sVEGF R1 and Tie-2 levels during chemotherapy of lung cancer patients

Immunohistochemical expression of VEGFR1 in non small cell lung carcinomas: Lower VEGFR1 expression is asociated with squamous cell carcinoma subtype and high SUV max values in 18F-FDG PET

BACKGROUND

To study the possible relation between immunohistochemical expression of vascular endothelial growth factor receptor 1 (VEGFR1) and the maximum standardised uptake value (SUV max) of ¹⁸F-FDG PET in patients with non small cell lung cancer (NSCLC).

MATERIAL AND METHODS

The study included 39 patients with NSCLC (24 squamous cell carcinomas and 15 adenocarcinomas). According to the clinical stage, the patients were distributed as follows: 8 stage I, 7 stage II, 15 stage III and 9 stage IV. Immunohistochemical expression of VEGFR1 was studied through the technique of tissue-matrix using Tissue Arrayer Device (Beecher Instruments, Sun Prairie, WI), using the polyclonal antibody against VEGFR1 (Santa Cruz Biotechnology, California, USA).

RESULTS

Positive VEGFR1 immunohistochemical expression was noted in 23 cases (59%). The number of positive tumours was not related with clinical stage but there was a different statistically significant association (p:0,0009) between VEGFR1 positivity and histological type, corresponding the greater percentages of positive results to adenocarcinomas (93,3%) versus in squamous cell carcinomas (37,5%). Likewise, SUV max values were higher (p: 0,039) in negative VEGFR1 carcinomas than in positive VEGFR1 tumors (r: 4-32,1; 16,4+/-6,4 (median 16,1) vs r: 3-47; 14,5+/-8,6 (12,8)).

CONCLUSIONS

Our results led us to consider that in NSCLC, the negative VEGFR1 immunohistochemical expression is associated significantly with squamous cell carcinomas subtype and with higher SUV max values in ¹⁸F-FDG-PET.

Immunohistochemical expression of VEGFR1 in non small cell lung carcinomas: Lower VEGFR1 expression is asociated with squamous cell carcinoma subtype and high max SUV values in 18F-FDG PET

BACKGROUND

To study the possible relation between immunohistochemical expression of vascular endothelial growth factor receptor 1 (VEGFR1) and the maximum standardised uptake value (maxSUV) of 18F-FDG PET in patients with non small cell lung cancer.

MATERIAL AND METHODS

The study included 39 patients with NSCLC (24 squamous cell carcinomas and 15 adenocarcinomas). According to the clinical stage, the patients were distributed as follows: 8 stage I, 7 stage II, 15 stage III and 9 stage IV. Immunohistochemical expression of VEGFR1 was studied through the technique of tissue-matrix using tissue arrayer device (Beecher Instruments, Sun Prairie, WI), using the polyclonal antibody against VEGFR1 (Santa Cruz Biotechnology, California, USA).

RESULTS

Positive VEGFR1 immunohistochemical expression was noted in 23 cases (59%). The number of positive tumours was not related with clinical stage but there was a different statistically significant association (p:.0009) between VEGFR1 positivity and histological type, corresponding the greater percentages of positive results to adenocarcinomas (93.3%) versus in squamous cell carcinomas (37.5%). Likewise, maxSUV values were higher (p: .039) in negative VEGFR1 carcinomas than in positive VEGFR1 tumors (r: 4-32.1; 16.4+/-6.4 [median 16.1] vs. r: 3-47; 14.5+/-8.6 [12.8]).

CONCLUSIONS

Our results led us to consider that in NSCLC, the negative VEGFR1 immunohistochemical expression is associated significantly with squamous cell carcinomas subtype and with higher maxSUV values in 18F-FDG-PET.

miR-19a-3p Facilitates Lung Adenocarcinoma Cell Phenotypes by Inhibiting TEK

Background: Both TEK and miR-19a-3p have been reported to regulate lung adenocarcinoma (LUAD) progression. However, the association between TEK and miR-19a-3p in LUAD remained unknown. This research aimed to investigate a novel miR-19a-3p/TEK interactome in LUAD cells. Methods: The mRNA expression and protein expression in the cell lines were determined using qPCR and Western blot assay, respectively. CCK-8 assay, EDU assay, flow cytometry cell apoptosis assay, scratch assay, and cell-to-extracellular matrix adhesion assay were performed to detect the proliferation, apoptosis, migration, and adhesion ability of A549 and H1975 cell lines. Results: Findings revealed that both mRNA and protein levels of TEK were downregulated in the LUAD tumor tissues and cell lines. It was also found that compared with the control group, the transfection of TEK overexpression plasmids into H1975 and A549 cell lines significantly inhibited cancerous phenotypes. However, experimental results indicated that by downregulating TEK, miR-19a-3p promoted LUAD cell phenotypes. Conclusion: This research demonstrated that an interactome existed between miR-19a-3p and TEK and that miR-19a-3p could suppress LUAD tumors by inhibiting TEK. This novel interactome could be used as a novel therapy target for LUAD.

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

Background

The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field.

Results

In this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS which simultaneously performs feature selection and model learning for genomic data analysis. The experimental results demonstrated that MCBFS is robust and effective by comparing it with seven benchmark and six state-of-the-art supervised methods on eight data sets. The visualization results and the statistical test showed that MCBFS can capture the informative genes and improve the interpretability and visualization of tumor gene expression and single-cell sequencing data. Additionally, we developed a general framework named McbfsNW using gene expression data and protein interaction data to identify robust biomarkers and therapeutic targets for diagnosis and therapy of diseases. The framework incorporates the MCBFS algorithm, network recognition ensemble algorithm and feature selection wrapper. McbfsNW has been applied to the lung adenocarcinoma (LUAD) data sets. The preliminary results demonstrated that higher prediction results can be attained by identified biomarkers on the independent LUAD data set, and we also structured a drug-target network which may be good for LUAD therapy.

Conclusions

The proposed novel feature selection method is robust and effective for gene selection, classification, and visualization. The framework McbfsNW is practical and helpful for the identification of biomarkers and targets on genomic data. It is believed that the same methods and principles are extensible and applicable to other different kinds of data sets.

Integrated analysis of transcription factors and targets co-expression profiles reveals reduced correlation between transcription factors and target genes in cancer

Transcription factors are recognized as the key regulators of gene expression. However, the changes in the correlation of transcription factors and their target genes between normal and tumor tissues are usually ignored. In this research, we used mRNA expression profile data from The Cancer Genome Atlas which included 5726 samples across 11 major human cancers to perform co-expression analysis by the Pearson correlation coefficients. Then, integrating 81,357 pairs of transcription factors and target genes from transcription factors databases to find out the changes in the co-expression correlation of these gene pairs from normal to tumor tissues. Based on the changes in the number of co-expressed TF-TG pairs and changes in the level of co-expression, we found the generally reduced correlation between transcription factors and their target genes in cancer. Additionally, we screened out universal and specific transcription factors-target genes pairs which may significant influence particular cancer. Then, we obtained 423 cancer cell line expression profiles from Broad Institute Cancer Cell Line Encyclopedia to verify our results. Some of these pairs like XRCC5-XRCC6 have been reported to involve in multiple cancers, while pairs like IRF1-PSMB9 without any previous articles related to tumor but involve in the biological processes of cancer, which are of great potential to be therapeutic targets. Our research may provide insights to better understand the tumor development mechanisms and find potential therapeutic targets.