Integrative Analysis of Angiogenesis-Related Long Non-Coding RNA and Identification of a Six-DEARlncRNA Signature Associated with Prognosis and Therapeutic Response in Esophageal Squamous Cell Carcinoma

Cao, Shasha; Wang, Xiaomin; Liu, Xiaohui; Li, Junkuo; Duan, Lijuan; Gao, Zhaowei; Lun, Shu‐Min; Zhu, Yiming; Yang, Haijun; Zhang, Hao; Zhou, Fuyou

doi:10.3390/cancers14174195

Cited by 5 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A ROC curve analysis showed that the AUCs for 6-DElncRNAs for predicting 1-, 3-and 5-year survival rates of patients with ESCC are 0.768, 0.825 and 0.822, respectively. As the high-risk group has higher gefitinib and lapatinib IC 50 values, 6-DElncRNAs may also be able to predict drug susceptibility (148). Liu et al (149) demonstrated that a 6-lncRNA signature (AC005091.1, SNHG6, AC091544.4, DNAJB5-DT, HTT-AS and ANKRD10-IT1) has a high predictive value for ESCC.…”

Section: Ncrnas As Biomarkers In Esccmentioning

confidence: 99%

Non‑coding RNA: A promising diagnostic biomarker and therapeutic target for esophageal squamous cell carcinoma (Review)

Zhang,

Wang,

Gao

et al. 2024

Oncol Lett

View full text Add to dashboard Cite

Esophageal cancer (EC) is a common form of malignant tumor in the digestive system that is classified into two types: Esophageal squamous cell carcinomas (ESCC) and esophageal adenocarcinoma. ESCC is known for its early onset of symptoms, which can be difficult to identify, as well as its rapid progression and tendency to develop drug resistance to chemotherapy and radiotherapy. These factors contribute to the high incidence of disease and low cure rate. Therefore, a diagnostic biomarker and therapeutic target need to be identified for ESCC. Non-coding RNAs (ncRNAs) are a class of molecules that are transcribed from DNA but do not encode proteins. Initially, ncRNAs were considered to be non-functional segments generated during transcription. However, with advancements in high-throughput sequencing technologies in recent years, ncRNAs have been associated with poor prognosis, drug resistance and progression of ESCC. The present study provides a comprehensive overview of the biogenesis, characteristics and functions of ncRNAs, particularly focusing on microRNA, long ncRNAs and circular RNAs. Furthermore, the ncRNAs that could potentially be used as diagnostic biomarkers and therapeutic targets for ESCC are summarized to highlight their application value and prospects in ESCC.

show abstract

Section: Ncrnas As Biomarkers In Esccmentioning

confidence: 99%

Non‑coding RNA: A promising diagnostic biomarker and therapeutic target for esophageal squamous cell carcinoma (Review)

Zhang,

Wang,

Gao

et al. 2024

Oncol Lett

View full text Add to dashboard Cite

show abstract

“…Currently, transcriptomics and chip analysis have found widespread application in various diseases [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

Exploring Potential Biomarkers and Molecular Mechanisms of Cutaneous squamous cell carcinoma Based on Bioinformatics

Qi,

Guo,

Bai

et al. 2023

Preprint

View full text Add to dashboard Cite

Cutaneous squamous cell carcinoma (cSCC) ranks as the second most common malignancy in clinical practice and poses a significant threat to public health due to its high malignancy. In this study, we aimed to explore potential biomarkers and molecular mechanisms of cSCC. Differentially expressed genes (DEGs) from GSE66359 and GSE117247 datasets were identified using R software. We conducted enrichment analyses and screened hub genes through protein-protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA). To assess the diagnostic performance of these genes, we generated ROC curves using both internal and external datasets (GSE45164) and validated the expression levels of these genes in cSCC tissues through immunohistochemistry. Subsequently, we predicted the target miRNAs and lncRNAs for hub genes using online databases and constructed competing endogenous RNA (ceRNA) networks. In total, we identified 505 upregulated DEGs and 522 downregulated DEGs. Through PPI and WGCNA analyses, we identified four hub genes exhibiting robust diagnostic performance in internal and external datasets (AUC > 0.9) and selected three previously unreported genes for further analysis. Immunohistochemistry demonstrated significantly elevated CCNA2, CCNB2, and UBE2C expression in cSCC tissues compared to normal skin tissues. Finally, we constructed three ceRNA networks, namely NEAT1/H19-hsa-miR-148a-3p-CCNA2 and NEAT1-hsa-miR-140-3p-UBE2C. In conclusion, we have identified CCNA2, CCNB2, and UBE2C as novel biomarkers for cSCC, and the NEAT1/H19-hsa-miR-148a-3p-CCNA2 and NEAT1-hsa-miR-140-3p-UBE2C ceRNA networks may represent molecular mechanisms under-lying cSCC progression. The findings of this study offer new diagnostic and therapeutic options for cSCC patients.

show abstract

Deep learning detected histological differences between invasive and non‐invasive areas of early esophageal cancer

Urabe,

Adachi,

Sakamoto

et al. 2024

Cancer Science

View full text Add to dashboard Cite

The depth of invasion plays a critical role in predicting the prognosis of early esophageal cancer, but the reasons behind invasion and the changes occurring in invasive areas are still not well understood. This study aimed to explore the morphological differences between invasive and non‐invasive areas in early esophageal cancer specimens that have undergone endoscopic submucosal dissection (ESD), using artificial intelligence (AI) to shed light on the underlying mechanisms. In this study, data from 75 patients with esophageal squamous cell carcinoma (ESCC) were analyzed and endoscopic assessments were conducted to determine submucosal (SM) invasion. An AI model, specifically a Clustering‐constrained Attention Multiple Instance Learning model (CLAM), was developed to predict the depth of cancer by training on surface histological images taken from both invasive and non‐invasive regions. The AI model highlighted specific image portions, or patches, which were further examined to identify morphological differences between the two types of areas. The 256‐pixel AI model demonstrated an average area under the receiver operating characteristic curve (AUC) value of 0.869 and an accuracy (ACC) of 0.788. The analysis of the AI‐identified patches revealed that regions with invasion (SM) exhibited greater vascularity compared with non‐invasive regions (epithelial). The invasive patches were characterized by a significant increase in the number and size of blood vessels, as well as a higher count of red blood cells (all with p‐values <0.001). In conclusion, this study demonstrated that AI could identify critical differences in surface histopathology between non‐invasive and invasive regions, particularly highlighting a higher number and larger size of blood vessels in invasive areas.

show abstract

Integrative Analysis of Angiogenesis-Related Long Non-Coding RNA and Identification of a Six-DEARlncRNA Signature Associated with Prognosis and Therapeutic Response in Esophageal Squamous Cell Carcinoma

Cited by 5 publications

References 46 publications

Non‑coding RNA: A promising diagnostic biomarker and therapeutic target for esophageal squamous cell carcinoma (Review)

Non‑coding RNA: A promising diagnostic biomarker and therapeutic target for esophageal squamous cell carcinoma (Review)

Exploring Potential Biomarkers and Molecular Mechanisms of Cutaneous squamous cell carcinoma Based on Bioinformatics

Deep learning detected histological differences between invasive and non‐invasive areas of early esophageal cancer

Contact Info

Product

Resources

About