Bioinformatics Analysis Reveals Candidate Biomarkers Served As Dendritic Cell-based Lung Squamous CellCarcinoma (LUSC) Treatment

IEEE International Conference on Bioinformatics and Biomedicine（2024）

Changwai Bilingual School

Cited 0|Views1

Abstract

Lung squamous cell carcinoma (LUSC) causes high mortality rates worldwide. The goal of the study is to use bioinformatics approaches to identify candidate genes associated with lung cancer and the ones that could serve as targets for specific immune infiltration.The candidate genes in two significant clusters reported for LUSC based on TCGA database were chosen for analysis. The pattern of gene expression in both clusters shows opposite to their genomic landscape signature: one cluster of genes tends to have longer average gene length and fewer average number of exons than the genes in the other cluster. Functional annotation using The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was performed after genomic information analysis. Groups of genes for their enriched terms having the False Discovery Rate (FDR) value less than 0.1 were further studied. Cancer association for selected genes were confirmed using the Cancer Genetics Web tool. Three genes (RNASE6, CCR1, CMKLR1) from one cluster and 11 genes (DLL4, NID2, ROBO4, COL4A1, VASH1, VWF, BGN, KIT, PRND, OSBPL10, CD34) from the other cluster are prioritized to be top candidate genes.Correlation between gene expression and immune cell infiltration was examined using TIMER2.0. Positive correlations were observed between the expressions of these genes and six different types of immune infiltration, particularly with dendritic cells, according to TIMER2.0 Gene analysis. Mutation analysis revealed varied frequencies, with ROBO4 showing the highest mutation rate (6.6%) among our identified LUSC-associated genes. Five genes (DLL4, CMKLR1, CD34, ROBO4, and NID2) exhibit a strong positive correlation between their expressions with immune infiltration. This suggests that these genes may serve as potential targets for dendritic cell immune infiltration in the treatment of LUSC. The candidate genes prioritized in our study have medical significance.

Translated text

Key words

LUSC,Dendritic Cell,Immune Infiltration

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Data Disclaimer

The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn

Chat Paper

Summary is being generated by the instructions you defined