Chao Chen

Bio: Chao Chen is an academic researcher from North Sichuan Medical College. The author has co-authored 1 publications.

Five metastasis-related mRNAs signature predicting the survival of patients with liver hepatocellular carcinoma.

Abstract: BACKGROUNDS Liver hepatocellular carcinoma (HCC) is one of the most malignant tumors, of which prognosis is unsatisfactory in most cases and metastatic of HCC often results in poor prognosis. In this study, we aimed to construct a metastasis- related mRNAs prognostic model to increase the accuracy of prediction of HCC prognosis. METHODS Three hundred seventy-four HCC samples and 50 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database, involving transcriptomic and clinical data. Metastatic-related genes were acquired from HCMBD website at the same time. Two hundred thirty-three samples were randomly divided into train dataset and test dataset with a proportion of 1:1 by using caret package in R. Kaplan-Meier method and univariate Cox regression analysis and lasso regression analysis were performed to obtain metastasis-related mRNAs which played significant roles in prognosis. Then, using multivariate Cox regression analysis, a prognostic prediction model was established. Transcriptome and clinical data were combined to construct a prognostic model and a nomogram for OS evaluation. Functional enrichment in high- and low-risk groups were also analyzed by GSEA. An entire set based on The International Cancer Genome Consortium(ICGC) database was also applied to verify the model. The expression levels of SLC2A1, CDCA8, ATG10 and HOXD9 are higher in tumor samples and lower in normal tissue samples. The expression of TPM1 in clinical sample tissues is just the opposite. RESULTS One thousand eight hundred ninety-five metastasis-related mRNAs were screened and 6 mRNAs were associated with prognosis. The overall survival (OS)-related prognostic model based on 5 MRGs (TPM1,SLC2A1, CDCA8, ATG10 and HOXD9) was significantly stratified HCC patients into high- and low-risk groups. The AUC values of the 5-gene prognostic signature at 1 year, 2 years, and 3 years were 0.786,0.786 and 0.777. A risk score based on the signature was a significantly independent prognostic factor (HR = 1.434; 95%CI = 1.275-1.612; P < 0.001) for HCC patients. A nomogram which incorporated the 5-gene signature and clinical features was also built for prognostic prediction. GSEA results that low- and high-risk group had an obviously difference in part of pathways. The value of this model was validated in test dataset and ICGC database. CONCLUSION Metastasis-related mRNAs prognostic model was verified that it had a predictable value on the prognosis of HCC, which could be helpful for gene targeted therapy.

Significance of HOXD transcription factors family in progression, migration and angiogenesis of cancer.

Abstract: The transcription factors (TFs) of the HOX family play significant roles during early embryonic development and cellular processes. They also play a key role in tumorigenesis as tumor oncogenes or suppressors. Furthermore, TFs of the HOXD geFIne cluster affect proliferation, migration, and invasion of tumors. Consequently, dysregulated activity of HOXD TFs has been linked to clinicopathological characteristics of cancer. HOXD TFs are regulated by non-coding RNAs and methylation of DNA on promoter and enhancer regions. In addition, HOXD genes modulate the biological function of cancer cells via the MEK and AKT signaling pathways, thus, making HOXD TFs, a suitable molecular marker for cancer prognosis and therapy. In this review, we summarized the roles of HOXD TFs in different cancers and highlighted its potential as a diagnostic and therapeutic target.

CDCA8 induced by NF-YA promotes hepatocellular carcinoma progression by regulating the MEK/ERK pathway

Abstract: Hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors. Cell division cycle associated 8 (CDCA8) is an important multifactorial regulator in cancers. However, its up and downstream targets and effects in HCC are still unclear.A comprehensive bioinformatics analysis was performed using The Cancer Genome Atlas dataset (TCGA) to explore novel core oncogenes. We quantified CDCA8 levels in HCC tumors using qRT-PCR. HCC cell's proliferative, migratory, and invasive abilities were detected using a Cell Counting Kit-8 (CCK-8) assay, 5-ethynyl-2'-deoxyuridine (EdU) assay, clone formation, and a Transwell assay. An orthotopic tumor model and tail vein model were constructed to determine the effects of CDCA8 inhibition in vivo. The mechanism underlying CDCA8 was investigated using RNA sequencing. The prognostic value of CDCA8 was assessed with immunohistochemical staining of the tissue microarrays.CDCA8 was identified as a novel oncogene during HCC development. The high expression of CDCA8 was an independent predictor for worse HCC outcomes both in publicly available datasets and in our cohort. We found that CDCA8 knockdown inhibited HCC cell proliferation, colony formation, and migration by suppressing the MEK/ERK pathway in vitro. Moreover, CDCA8 deficiency significantly inhibited tumorigenesis and metastasis. Next-generation sequencing and laboratory validation showed that CDCA8 silencing inhibited the expression of TPM3, NECAP2, and USP13. Furthermore, NA-YA overexpression upregulated the expression of CDCA8. CDCA8 knockdown could attenuate NF-YA-mediated cell invasion in vitro. The expression of NF-YA alone or in combined with CDCA8 were validated as significant independent risk factors for patient survival.Our findings revealed that the expression of CDCA8 alone or in combined with NF-YA contributed to cancer progression, and could serve as novel potential therapeutic targets for HCC patients.

Ubiquitin-proteasome system-based signature to predict the prognosis and drug sensitivity of hepatocellular carcinoma

Abstract: Background: Ubiquitin-proteasome system (UPS) is implicated in cancer occurrence and progression. Targeting UPS is emerging as a promising therapeutic target for cancer treatment. Nevertheless, the clinical significance of UPS in hepatocellular carcinoma (HCC) has not been entirely elucidated. Methods: Differentially expressed UPS genes (DEUPS) were screened from LIHC-TCGA datasets. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate regression analysis were conducted to establish a UPS-based prognostic risk model. The robustness of the risk model was further validated in HCCDB18, GSE14520, and GSE76427 cohorts. Subsequently, immune features, clinicopathologic characteristics, enrichment pathways, and anti-tumor drug sensitivity of the model were further evaluated. Moreover, a nomogram was established to improve the predictive ability of the risk model. Results: Seven UPS-based signatures (ATG10, FBXL7, IPP, MEX3A, SOCS2, TRIM54, and PSMD9) were developed for the prognostic risk model. Individuals with HCC with high-risk scores presented a more dismal prognosis than those with low-risk scores. Moreover, larger tumor size, advanced TNM stage, and tumor grade were observed in the high-risk group. Additionally, cell cycle, ubiquitin-mediated proteolysis, and DNA repair pathways were intimately linked to the risk score. In addition, obvious immune cell infiltration and sensitive drug response were identified in low-risk patients. Furthermore, both nomogram and risk score showed a significant prognosis-predictive ability. Conclusion: Overall, we established a novel UPS-based prognostic risk model in HCC. Our results will facilitate a deep understanding of the functional role of UPS-based signature in HCC and provide a reliable prediction of clinical outcomes and anti-tumor drug responses for patients with HCC.

Developing high-resolution metastasis signatures for improved cancer prognosis using single-cell RNA sequencing data：A case study in lung adenocarcinoma

Abstract: Abstract Background Metastasis remains the reason for high cancer mortality and it is a valuable predictive factor in cancer prognosis. Single-cell RNA sequencing (scRNA-seq) can reveal cellular heterogeneity in metastasis microenvironment and capture high-resolution signatures for improved cancer prediction. Methods An integrated analysis framework was designed for metastatic lung adenocarcinoma (LUAD) scRNA-seq profiles and we identified 9 key prognostic genes (KPGs) that were trained and validated in 407 internal and external patient cohorts using Lasso-Cox method and Receiver Operating Characteristic (ROC) curves. To ensure the predictive stability of the KPGs signatures, 10 random samples of data from the TCGA cohort were taken. Correlation analysis revealed the strong association between KPGs signatures and several clinical characteristics such as gender, T-stage, and N-stage. We incorporated these risk clinical variables into a KPGs nomogram model. Results The results based on ROC curves and calibration curves show that the KPGs nomogram model with superior accuracy for overall survival (OS) prediction. We also found that high risk group with high nomogram scores had poorer prognosis accompanied by a higher tumor mutation burden (TMB) and it was associated with the upregulation of cell cycle, DNA replication, ECM receptor interaction, P53 signaling pathway, spliceosome and proteasome pathway. Conclusions Mining single-cell resolution metastatic features from scRNA-seq data to improve cancer prognosis is a viable strategy that would be a useful tool in risk gene discovery and targeted therapy in metastatic cancers.

Characterization of chromatin regulators identified prognosis and heterogeneity in hepatocellular carcinoma

Abstract: Liver carcinogenesis is a multiprocess that involves complicated interactions between genetics, epigenetics, and transcriptomic alterations. Aberrant chromatin regulator (CR) expressions, which are vital regulatory epigenetics, have been found to be associated with multiple biological processes. Nevertheless, the impression of CRs on tumor microenvironment remodeling and hepatocellular carcinoma (HCC) prognosis remains obscure. Thus, this study aimed to systematically analyze CR-related patterns and their correlation with genomic features, metabolism, cuproptosis activity, and clinicopathological features of patients with HCC in The Cancer Genome Atlas, International Cancer Genome Consortium-LIRI-JP cohort, and GSE14520 that utilized unsupervised consensus clustering. Three CR-related patterns were recognized, and the CRs phenotype-related gene signature (CRsscore) was developed using the least absolute shrinkage and selection operator-Cox regression and multivariate Cox algorithms to represent the individual CR-related pattern. Additionally, the CRsscore was an independent prognostic index that served as a fine predictor for energy metabolism and cuproptosis activity in HCC. Accordingly, describing a wide landscape of CR characteristics may assist us to illustrate the sealed association between epigenetics, energy metabolism, and cuproptosis activity. This study may discern new tumor therapeutic targets and exploit personalized therapy for patients.