Analysis of exosomal competing endogenous RNA network response to paclitaxel treatment reveals key genes in advanced gastric cancer

Liu, Jun; Zhang, Guifeng; Li, Deyu; Zhong, Jiangming; Qiao, Chunming; Li, Lin; Liu, Zhenhua

doi:10.3389/fonc.2022.1027748

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…As a new fluorinated anti-metabolite, gemcitabine contributes to enhancing the individual anti-tumor activity of either 5-Fluorouracil or oxaliplatin ( Nam et al, 2013 ). Paclitaxel is a chemotherapeutic agent that has been applied to treat various types of cancer, and its monotherapy can significantly ameliorate the tumor response and prognosis of GC ( Lei et al, 2022 ). Immunotherapy and chemotherapy combined may provide precise treatment with various risk scores.…”

Section: Discussionmentioning

confidence: 99%

Characterization of cuproptosis in gastric cancer and relationship with clinical and drug reactions

Chen¹,

Luo²,

Qi³

et al. 2023

Front. Cell Dev. Biol.

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Gastric cancer (GC) is the fifth most common cancer worldwide. Cuproptosis is associated with cell growth and death as well as tumorigenesis. Aiming to lucubrate the potential influence of CRGs in gastric cancer, we acquired datasets of gastric cancer patients from TCGA and GEO. The identification of molecular subtypes with CRGs expression was achieved through unsupervised learning-cluster analysis. To evaluate the application value of subtypes, the K-M survival analysis was conducted to evaluate the clinical prognostic characteristics. Subsequently, we performed Gene Set Variation Analysis (GSVA) and utilized ssGSEA to quantify the extent of immune infiltration. Further, the K-M survival analysis was used to identify the prognosis-related CRGs. Next, signature genes of diagnostic predictive value were screened using the least absolute shrinkage and selection operator (LASSO) algorithm from the expression matrix for TCGA, as well as the signature gene-related subtype was clustered by the “ConsensusClusterPlus” package. Finally, the immunological and drug sensitivity assessments of the signature gene-related subtypes were conducted. A total of 173 CRGs were identified, most of the CRGs undergo copy number variation in gastric cancer. Under different patient subtypes, immune cell levels differed significantly, and the subtype exhibiting high expression of the CRGs had a better prognosis. Furthermore, we selected 34 CRGs that were highly correlated with the prognosis of gastric cancer. By constructing a multivariate Cox proportional-hazards model and a hazard scoring system, we were able to categorize patients into high- and low-risk groups based on their hazard score. K-M analysis demonstrated a significant survival disadvantage in the high-risk group. Based on Lasso regression analysis, we screened 16 signature genes, a multivariate logistic regression model [cutoff: 0.149 (0.000, 0.974), AUC:0.987] and a prognosis network diagram was constructed and their prediction efficiency for gastric cancer prognostic diagnosis was well validated. According to the signature genes, the patients were separated to two signature subtypes. We found that patients with higher CRGs expression and better prognosis had lower levels of immune infiltration. Finally, according to the results of drug susceptibility analysis, docetaxel, 5-Fluorouracil, gemcitabin, and paclitaxel were found to be more sensitive to gastric cancer.

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Section: Discussionmentioning

confidence: 99%

Characterization of cuproptosis in gastric cancer and relationship with clinical and drug reactions

Chen¹,

Luo²,

Qi³

et al. 2023

Front. Cell Dev. Biol.

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“…Exosome transcriptome could represent a more detailed and specific molecular biomarker compared with a single RNA type. The use of exosome transcriptome to predict the treatment response of nCRT has been widely used in other cancers (such as breast cancer and gastric cancer) (10,11); however, it is still in its infancy and has not been reported in ESCC. In addition, the current efficacy prediction mainly uses exo-miRNA panels, which cannot reveal the deep relationship between the three important types of exo-RNAs (mRNA, miRNA, and lncRNA).…”

Section: Introductionmentioning

confidence: 99%

Use of exosome transcriptome-based analysis to identify novel biomarkers in patients with locally advanced esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy

Yang¹,

Zhang²,

Liu³

et al. 2023

Ann Transl Med

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Background: The prognosis of esophageal squamous cell carcinoma (ESCC) is improved by neoadjuvant chemoradiotherapy (nCRT), especially for patients with pathologic complete response (pCR). Despite the efforts to predict treatment response using multimodality, no molecule has proven to be a strong biomarker.This study aimed to profile the expression of exosome transcriptome that could predict pCR in ESCC before and after nCRT.Methods: We collected paired blood samples of 15 patients with ESCC who received nCRT and radical surgery. They were divided into 3 groups: (A) residual tumor in the first clinical response evaluation (CRE-1), (B) no residual tumor in CRE-1 but with residual tumor in CRE-2 which was performed after 5-6 weeks, and (C) no residual tumor in CRE-1 or CRE-2. For each patient, the blood sample was collected before nCRT (time point 0); and then 6 weeks after nCRT, the clinical response was evaluated, and another blood sample was collected (time point 1).Results: Using the intersection of different sets, we found 23 progression-associated messenger RNAs (mRNAs) and 67 remission-associated mRNAs. Between remission-associated mRNAs and the targets of progression-associated (carcinogenic) microRNAs (miRNAs), the intersection was acquired, and 2 miRNA-mRNA networks (IFIT2-miR-3615-IFIT2-miR-484 and BTN3A3-miR-6803-3p) were identified. Among the intersection of progression-associated (carcinogenic) mRNAs and the targets of remission-associated miRNAs, there is a network with miR-132-3p (remission-associated miRNA) located at the core, matched with DICER1, KLHL8, ANKRD12, ASH1L, and IMP4.Conclusions: Our findings identified altered plasma exosome RNAs among the different groups and between different time points of nCRT, as well as the corresponding enrichments and regulatory networks, which may serve as potentially predictors of treatment response for patients with ESCC after nCRT.

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Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer

Wang,

Li,

Zhang

2024

Environmental Toxicology

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Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non‐cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co‐expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co‐expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR‐1290, miR‐1246, and miR‐451a among the top up‐regulated, and miR‐6875‐5p, miR‐6784‐5p, miR‐1228‐5p, and miR‐6765‐5p among the top down‐regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA‐target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR‐187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR‐187 expression. In summary, the integration of differential gene expression, co‐expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

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Analysis of exosomal competing endogenous RNA network response to paclitaxel treatment reveals key genes in advanced gastric cancer

Cited by 4 publications

References 36 publications

Characterization of cuproptosis in gastric cancer and relationship with clinical and drug reactions

Characterization of cuproptosis in gastric cancer and relationship with clinical and drug reactions

Use of exosome transcriptome-based analysis to identify novel biomarkers in patients with locally advanced esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer

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