RNA-Seq profiling of circular RNA in human lung adenocarcinoma and squamous cell carcinoma

Abstract Background Overexpression of vesicular nucleotide transporter (SLC17A9) has been found in different types of cancers. Nonetheless, little is known about its influence on lung cancers including human lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Methods Integrative analysis of SLC17A9 and other solute carrier family 17 genes (SLC17A1-8) was performed in patients with LUAD and LUSC based on The Cancer Genome Atlas database. Real-time PCR, western blots, MTS assay, EdU assay, ATP production assays and cell cycle analysis were applied to determine the effect and mechanism of SLC17A9 knockdown in LUAD cells. Results Compared with normal tissue, two SLC17 genes (SLC17A5 and SLC17A9) exhibited a distinctly different expression pattern in LUAD and LUSC. The expression of SLC17A3/7/8/9 expression was significantly correlated with worse overall survival (p < 0.05) in LUAD. Conversely, SLC17A1/2/4/6/9 expression was correlated with poorer OS (p < 0.05) in LUSC. ROC analysis suggested that the area under the curve of most SLC17 family genes was higher than 0.5. Meanwhile, multiple types of genetic alterations in SLC17 family genes were observed in tumor samples. Gene set enrichment analysis GSEA and protein-protein interaction results revealed that oncogenic signaling pathways and biological regulation, metabolic process, hallmark of myc targets, DNA repair, coagulation and complement were linked to SLC17A9 upregulation. Moreover, SLC17A9 knockdown significantly inhibited cell proliferation and ATP levels by affecting Myc, MFN2, STAT3, Cytochrome C and P2X1 expression in A549 cells. Specifically, SLC17A9 expression correlated negatively with overall survival and positively with most LUSC immune cells. SLC17A9 expression has correlations with infiltrating levels of B cells, CD4 + T cells, M1 macrophages, natural killer cells, Th1, Th2, Tfh, Th17 and Treg cells, as well as PD-1, CTLA4, and LAG3 of T cell exhaustion in LUAD. Conclusions Together, SLC17A9 could potentially serve as a prognostic biomarker correlated with immune infiltrates in LUAD and LUSC.

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Identification of reference genes for qRT-PCR in human lung squamous-cell carcinoma by RNA-Seq

Acta Biochimica et Biophysica Sinica ◽

10.1093/abbs/gmt153 ◽

2014 ◽

Vol 46 (4) ◽

pp. 330-337 ◽

Cited By ~ 29

Author(s):

Cheng Zhan ◽

Yongxing Zhang ◽

Jun Ma ◽

Lin Wang ◽

Wei Jiang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Reference Genes ◽

Human Lung ◽

Lung Squamous Cell Carcinoma ◽

Rna Seq ◽

Qrt Pcr

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Classifying Lung Adenocarcinoma and Squamous Cell Carcinoma using RNA-Seq Data

Cancer Studies and Molecular Medicine - Open Journal ◽

10.17140/csmmoj-3-120 ◽

2017 ◽

Vol 3 (2) ◽

pp. 27-31 ◽

Cited By ~ 1

Author(s):

Zhengyan Huang ◽

◽

Li Chen ◽

Chi Wang ◽

◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Lung Adenocarcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Rna Seq

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In silico assessment of EpCAM transcriptional expression and determination of the prognostic biomarker for human lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC)

Biochemistry and Biophysics Reports ◽

10.1016/j.bbrep.2021.101074 ◽

2021 ◽

Vol 27 ◽

pp. 101074

Author(s):

Abu Tayab Moin ◽

Bishajit Sarkar ◽

Md Asad Ullah ◽

Yusha Araf ◽

Nafisa Ahmed ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Lung Adenocarcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

In Silico ◽

Human Lung ◽

Prognostic Biomarker ◽

Lung Squamous Cell Carcinoma ◽

Transcriptional Expression

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Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data

Scientific Reports ◽

10.1038/s41598-021-88209-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

François Fauteux ◽

Anuradha Surendra ◽

Scott McComb ◽

Youlian Pan ◽

Jennifer J. Hill

Keyword(s):

Gene Expression ◽

Squamous Cell Carcinoma ◽

Protein Expression ◽

Lung Adenocarcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Expression Profiling ◽

Rna Seq ◽

Gene And Protein Expression ◽

Cross Platform

AbstractClassification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

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