MOESM3 of A novel gene-expression-signature-based model for prediction of response to Tripterysium glycosides tablet for rheumatoid arthritis patients

Additional file 3. Differentially expressed genes between responder and non-responder groups

MOESM2 of A novel gene-expression-signature-based model for prediction of response to Tripterysium glycosides tablet for rheumatoid arthritis patients

Additional file 2. Primer sequences used in the qPCR analysis

MOESM1 of A novel gene-expression-signature-based model for prediction of response to Tripterysium glycosides tablet for rheumatoid arthritis patients

Additional file 1. Detailed information of clinical and inflammatory parameters of RA patients enrolled in the current study

Artesunate Sensitizes Human hepatocellular carcinoma to sorafenib via exacerbating AFAP1L2-SRC-FUNDC1 axis-dependent mitophagy

Sorafenib is the most widely used first-line drug for the treatment of the advanced hepatocellular carcinoma (HCC). Unfortunately, sorafenib resistance often limits its therapeutic efficacy. To evaluate the efficacy of artesunate against sorafenib-resistant HCC and to investigate its underlying pharmacological mechanisms, a “sorafenib resistance related gene-ART candidate target” interaction network was constructed, and a signaling axis consisting with artesunate candidate target AFAP1L2 and sorafenib target SRC, and the downstream FUNDC1-dependent mitophagy was identified as a major contributor to the sorafenib resistance and a potential way of artesunate to mitigate resistance. Notably, our clinical data demonstrated that AFAP1L2 expression in HCC tissues was markedly higher than that in adjacent non-cancerous liver tissues (P P < 0.05). Experimentally, AFAP1L2 was overexpressed in sorafenib resistant cells, leading to the activation of downstream SRC-FUNDC1 signaling axis, further blocking the FUNDC1 recruitment of LC3B to mitochondria and inhibiting the activation of mitophagy, based on both in vitro and in vivo systems. Moreover, artesunate significantly enhanced the inhibitory effects of sorafenib on resistant cells and tumors by inducing excessive mitophagy. Mechanically, artesunate reduced the expression of AFAP1L2 protein, suppressed the phosphorylation levels of SRC and FUNDC1 proteins, promoted the FUNDC1 recruitment of massive LC3B to mitochondria, and further overactivated the mitophagy and subsequent cell apoptosis of sorafenib resistant cells. In conclusion, artesunate may be a promising strategy to mitigate sorafenib resistance in HCC via exacerbating AFAP1L2-SRC-FUNDC1 axis-dependent mitophagy.</p

A systematic investigation based on microRNA-mediated gene regulatory network reveals that dysregulation of microRNA-19a/Cyclin D1 axis confers an oncogenic potential and a worse prognosis in human hepatocellular carcinoma

<div><p>MicroRNAs (miRNAs) contribute to a wide variety of human diseases by regulating gene expression, leading to imbalances in gene regulatory networks. To discover novel hepatocellular carcinoma (HCC)-related miRNA-target axes and to elucidate their functions, we here performed a systematic investigation combining biological data acquisition and integration, miRNA-target prediction, network construction, functional assay and clinical validation. As a result, a total of 117 HCC differentially expressed miRNAs were identified, and 728 high confident target genes of these miRNAs were collected. Then, the interaction network of target genes was constructed and 221 key nodes with topological importance in the network were identified according to their topological features including degree, node-betweenness, closeness and K-coreness. Among these key nodes, Cyclin D1 had the highest node-betweenness, implying its bottleneck role in the network. Luciferase reporter assay confirmed that miRNA-19a, which was one of HCC downregulated miRNAs, directly targeted Cyclin D1 in HCC cells. Moreover, miR-19a might play inhibitory roles in HCC malignancy via regulating Cyclin D1 expression. Further clinical evidence also highlighted the prognostic potential of miR-19a/Cyclin D1 axis in HCC. In conclusion, this systematic investigation provides a framework to identify featured miRNAs and their target genes which are potent effectors in the occurrence and development of HCC. More importantly, miR-19a/Cyclin D1 axis might have promising applications as a therapeutic target and a prognostic marker for patients with HCC.</p></div

Table_7_A Novel Circulating miRNA-Based Model Predicts the Response to Tripterysium Glycosides Tablets: Moving Toward Model-Based Precision Medicine in Rheumatoid Arthritis.docx

<p>Accumulating clinical evidence show that not all rheumatoid arthritis (RA) patients benefit to the same extent from a Tripterygium wilfordii Hook F (TwHF)-based therapy-Tripterysium glycosides tablets (TG tablets), which emphasizes the need of predictive biomarkers and tools for drug response. Herein, we integrated TG tablets' response-related miRNA and mRNA expression profiles obtained from the clinical cohort-based microarray, miRNA target prediction, miRNA-target gene coexpression, as well as gene-gene interactions, to identify four candidate circulating miRNA biomarkers that were predictive of response to TG tablets. Moreover, we applied the support vector machines (SVM) algorithm to construct the prediction model for the treatment outcome of TG tablets based on the levels of the candidate miRNA biomarkers, and also confirmed its good performance via both 5-fold cross-validation and the independent clinical cohort validations. Collectively, this circulating miRNA-based biomarker model may assist in screening the responsive RA patients to TG tablets and thus potentially benefit individualized therapy of RA in a daily clinical setting.</p

Table_1_A Novel Circulating miRNA-Based Model Predicts the Response to Tripterysium Glycosides Tablets: Moving Toward Model-Based Precision Medicine in Rheumatoid Arthritis.DOCX

<p>Accumulating clinical evidence show that not all rheumatoid arthritis (RA) patients benefit to the same extent from a Tripterygium wilfordii Hook F (TwHF)-based therapy-Tripterysium glycosides tablets (TG tablets), which emphasizes the need of predictive biomarkers and tools for drug response. Herein, we integrated TG tablets' response-related miRNA and mRNA expression profiles obtained from the clinical cohort-based microarray, miRNA target prediction, miRNA-target gene coexpression, as well as gene-gene interactions, to identify four candidate circulating miRNA biomarkers that were predictive of response to TG tablets. Moreover, we applied the support vector machines (SVM) algorithm to construct the prediction model for the treatment outcome of TG tablets based on the levels of the candidate miRNA biomarkers, and also confirmed its good performance via both 5-fold cross-validation and the independent clinical cohort validations. Collectively, this circulating miRNA-based biomarker model may assist in screening the responsive RA patients to TG tablets and thus potentially benefit individualized therapy of RA in a daily clinical setting.</p

Table_4_A Novel Circulating miRNA-Based Model Predicts the Response to Tripterysium Glycosides Tablets: Moving Toward Model-Based Precision Medicine in Rheumatoid Arthritis.XLSX

<p>Accumulating clinical evidence show that not all rheumatoid arthritis (RA) patients benefit to the same extent from a Tripterygium wilfordii Hook F (TwHF)-based therapy-Tripterysium glycosides tablets (TG tablets), which emphasizes the need of predictive biomarkers and tools for drug response. Herein, we integrated TG tablets' response-related miRNA and mRNA expression profiles obtained from the clinical cohort-based microarray, miRNA target prediction, miRNA-target gene coexpression, as well as gene-gene interactions, to identify four candidate circulating miRNA biomarkers that were predictive of response to TG tablets. Moreover, we applied the support vector machines (SVM) algorithm to construct the prediction model for the treatment outcome of TG tablets based on the levels of the candidate miRNA biomarkers, and also confirmed its good performance via both 5-fold cross-validation and the independent clinical cohort validations. Collectively, this circulating miRNA-based biomarker model may assist in screening the responsive RA patients to TG tablets and thus potentially benefit individualized therapy of RA in a daily clinical setting.</p

Table_5_A Novel Circulating miRNA-Based Model Predicts the Response to Tripterysium Glycosides Tablets: Moving Toward Model-Based Precision Medicine in Rheumatoid Arthritis.XLSX

<p>Accumulating clinical evidence show that not all rheumatoid arthritis (RA) patients benefit to the same extent from a Tripterygium wilfordii Hook F (TwHF)-based therapy-Tripterysium glycosides tablets (TG tablets), which emphasizes the need of predictive biomarkers and tools for drug response. Herein, we integrated TG tablets' response-related miRNA and mRNA expression profiles obtained from the clinical cohort-based microarray, miRNA target prediction, miRNA-target gene coexpression, as well as gene-gene interactions, to identify four candidate circulating miRNA biomarkers that were predictive of response to TG tablets. Moreover, we applied the support vector machines (SVM) algorithm to construct the prediction model for the treatment outcome of TG tablets based on the levels of the candidate miRNA biomarkers, and also confirmed its good performance via both 5-fold cross-validation and the independent clinical cohort validations. Collectively, this circulating miRNA-based biomarker model may assist in screening the responsive RA patients to TG tablets and thus potentially benefit individualized therapy of RA in a daily clinical setting.</p

Table_2_A Novel Circulating miRNA-Based Model Predicts the Response to Tripterysium Glycosides Tablets: Moving Toward Model-Based Precision Medicine in Rheumatoid Arthritis.DOCX

<p>Accumulating clinical evidence show that not all rheumatoid arthritis (RA) patients benefit to the same extent from a Tripterygium wilfordii Hook F (TwHF)-based therapy-Tripterysium glycosides tablets (TG tablets), which emphasizes the need of predictive biomarkers and tools for drug response. Herein, we integrated TG tablets' response-related miRNA and mRNA expression profiles obtained from the clinical cohort-based microarray, miRNA target prediction, miRNA-target gene coexpression, as well as gene-gene interactions, to identify four candidate circulating miRNA biomarkers that were predictive of response to TG tablets. Moreover, we applied the support vector machines (SVM) algorithm to construct the prediction model for the treatment outcome of TG tablets based on the levels of the candidate miRNA biomarkers, and also confirmed its good performance via both 5-fold cross-validation and the independent clinical cohort validations. Collectively, this circulating miRNA-based biomarker model may assist in screening the responsive RA patients to TG tablets and thus potentially benefit individualized therapy of RA in a daily clinical setting.</p