Comprehensive Analysis Reveals GPRIN1 is a Potential Biomarker for Non-sm all Cell Lung Cancer

Function of CD8+, conventional CD4+, and regulatory CD4+ T cell identification in lung cancer

Lung cancer is the malignant tumor with the highest mortality rate in the world. There is obvious heterogeneity within the tumor. Single cell sequencing technology enables scholars to obtain information about the cell type, status, subpopulation distribution and communication behavior between cells in the tumor microenvironment from the cellular level. However, due to the problem of sequencing depth, some genes with low expression cannot be detected, which results in that most of the specific genes of immune cells cannot be recognized, and lead to defects in the functional identification of immune cells. In this paper, we used single cell sequencing data of 12346 T cells in 14 treatment-naïve non-small-cell lung cancer patients to identify immune cell-specific genes and infer the function of three types of T cells. The method, named GRAPH-LC, implemented this function by gene interaction network and graph learning methods. Graph learning methods are used to extract genes feature and dense neural network is used to identify immune cell-specific genes. The experiments on 10-cross validation shows that the AUROC and AUPR reached at least 0.802, 0.815 on identifying cell-specific genes of three types of T cells. And we did functional enrichment analysis on the top 15 expressed genes. By functional enrichment analysis, we got 95 GO terms and 39 KEGG pathways that related to three types of T cells. The use of this technology will help to deeply understand the mechanism of the occurrence and development of lung cancer, find new diagnostic markers and therapeutic targets, and provide a theoretical reference for the precise treatment of lung cancer patients in the future.

Dynamic network biomarker to determine the critical point of breast cancer stage progression

BACKGROUND

The discovery of early warning signs and biomarkers in patients with early breast cancer is crucial for the prevention and treatment of breast cancer. Dynamic Network Biomarker (DNB) is an approach based on nonlinear dynamics theory, which we exploited to identify a set of DNB members and their key genes as early warning signals during breast cancer staging progression.

METHODS

First, based on the gene expression profile of breast cancer in the TCGA database, the DNB algorithm was used to calculate the composite index (CI) of each gene cluster in the process of breast cancer anatomical staging. Then we calculated gene modules associated with the clinical phenotype stage based on weighted gene co-expression network analysis (WGCNA), combined with DNB membership to identify key genes in the network.

RESULTS

We identified a set of gene clusters with the highest CI in Stage II as DNBs, whose roles in related pathways indicate the emergence of a tipping point and impact on breast cancer development. In addition, analysis of the key gene GPRIN1 showed that high expression of GPRIN1 predicts poor prognosis, and related immune analysis showed that GPRIN1 is involved in the development of breast cancer through immune aspects.

CONCLUSION

The discovery of DNBs and the key gene GPRIN1 can provide potential biomarkers and therapeutic targets for breast cancer.

Prediction of lung cancer metastasis by gene expression

Tumor metastasis is the main cause of death in cancer patients. Early prediction of tumor metastasis can allow for timely intervention. At present, research on tumor metastasis mainly focuses on manual diagnosis by imaging or diagnosis by computational methods. With the deterioration of the tumor, gene expression levels in blood change greatly. It is feasible to measure the transcripts of key genes to predict whether cancer will metastasize. Therefore, in this paper, we obtained gene expression data from 226 patients from TCGA. These data included 239,322 transcripts. Background screening and LASSO analysis were used to select 31 transcripts as features. Finally, a deep neural network (DNN) was used to determine whether or not lung cancer would metastasize. We compared our methods with several other methods and found that our method achieved the best precision. In addition, in a previous study, we identified 7 genes that play a vital role in lung cancer. We added those gene transcripts into the DNN and found that the AUC and AUPR of the model were increased.

Comprehensive Analysis of the Transcriptome-wide m6A Methylome in Lung Adenocarcinoma by MeRIP Sequencing

N6-methyladenosine (m6A) is the most abundant internal modification on eukaryotic mRNAs. There is increasing evidence that m6A plays a key role in tumor progression, so it is important to analyze m6A modifications within the transcriptome-wide in lung adenocarcinoma (LUAD). Three pairs of LUAD samples and tumor-adjacent normal tissues were obtained from the South University of Science and Technology Hospital. And then methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) were used to identify differential m6A modifications between tumor and tumor-adjacent normal tissues. We identified 4041 aberrant m6A peaks, of which 1192 m6A peaks were upregulated and 2849 m6A peaks downregulated. It was found that genes with the dysregulated m6A peaks were enriched in the pathways in cancer, Rap1 signaling pathway, and insulin resistance. Additionally, 612 genes with abnormal regulation of m6A peaks and RNA expression were identified by combining MeRIP-seq and RNA-seq data. Through KEGG analysis, the 612 genes were enriched in cancer-related signaling pathways, such as the cGMP-PKG signaling pathway, and the Rap1 signaling pathway. What’s more, GSEA enrichment analysis showed these genes were enriched in cell cycle phase transition, cell division, cellular response to DNA damage stimulus, and chromosome organization. To further explore the relationship between differential m6A modified genes and clinical parameters of LUAD patients, we searched The Cancer Genome Atlas (TCGA) and identified 2 genes (FCRL5 and GPRIN1) that were associated with the prognosis and diagnosis of LUAD patients. Furthermore, we found a positive correlation between GPRIN1 and m6A reader YTHDF1 in the GEPIA2 database. It was verified that YTHDF1 binds to GPRIN1 mRNA and regulates its expression. Our study results suggest that m6A modification plays important role in the progression and prognosis of LUAD and maybe a potential new therapeutic target for LUAD patients in the future.