Qingzhou Guan

First Affiliated Hospital of Henan University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Zheng Guo

Second Affiliated Hospital of Nanjing Medical University

Haidan Yan

Fujian Medical University

You Guo

First Affiliated Hospital of Gannan Medical University

Jun He

University of South China

Xianlong Wang

Fujian Medical University

Xiangyu Li

North China Electric Power University

Lu Ao

National Institute of Metrology

Hao Cai

First Affiliated Hospital of Gannan Medical University

Jiahui Zhang

Peking Union Medical College Hospital

Yange Tian

First Affiliated Hospital of Henan University

Cooperative Institutions

Fujian Medical University

First Affiliated Hospital of Henan University

Harbin Medical University

Central South University

Chinese Academy of Sciences

Chinese Academy of Medical Sciences & Peking Union Medical College

Union Hospital

Zhejiang University

Southern Medical University

Shanghai Jiao Tong University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Mechanism underlying the therapeutic effects of effective component compatibility of Bufei Yishen formula III combined with exercise rehabilitation on chronic obstructive pulmonary disease

Annals of Medicine (2024)

Lidong Huang Qingzhou Guan Ruilong Lu Zhenzhen Zhang Chunlei Liu

To explore the mechanism underlying the therapeutic effect of Bufei Yishen Formula III combined with exercise rehabilitation (ECC-BYF III + ER) on chronic obstructive pulmonary disease (COPD) and further identify hub genes.

10.1080/07853890.2024.2403729

Cite

Citations (0)

MOESM4 of A robust qualitative transcriptional signature for the correct pathological diagnosis of gastric cancer

Figshare (2019)

Haidan Yan Meifeng Li Long‐Long Cao Haifeng Chen Hung-Ming Lai

Additional file 4. The REOs of the top gene pairs. The distributions of REOs of the top six gene pairs in each of the training datasets.

Signature (topology)

10.6084/m9.figshare.7790264

Cite

Citations (0)

Additional file 6: of Identification and characterization of genes with absolute mRNA abundances changes in tumor cells with varied transcriptome sizes

Figshare (2019)

Hao Cai Xiangyu Li Jun He Wenbin Zhou Kai Song

Table S3. Pathway enrichment analysis of the absolute DEGs and relative DEGs for ten cancers. (XLSX 58 kb)

Identification

RNA-Seq

10.6084/m9.figshare.7717334

Cite

Citations (0)

Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes

Figshare (2022)

Qingzhou Guan Peng Zhao Yange Tian Liping Yang Zhenzhen Zhang

It is essential to assess the cancer risk for patients with chronic obstructive pulmonary disease (COPD). Comparing gene expression data from patients with lung cancer (a total of 506 samples) and those with cancer-adjacent normal lung tissues (a total of 370 samples), we generated a qualitative transcriptional signature consisting of 2046 gene pairs. The signature was verified in an evaluation dataset comprising 18 subjects with severe disease and 52 subjects with moderate disease (Wilcoxon rank-sum test; p = 7.33 × 10⁻⁵). Similar results were obtained in other independent datasets. Among the gene pairs in the signature, 326 COPD stage-related gene pairs were identified based on Spearman’s rank correlation tests and those gene pairs comprised 368 unique genes. Of these 368 genes, 16 genes were significantly dysregulated in COPD rat model data compared with control data. Some of these genes (Dhx16, Upf2, Notch3, Sec61a1, Dyrk2, and Hmmr) were altered when the COPD rat model was treated with traditional Chinese medicines (TCM), including Bufei Yishen formula, Bufei Jianpi formula, and Yiqi Zishen formula. Overall, the signature could predict the cancer incidence-risk of COPD and the identified key genes might provide guidance regarding both the treatment of COPD using TCM and the prevention of cancer in patients with COPD. KEY MESSAGESA cancer risk assessment signature was identified in patients with COPD.The signature is insensitive to batch effects and is well verified.COPD key genes identified in this study might play a crucial role in TCM treatment and cancer prevention. A cancer risk assessment signature was identified in patients with COPD. The signature is insensitive to batch effects and is well verified. COPD key genes identified in this study might play a crucial role in TCM treatment and cancer prevention.

Identification

Signature (topology)

10.6084/m9.figshare.20521997

Cite

Citations (0)

A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy

Frontiers in Molecular Biosciences (2020)

Yanhua Chen Hao Cai Wannan Chen Qingzhou Guan Jun He

For estrogen receptor (ER)-negative breast cancer patients, paclitaxel (P), doxorubicin (A) and cyclophosphamide (C) neoadjuvant chemotherapy (NAC) is the standard therapeutic regimen. Pathologic complete response (pCR) and residual disease (RD) are common surrogate measures of chemosensitivity. After NAC, most patients still have RD; of these, some partially respond to NAC, whereas others show extreme resistance and cannot benefit from NAC but only suffer complications resulting from drug toxicity. Here we developed a qualitative transcriptional signature, based on the within-sample relative expression ordering (REO) of gene pairs, to identify extremely resistant samples to PAC NAC. Using gene expression data for ER-negative breast cancer patients including 113 pCR samples and 137 RD samples from four datasets, we selected 61 gene pairs with reversal REO patterns between the two groups as the resistance signature, denoted as NR61. Samples with more than 37 signature gene pairs that had the same REO patterns within the extremely resistant group were defined as having extreme resistance; otherwise, they were considered responders. In the GSE25055 and GSE25065 dataset, the NR61 signature could correctly identify 44 (97.8%) of the 45 pCR samples and 22 (95.7%) of the 23 pCR samples as responder samples, respectively; it also identified 13 (16.9%) of 77 RD samples and 8 (21.1%) of 38 RD samples as extremely resistant samples, respectively. Survival analysis showed that the distant relapse-free survival (DRFS) time of the 14 extremely resistant cases was significantly shorter than that of the 108 responders (P < 0.01; HR = 3.84; 95% CI = 1.91-7.70) in GSE25055. Similar results were obtained in GSE25065. Moreover, in the integrated data of the two datasets with 94 responders and 21 extremely resistant samples identified from RD patients, the former had significantly longer DRFS than the latter (P < 0.01; HR = 2.22; 95% CI = 1.26-3.90). In summary, our signature could effectively identify patients who completely respond to PAC NAC, as well as cases of extreme resistance, which can assist decision-making on the clinical therapy for these patients.

Regimen

Gene signature

10.3389/fmolb.2020.00034

Cite

Citations (5)

Identification of idiopathic pulmonary fibrosis hub genes and exploration of the mechanisms of action of Jinshui Huanxian formula

International Immunopharmacology (2024)

Qingzhou Guan Zhenzhen Zhang Peng Zhao Lidong Huang Ruilong Lu

Idiopathic pulmonary fibrosis (IPF) is a common and heterogeneous chronic disease, and the mechanism of Jinshui Huanxian formula (JHF) on IPF remains unclear. For a total of 385 lung normal tissue samples from the Gene Expression Omnibus database, 37,777,639 gene pairs were identified through microarray and RNA-seq platforms. Using the individualized differentially expressed gene (DEG) analysis algorithm RankComp (FDR < 0.01), we identified 344 genes as DEGs in at least 95 % (n = 81) of the IPF samples. Of these genes, IGF1, IFNGR1, GLI2, HMGCR, DNM1, KIF4A, and TNFRSF11A were identified as hub genes. These genes were verified using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) in mice with pulmonary fibrosis (PF) and MRC-5 cells, and they were highly effective at classifying IPF samples in the independent dataset GSE134692 (AUC = 0.587-0.788) and mice with PF (AUC = 0.806-1.000). Moreover, JHF ameliorated the pathological changes in mice with PF and significantly reversed the changes in hub gene expression (KIF4A, IFNGR1, and HMGCR). In conclusion, a series of IPF hub genes was identified, and validated in an independent dataset, mice with PF, and MRC-5 cells. Moreover, the abnormal gene expression was normalized by JHF. These findings provide guidance for further exploration of the pathogenesis and treatment of IPF.

Pathogenesis

Fold change

10.1016/j.intimp.2024.112048

Cite

Citations (0)

Maintenance of airway epithelial barrier integrity via the inhibition of AHR/EGFR activation ameliorates chronic obstructive pulmonary disease using effective-component combination

Phytomedicine (2023)

Yanxin Wei Xuefang Liu Yuhang Jiang Qingzhou Guan Yange Tian

Airway epithelial barrier dysfunction is highly related to the pathogenesis of chronic obstructive pulmonary disease (COPD). Effective-component combination (ECC) derived from Bufei Yishen formula (BYF) is an effective treatment regimen for patients with COPD and has previously been found to attenuate COPD and airway epithelial inflammation in rats.To determine the mechanism underlying the protective effects of ECC-BYF against the disruption of the airway epithelial barrier in COPD.The protective effects of ECC-BYF on the airway epithelial barrier were investigated in a rat COPD model. BEAS-2B epithelial cells were stimulated with cigarette smoke extract (CSE) to determine the direct effects of ECC-BYF on epithelial barrier function and aryl hydrocarbon receptor (AHR)/ epidermal growth factor receptor (EGFR) signaling.The results revealed that ECC-BYF attenuated COPD in rats and maintained the airway epithelial barrier by upregulating the expression of apical junction proteins, including occludin (OCC), zonula occludens (ZO)-1, and E-cadherin (E-cad). In BEAS-2B cells, ECC-BYF decreased permeability, increased transepithelial electrical resistance, and prevented the decrease in OCC, ZO-1, and E-cad expression induced by CSE exposure. In addition, transcriptomics and network analysis revealed that the protective effects of ECC-BYF may be related to multiple signaling pathways, including ErbB, AHR, and PI3K-Akt-mTOR pathways. ECC-BYF treatment suppressed the protein levels of p-EGFR and p-ERK1/2 and mRNA levels of CYP1A1 in CSE-exposed BEAS-2B cells as well as the protein levels of p-EGFR, p-ERK1/2, and CYP1A1 in the lungs of rats with COPD. In BEAS-2B cells, the AHR agonist FICZ weakened the protective effect of ECC-BYF on the epithelial barrier by suppressing the increase in ZO-1 and OCC expression induced by ECC-BYF and preventing the inhibitory effects of ECC-BYF on EGFR phosphorylation.This is the first study to demonstrate the protective effect of ECC-BYF on airway epithelial barrier function. The underlying mechanism may be associated with the suppression of the AHR/EGFR pathway to promote apical junction protein adhesion.

Occludin

Barrier function

10.1016/j.phymed.2023.154980

Cite

Citations (6)

A qualitative signature for early diagnosis of hepatocellular carcinoma based on relative expression orderings

Liver International (2018)

Lu Ao Zimei Zhang Qingzhou Guan Yating Guo You Guo

Abstract Background & Aims Currently, using biopsy specimens to confirm suspicious liver lesions of early hepatocellular carcinoma are not entirely reliable because of insufficient sampling amount and inaccurate sampling location. It is necessary to develop a signature to aid early hepatocellular carcinoma diagnosis using biopsy specimens even when the sampling location is inaccurate. Methods Based on the within‐sample relative expression orderings of gene pairs, we identified a simple qualitative signature to distinguish both hepatocellular carcinoma and adjacent non‐tumour tissues from cirrhosis tissues of non‐hepatocellular carcinoma patients. Results A signature consisting of 19 gene pairs was identified in the training data sets and validated in 2 large collections of samples from biopsy and surgical resection specimens. For biopsy specimens, 95.7% of 141 hepatocellular carcinoma tissues and all (100%) of 108 cirrhosis tissues of non‐hepatocellular carcinoma patients were correctly classified. Especially, all (100%) of 60 hepatocellular carcinoma adjacent normal tissues and 77.5% of 80 hepatocellular carcinoma adjacent cirrhosis tissues were classified to hepatocellular carcinoma. For surgical resection specimens, 99.7% of 733 hepatocellular carcinoma specimens were correctly classified to hepatocellular carcinoma, while 96.1% of 254 hepatocellular carcinoma adjacent cirrhosis tissues and 95.9% of 538 hepatocellular carcinoma adjacent normal tissues were classified to hepatocellular carcinoma. In contrast, 17.0% of 47 cirrhosis from non‐hepatocellular carcinoma patients waiting for liver transplantation were classified to hepatocellular carcinoma, indicating that some patients with long‐lasting cirrhosis could have already gained hepatocellular carcinoma characteristics. Conclusions The signature can distinguish both hepatocellular carcinoma tissues and tumour‐adjacent tissues from cirrhosis tissues of non‐hepatocellular carcinoma patients even using inaccurately sampled biopsy specimens, which can aid early diagnosis of hepatocellular carcinoma.

10.1111/liv.13864

Cite

Citations (31)

Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples

Oncotarget (2016)

Rou Chen Qingzhou Guan Jun Cheng Jun He Huaping Liu

Formalin-fixed paraffin-embedded (FFPE) samples represent a valuable resource for clinical researches.However, FFPE samples are usually considered an unreliable source for gene expression analysis due to the partial RNA degradation.In this study, through comparing gene expression profiles between FFPE samples and paired fresh-frozen (FF) samples for three cancer types, we firstly showed that expression measurements of thousands of genes had at least two-fold change in FFPE samples compared with paired FF samples.Therefore, for a transcriptional signature based on risk scores summarized from the expression levels of the signature genes, the risk score thresholds trained from FFPE (or FF) samples could not be applied to FF (or FFPE) samples.On the other hand, we found that more than 90% of the relative expression orderings (REOs) of gene pairs in the FF samples were maintained in their paired FFPE samples and largely unaffected by the storage time.The result suggested that the REOs of gene pairs were highly robust against partial RNA degradation in FFPE samples.Finally, as a case study, we developed a REOs-based signature to distinguish liver cirrhosis from hepatocellular carcinoma (HCC) using FFPE samples.The signature was validated in four datasets of FFPE samples and eight datasets of FF samples.In conclusion, the valuable FFPE samples can be fully exploited to identify REOs-based diagnostic and prognostic signatures which could be robustly applicable to both FF samples and FFPE samples with degraded RNA.

10.18632/oncotarget.14257

Cite

Citations (57)

Additional file 1 of Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs

Figshare (2023)

Zhenzhen Zhang Qingzhou Guan Yange Tian Xuejie Shao Peng Zhao

Additional file 1: Table S1. The analyze network results of 1640 DEGs. Table S2. GO terms of the 18 hub genes. Table S3. KEGG pathways of the 18 hub genes. Table S4. Target microRNAs of SPP1 based on five online miRNA databases. Table S5. Target microRNAs of VEGFA based on five online miRNA databases. Table S6. Target microRNAs of COL1A1 based on five online miRNA databases. Table S7. Target microRNAs of CAV1 based on five online miRNA databases. Table S8. Target microRNAs of PECAM1 based on five online miRNA databases. Table S9. Target microRNAs of BMP4 based on five online miRNA databases. Table S10. Target microRNAs of FYN based on five online miRNA databases. Table S11. Traditional Chinese medicine prediction results of COL1A1. Table S12. Traditional Chinese medicine prediction results of VEGFA. Table S13. Traditional Chinese medicine prediction results of SPP1.

Identification

10.6084/m9.figshare.24248082

Cite

Citations (0)