to validate the reliability of the identified results, the expression of 3 proteins mdm2, c-jun and EGFR, which was correlated with lung squamous carcinoma, was detected by immunohistochemical staining and Western blot analysis. The results revealed that mdm2, c-jun and EGFR were up-regulated in lung squamous carcinomas, whereas they were down-regulated in adjacent normal bronchial epithelial tissues, normal lung tissues and inflammatory pseudotumor, which was consistent with our proteome analysis results. Conclusion: The well-resolved, reproducible 2-DE patterns of human lung squamous carcinoma and adjacent normal bronchial epithelial tissues were established and 68 differential proteins were characterized by applying comparative proteome analysis successfully. These results will provide scientific foundation for screening the molecular biomarker used to diagnose and treat lung squamous carcinoma, as well as to improve the patient’s prognosis and provide new clue for the research of lung squamous carcinogenic mechanism.

Comparative proteomics analysis of human lung squamous carcinoma

Two-dimensional polyacrylamide gel electrophoresis (2-DE) profiles of human lung squamous carcinoma tissue and paired surrounding normal bronchial epithelial tissue were compared. Selected differential protein-spots were identified with peptide mass fingerprinting based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and database searching. Well-resolved and reproducible 2-DE patterns of both the tumor and the normal tissues were acquired. The average deviations of spot position were 0.873 ± 0.125 mm in IEF direction and 1.025 ± 0.213 mm in SDS–PAGE direction, respectively. For the tumor tissues, a total of 1349 ± 67 spots were detected and 1235 ± 48 spots were matched with an average matching rate of 91.5%. For the corresponding normal tissues, a total of 1297 ± 73 spots were detected and 1183 ± 56 spots were matched with an average matching rate of 91.2%. A total of 1069 ± 45 spots were matched between the tumor and the normal tissues. Forty differential proteins between tumor and normal tissues were characterized. Some proteins were the products of oncogenes and others were involved in the regulation of cell cycle and signal transduction. These data are valuable for mass identification of differentially expressed proteins involved in lung carcinogenesis, establishing human lung cancer proteome database and screening molecular marker to further study human lung squamous carcinoma.

Proteomic Comparison of Two-Dimensional Gel Electrophoresis Profiles from Human Lung Squamous Carcinoma and Normal Bronchial Epithelial Tissues

Differential proteome profiles of human lung squamous carcinoma tissue compared to paired tumor-adjacent normal bronchial epithelial tissue were established and analyzed by means of immobilized pH gradient-based two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS). The results showed that well-resolved, reproducible 2-DE patterns of human lung squamous carcinoma and adjacent normal bronchial epithelial tissues were obtained under the condition of 0.75-mg protein-load. The average deviation of spot position was 0.733±0.101 mm in IEF direction, and 0.925±0.207 mm in SDS-PAGE direction. For tumor tissue, a total of 1241±88 spots were detected, 987±65 spots were matched with an average matching rate of 79.5%. For control, a total of 1190±72 spots were detected, and 875±48 spots were matched with an average matching rate of 73.5%. A total of 864±34 spots were matched between tumors and controls. Forty-three differential proteins were characterized: some proteins were related to oncogenes, and others involved in the regulation of cell cycle and signal transduction. It is suggested that the differential proteomic approach is valuable for mass identification of differentially expressed proteins involved in lung carcinogenesis. These data will be used to establish human lung cancer proteome database to further study human lung squamous carcinoma.

Involvement ofSonic hedgehogin the Cell Growth of LK-2 Cells, Human Lung Squamous Carcinoma Cells

Mutation of thePatchedgene has been detected in human inherited basal cell nevus syndrome (BCNS) and sporadic basal cell carcinomas (BCC), suggesting a strong relation between a Sonic hedgehog-Patched signal and cell proliferation. In the present study, we demonstrate thatSonic hedgehogis expressed in human lung squamous carcinoma (LK-2 and EBC-1) and some adenocarcinoma cell lines. The expression ofSonic hedgehogis also detected in the human lung squamous carcinoma tissues, but not in the normal lung tissue of the same patient. The N-terminal region of Sonic hedgehog stimulates the incorporation of BrdU into LK-2 cells and stimulates their cell growth, while anti-Shh-N inhibits their cell growth. These results suggest that a Sonic hedgehog signal is involved in the cell growth of LK-2 cells.