Analysis of cancer-promoting genes related to chemotherapy resistance in esophageal squamous cell carcinoma

Xie, Feng; Zhang, Donglei; Qian, Xiangping; Wei, Huabing; Zhou, Lihuang; Ding, Chunyong; Pan, Wenbiao; Ye, Qing

doi:10.21037/atm-21-7032

Cited by 5 publications

(6 citation statements)

References 39 publications

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“…We identified BMP1 and TPM2 as promising universal candidate biomarkers for identifying chemoresistant patients before treatment when using bulk transcriptome data. Both genes can facilitate chemoresistance (36,37), supporting the rationality of our analyses.…”

Section: Discussionsupporting

confidence: 80%

Pre-existing cancer cells and induced fibroblasts are key cells for early chemoresistance in ovarian cancer

Gu,

He,

et al. 2024

Preprint

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Chemoresistance has long been a significant but unresolved issue in the treatment of various cancers, including the most deadly gynecological cancer, the high-grade serous ovary cancer (HGSOC). In this study, single nuclei transcriptome analyses were utilized to identify key cells and core networks for chemoresistance in HGSOC patients with different early responses to platinum-based chemotherapy at the single-cell level. Biomarkers for chemoresistance were also screened using bulk transcriptome data from independent cohorts with larger sample sizes. A total of 62,482 single cells from six samples were analyzed, revealing that chemoresistant cancer cells (Epithelial cells_0) pre-existed within individual patient before treatment. Two network modules formed with hub genes such as hormone-related genes (ESR1 and AR), insulin-related genes (INSR and IGF1R), and CTNNB1, were significantly overexpressed in these cells in the chemoresistant patient. BMP1 and TPM2 could be promise biomarkers in identifying chemoresistant patients before chemotherapy using bulk transcriptome data. Additionally, chemotherapy-induced fibroblasts (Fibroblasts_01_after) emerged as key stromal cells for chemoresistance. One network module containing one subnetwork formed by cholesterol biosynthesis-related genes and one subnetwork formed by cancer-related genes such as STAT3 and MYC, was significantly overexpressed in these cells in the chemoresistant patient. Notably, the NAMPT-INSR was the most prioritized ligand-receptor pair for cells interacting with Fibroblasts_01_after cells and Epithelial cells_0 cells to drive the up-regulation of their core genes, including IL1R1, MYC and INSR itself. Our findings deepen the understandings about mechanisms of early chemoresistance in HGSOC patients.

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Section: Discussionsupporting

confidence: 80%

Pre-existing cancer cells and induced fibroblasts are key cells for early chemoresistance in ovarian cancer

Gu,

He,

et al. 2024

Preprint

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“…The drug resistance/response mechanism of ESCC is very complicated. A study found that differential genes are also key cancer-promoting genes related to chemotherapy resistance through PPI, such as BMP1 and DBF4 ( 56 ); prognosis-related genes, such as TTK and KIF18A . These studies can help improve the survival rate of patients ( 57 ).…”

Section: Discussionmentioning

confidence: 99%

Network and pathway-based analysis of genes associated with esophageal squamous cell carcinoma

He¹,

Yuan²,

He³

et al. 2023

Ann Transl Med

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Background Although diagnostic methods and treatments have improved over the last few years, the 5-year survival rate of esophageal squamous cell carcinoma (ESCC) patients remains generally poor. The development of high-throughput technology has facilitated great achievements in localization of ESCC-related genes. To take a further step toward a thorough understanding of ESCC at a molecular level, the potential pathogenesis of ESCC needs to be deciphered. Methods The interaction of ESCC-related genes was explored by collecting genes associated with ESCC and then performing gene enrichment assays, pathway enrichment assays, pathway crosstalk analysis, and extraction of ESCC-specific subnetwork to describe the relevant biochemical processes. Results Through Gene Ontology (GO) enrichment analysis, many molecular functions related to response to chemical, cellular response to stimulus, and cell proliferation were found to be significantly enriched in ESCC-related genes. The results of pathway and pathway crosstalk analysis showed that pathways associated with multiple malignant tumors, the immune system, and metabolic processes were significantly enriched in ESCC-related genes. Through the analysis of specific subnetworks, we obtained some novel ESCC-related potential genes, such as MUC13 , GSTO1 , FIN , GRB2 , CDC25C , and others. Conclusions The molecular mechanism of ESCC is extremely complex. Some inducing factors change the expression status of many genes. The abnormal expression of genes mediates the biological processes involved in immunity and metabolism, apoptosis, and cell proliferation, leading to the occurrence of tumors. The genes MUC13 , RYK , and FIN may be potential prognostic indicators of ESCC; GRB2 and CDC25C may be potential targets of ESCC in proliferation. Our work may provide valuable information for further understanding the molecular mechanisms for the development of ESCC.

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“…Computer-aided diagnosis ( 79 ) and computer-aided therapy ( 80 ) have become the main application directions, and deep learning (#5) has emerged as the name of specific methods of artificial intelligence with the highest word frequency13.89 ( Figure 8D ). Specifically, deep learning plays a role in early detection ( 81 ), accurate differentiation of precancerous lesions from tumor lesions ( 82 ), determination of invasive tumor margins during surgical treatment ( 83 ), monitoring of disease progression and acquired drug resistance ( 84 ), and prediction of tumor aggressiveness ( 85 ), metastasis pattern ( 86 ) and recurrence risk ( 87 ). The innovation of esophageal imaging recognition and cancer marker screening technology provides the possibility for esophageal cancer detection, treatment and monitoring.…”

Section: Discussionmentioning

confidence: 99%

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

Lin

et al. 2022

Front. Oncol.

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ObjectiveUsing visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated.MethodsOn April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used.ResultsA total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal “New England Journal of Medicine” of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future.ConclusionsAn increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.

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Analysis of cancer-promoting genes related to chemotherapy resistance in esophageal squamous cell carcinoma

Cited by 5 publications

References 39 publications

Pre-existing cancer cells and induced fibroblasts are key cells for early chemoresistance in ovarian cancer

Pre-existing cancer cells and induced fibroblasts are key cells for early chemoresistance in ovarian cancer

Network and pathway-based analysis of genes associated with esophageal squamous cell carcinoma

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

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