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学者姓名：谭建军
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Abstract ：
Current therapeutic drugs are inadequate for curing tumors, highlighting the need for novel tumor drugs. The advancement of single-cell RNA sequencing (scRNA-seq) technology offers new opportunities for tumor drug discovery. This technology allows us to explore tumor heterogeneity and developmental mechanisms at the single-cell level. In this review, we outline the application of scRNAseq in tumor drug discovery stages, including elucidating tumor mechanisms, identifying targets, screening drugs, and understanding drug action and resistance. We also discuss the challenges and future prospects of using scRNA-seq in drug development, providing a scientific foundation for advancing tumor therapies.
Keyword ：
tumor heterogeneity tumor heterogeneity tumor drug discovery tumor drug discovery tumor drug screening tumor drug screening single-cell RNA-sequencing single-cell RNA-sequencing target discovery target discovery
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| GB/T 7714 | Zhang, Lu , Yang, Yueying , Tan, Jianjun . Applications and emerging challenges of single-cell RNA sequencing technology in tumor drug discovery [J]. | DRUG DISCOVERY TODAY , 2025 , 30 (2) . |
| MLA | Zhang, Lu 等. "Applications and emerging challenges of single-cell RNA sequencing technology in tumor drug discovery" . | DRUG DISCOVERY TODAY 30 . 2 (2025) . |
| APA | Zhang, Lu , Yang, Yueying , Tan, Jianjun . Applications and emerging challenges of single-cell RNA sequencing technology in tumor drug discovery . | DRUG DISCOVERY TODAY , 2025 , 30 (2) . |
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Abstract ：
Ferroptosis, a commonly observed type of programmed cell death caused by abnormal metabolic and biochemical mechanisms, is frequently triggered by cellular stress. The occurrence of ferroptosis is predominantly linked to pathophysiological conditions due to the substantial impact of various metabolic pathways, including fatty acid metabolism and iron regulation, on cellular reactions to lipid peroxidation and ferroptosis. This mode of cell death serves as a fundamental factor in the development of numerous diseases, thereby presenting a range of therapeutic targets. Single-cell sequencing technology provides insights into the cellular and molecular characteristics of individual cells, as opposed to bulk sequencing, which provides data in a more generalized manner. Single-cell sequencing has found extensive application in the field of cancer research. This paper reviews the progress made in ferroptosis-associated cancer research using single-cell sequencing, including ferroptosis-associated pathways, immune checkpoints, biomarkers, and the identification of cell clusters associated with ferroptosis in tumors. In general, the utilization of single-cell sequencing technology has the potential to contribute significantly to the investigation of the mechanistic regulatory pathways linked to ferroptosis. Moreover, it can shed light on the intricate connection between ferroptosis and cancer. This technology holds great promise in advancing tumor-wide diagnosis, targeted therapy, and prognosis prediction. Graphical Abstract
Keyword ：
single-cell sequencing single-cell sequencing cancer cancer biomarker biomarker immunotherapy immunotherapy ferroptosis ferroptosis
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| GB/T 7714 | Du, Zhaolan , Shi, Yi , Tan, Jianjun . Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer [J]. | BRIEFINGS IN FUNCTIONAL GENOMICS , 2024 , 23 (6) : 713-725 . |
| MLA | Du, Zhaolan 等. "Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer" . | BRIEFINGS IN FUNCTIONAL GENOMICS 23 . 6 (2024) : 713-725 . |
| APA | Du, Zhaolan , Shi, Yi , Tan, Jianjun . Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer . | BRIEFINGS IN FUNCTIONAL GENOMICS , 2024 , 23 (6) , 713-725 . |
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Abstract ：
Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effec-tively narrow down the screening scope of biological experi-ments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.
Keyword ：
computational methods computational methods human diseases human diseases lncRNA-disease associations lncRNA-disease associations lncRNA lncRNA similarity calculation similarity calculation
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| GB/T 7714 | Yan, Jing , Wang, Ruobing , Tan, Jianjun . Recent advances in predicting lncRNA- disease associations based on computational methods [J]. | DRUG DISCOVERY TODAY , 2023 , 28 (2) . |
| MLA | Yan, Jing 等. "Recent advances in predicting lncRNA- disease associations based on computational methods" . | DRUG DISCOVERY TODAY 28 . 2 (2023) . |
| APA | Yan, Jing , Wang, Ruobing , Tan, Jianjun . Recent advances in predicting lncRNA- disease associations based on computational methods . | DRUG DISCOVERY TODAY , 2023 , 28 (2) . |
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Abstract ：
Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.
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| GB/T 7714 | Li, Xiaoyi , Qu, Wenyan , Yan, Jing et al. RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 , 64 (7) : 2221-2235 . |
| MLA | Li, Xiaoyi et al. "RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 64 . 7 (2023) : 2221-2235 . |
| APA | Li, Xiaoyi , Qu, Wenyan , Yan, Jing , Tan, Jianjun . RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 , 64 (7) , 2221-2235 . |
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Abstract ：
Triple-negative breast cancer (TNBC) is an aggressive subtype of mammary carcinoma characterized by low expression levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Along with the rapid development of the single-cell RNA-sequencing (scRNA-seq) technology, the heterogeneity within the tumor microenvironment (TME) could be studied at a higher resolution level, facilitating an exploration of the mechanisms leading to poor prognosis during tumor progression. In previous studies, hypoxia was considered as an intrinsic characteristic of TME in solid tumors, which would activate downstream signaling pathways associated with angiogenesis and metastasis. Moreover, hypoxia-related genes (HRGs) based risk score models demonstrated nice performance in predicting the prognosis of TNBC patients. However, it is essential to further investigate the heterogeneity within hypoxic TME, such as intercellular communications. In the present study, utilizing single-sample Gene Set Enrichment Analysis (ssGSEA) and cell-cell communication analysis on the scRNA-seq data retrieved from Gene Expression Omnibus (GEO) database with accession number GSM4476488, we identified four tumor subpopulations with diverse functions, particularly a hypoxia-related one. Furthermore, results of cell-cell communication analysis revealed the dominant role of the hypoxic tumor subpopulation in angiogenesis-and metastasis-related signaling pathways as a signal sender. Consequently, regard the TNBC cohorts acquired from The Cancer Genome Atlas (TCGA) and GEO as train set and test set respectively, we constructed a risk score model with reliable capacity for the prediction of overall survival (OS), where ARTN and L1CAM were identified as risk factors promoting angiogenesis and metastasis of tumors.The expression of ARTN and L1CAM were further analyzed through tumor immune estimation resource (TIMER) platform. In conclusion, these two marker genes of the hypoxic tumor subpopulation played vital roles in tumor development, indicating poor prognosis in TNBC patients.
Keyword ：
triple-negative breast cancer triple-negative breast cancer single-cell RNA-sequencing single-cell RNA-sequencing hypoxia hypoxia cell-cell communication cell-cell communication prognosis prognosis
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| GB/T 7714 | Shi, Yi , Huang, Xiaoqian , Du, Zhaolan et al. Analysis of single-cell RNA-sequencing data identifies a hypoxic tumor subpopulation associated with poor prognosis in triple-negative breast cancer [J]. | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2022 , 19 (6) : 5793-5812 . |
| MLA | Shi, Yi et al. "Analysis of single-cell RNA-sequencing data identifies a hypoxic tumor subpopulation associated with poor prognosis in triple-negative breast cancer" . | MATHEMATICAL BIOSCIENCES AND ENGINEERING 19 . 6 (2022) : 5793-5812 . |
| APA | Shi, Yi , Huang, Xiaoqian , Du, Zhaolan , Tan, Jianjun . Analysis of single-cell RNA-sequencing data identifies a hypoxic tumor subpopulation associated with poor prognosis in triple-negative breast cancer . | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2022 , 19 (6) , 5793-5812 . |
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Abstract ：
Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.
Keyword ：
human diseases human diseases machine learning methods machine learning methods lncRNA-disease associations lncRNA-disease associations predictive models predictive models lncRNA lncRNA
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| GB/T 7714 | Tan, Jianjun , Li, Xiaoyi , Zhang, Lu et al. Recent advances in machine learning methods for predicting LncRNA and disease associations [J]. | FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY , 2022 , 12 . |
| MLA | Tan, Jianjun et al. "Recent advances in machine learning methods for predicting LncRNA and disease associations" . | FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY 12 (2022) . |
| APA | Tan, Jianjun , Li, Xiaoyi , Zhang, Lu , Du, Zhaolan . Recent advances in machine learning methods for predicting LncRNA and disease associations . | FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY , 2022 , 12 . |
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Abstract ：
Long non-coding RNAs (LncRNAs) play important roles in a series of life activities, and they function primarily with proteins. The wet experimental-based methods in lncRNA-protein interactions (lncRPIs) study are timeconsuming and expensive. In this study, we propose for the first time a novel feature fusion method, the LPICSFFR, to train and predict LncRPIs based on a Convolutional Neural Network (CNN) with feature reuse and serial fusion in sequences, secondary structures, and physicochemical properties of proteins and lncRNAs. The experimental results indicate that LPI-CSFFR achieves excellent performance on the datasets RPI1460 and RPI1807 with an accuracy of 83.7 % and 98.1 %, respectively. We further compare LPI-CSFFR with the state-ofthe-art existing methods on the same benchmark datasets to evaluate the performance. In addition, to test the generalization performance of the model, we independently test sample pairs of five model organisms, where Mus musculus are the highest prediction accuracy of 99.5 %, and we find multiple hotspot proteins after constructing an interaction network. Finally, we test the predictive power of the LPI-CSFFR for sample pairs with unknown interactions. The results indicate that LPI-CSFFR is promising for predicting potential LncRPIs. The relevant source code and the data used in this study are available at https://github.com/JianjunTan-Beijing/LPICSFFR.
Keyword ：
LncRNA-protein interactions LncRNA-protein interactions Convolution neural network Convolution neural network Serial fusion Serial fusion Feature reuse Feature reuse
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| GB/T 7714 | Huang, Xiaoqian , Shi, Yi , Yan, Jing et al. LPI-CSFFR: Combining serial fusion with feature reuse for predicting LncRNA-protein interactions [J]. | COMPUTATIONAL BIOLOGY AND CHEMISTRY , 2022 , 99 . |
| MLA | Huang, Xiaoqian et al. "LPI-CSFFR: Combining serial fusion with feature reuse for predicting LncRNA-protein interactions" . | COMPUTATIONAL BIOLOGY AND CHEMISTRY 99 (2022) . |
| APA | Huang, Xiaoqian , Shi, Yi , Yan, Jing , Qu, Wenyan , Li, Xiaoyi , Tan, Jianjun . LPI-CSFFR: Combining serial fusion with feature reuse for predicting LncRNA-protein interactions . | COMPUTATIONAL BIOLOGY AND CHEMISTRY , 2022 , 99 . |
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Abstract ：
Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high morbidity and mortality globally. Compared with traditional diagnostic methods, microRNAs (miRNAs) are novel biomarkers with higher accuracy. Objective: We aimed to identify combinatorial biomarkers of miRNAs to construct a classification model for the diagnosis of HCC. Methods: The mature miRNA expression profile data of six cancers (liver, lung, gastric, breast, prostate, and colon) were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database with accession number GSE36915, GSE29250, GSE99417, GSE41970, GSE64333 and GSE35982. The messenger RNA (mRNA) expression profile data of these six cancers were obtained from TCGA. Three R software packages, student's t-test, and a normalized fold-change method were utilized to identify HCC-specific differentially expressed miRNAs (DEMs). Using all combinations of obtained HCC-specific DEMs as input features, we constructed a classification model by support vector machine searching for the optimal combination. Furthermore, target genes prediction was conducted on the miRWalk 2.0 website to obtain differentially expressed mRNAs (DEmRNAs), and KEGG pathway enrichment was analyzed on the DAVID website. Results: The optimal combination consisted of four miRNAs (hsa-miR-130a-3p, hsa-miR-450b-5p, hsa-miR-136-5p, and hsa-miR-24-1-5p), of which the last one has not been currently reported to be relevant to HCC. The target genes of hsa-miR-24-1-5p (CDC7, ACACA, CTNNA1, and NF2) were involved in the cell cycle, AMPK signaling pathway, Hippo signaling pathway, and insulin signaling pathway, which affect the proliferation, metastasis, and apoptosis of cancer cells. Moreover, the area under the receiver operating characteristic curves of the four miRNAs were all higher than 0.85. Conclusion: These results suggest that the miRNAs combined biomarkers were reliable for the diagnosis of HCC. Hsa-miR-24-1-5p was a novel biomarker for HCC diagnosis identified in this study.
Keyword ：
TCGA TCGA diagnosis diagnosis Hepatocellular carcinoma Hepatocellular carcinoma biomarker biomarker microRNAs microRNAs GEO GEO
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| GB/T 7714 | Shi, Yi , Men, Jingrui , Sun, Hongliang et al. The Identification and Analysis of MicroRNAs Combined Biomarkers for Hepatocellular Carcinoma Diagnosis [J]. | MEDICINAL CHEMISTRY , 2022 , 18 (10) : 1073-1085 . |
| MLA | Shi, Yi et al. "The Identification and Analysis of MicroRNAs Combined Biomarkers for Hepatocellular Carcinoma Diagnosis" . | MEDICINAL CHEMISTRY 18 . 10 (2022) : 1073-1085 . |
| APA | Shi, Yi , Men, Jingrui , Sun, Hongliang , Tan, Jianjun . The Identification and Analysis of MicroRNAs Combined Biomarkers for Hepatocellular Carcinoma Diagnosis . | MEDICINAL CHEMISTRY , 2022 , 18 (10) , 1073-1085 . |
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Abstract ：
Objective Lung cancer is one of the most common cancers in the world. Lung adenocarcinoma (LUAD) has the highest annual mortality rate among lung cancer patients. It has been reported that changes in gene spectrum were associated with the process of tumorigenesis and its development. The purpose of this study is to identify the gene signatures associated with LUAD and to further analyze their prognostic significance. Methods Weighted gene co-expression network analysis (WGCNA), differential gene analysis, cox regression analysis, and protein-protein interaction (PPI) network analysis were used to screen the hub genes highly related to LUAD based on The Cancer Genome Atlas (TCGA) database. The RNA-seq data sets from TCGA and GTEx (Genotype Tissue Expression) database were combined and divided into a training set and a validation set, which were used to construct the diagnostic model by support vector machine recursive feature elimination feature (SVM-RFE) algorithm. GSE328613 and GSE31210 were used to verify the diagnostic accuracy of the model and the prognostic value of our obtained gene signatures, respectively. Results The results demonstrated that the model of 5 gene signattues (anln, cenpa, plk 1 tpx2, cdca3) obtained by the SVM-RFE algorithm had an outstanding performance in the classification of LUAD patients. Functional enrichment analysis showed that these 5 gene signatures were highly related to the biological process of tumor initiation and progression. What's more, LEAD patients with high expression of these 5 genes also exerted a poor outcome in survival status. Conclusion Therefore, we could conclude that our study obtained useful models with 5 gene signatures for the diagnosis and prognosis of LUAD, which were essential for the development of novel targets applied in precision therapy.
Keyword ：
gene signature gene signature WGCNA WGCNA SVM-REE SVM-REE lung adenocarcinoma lung adenocarcinoma
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| GB/T 7714 | Wang Mei , Wang Ke-Xin , Tan Jian-Jun et al. Identification of Gene Signatures Associated With Lung Adenocarcinoma Diagnosis and Prognosis Based on WGCNA and SVM-RFE Algorithm [J]. | PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS , 2022 , 49 (2) : 381-394 . |
| MLA | Wang Mei et al. "Identification of Gene Signatures Associated With Lung Adenocarcinoma Diagnosis and Prognosis Based on WGCNA and SVM-RFE Algorithm" . | PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS 49 . 2 (2022) : 381-394 . |
| APA | Wang Mei , Wang Ke-Xin , Tan Jian-Jun , Wang Jing-Jing . Identification of Gene Signatures Associated With Lung Adenocarcinoma Diagnosis and Prognosis Based on WGCNA and SVM-RFE Algorithm . | PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS , 2022 , 49 (2) , 381-394 . |
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Abstract ：
Hepatocellular carcinoma (HCC) is one of the malignancies with high morbidity and mortality in the world. The purpose of our study was to search for HCC related miRNA prognostic biomarkers to predict the risk degree and survival time of HCC patients and provide effective prognostic information for HCC patients. Four methods were used to identify differentially expressed miRNAs (DEMs) from The Cancer Genome Atlas(TCGA). Kaplan-Meier survival curve, univariate and multivariable Cox regression analysis were used to identify prognostic miRNAs of HCC from DEMs. Four prognostic miRNAs biomarkers (hsa-miR-132-3p, hsa-miR-139-5p, hsa-miR-3677-3p, hsa-miR-500a-3p) of HCC were identified at last, and combined into a risk score model. There was no experimental evidence that hsa-mir-3677-3p is related to HCC, and it was a newly discovered miRNA in this study. The evaluation results of various bioinformatics methods, including survival curve, ROC curve, chi-square test, et al.. All indicated that the risk score calculated by the model can effectively predict the risk degree of patients(P<0.000, hazard ratio=2.551, 95% confidence interval=1.751-3.717). 1-5 year survival rates of HCC patients in the low risk group had 20%-30% higher than in the high risk group. Through the clinical data analysis, it was found that the combined biomarkers have a better prognostic effect than other clinical indicators, and can also be used as an independent prognostic factor. Target genes of four miRNAs were predicted, including AGO2, FOXO1, ROCK2, RAP1B, CYLD, et al., and enriched in biological processes such as cell proliferation, migration, apoptosis and immune response.
Keyword ：
hepatocellular carcinoma hepatocellular carcinoma miRNA miRNA prognosis prognosis TCGA TCGA biomarker biomarker
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| GB/T 7714 | Men Jing-Rui , Tan Jian-Jun , Sun Hong-Liang . The Identification and Analysis of a miRNA Risk Score Model for Hepatocellular Carcinoma Prognosis [J]. | PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS , 2020 , 47 (4) : 344-360 . |
| MLA | Men Jing-Rui et al. "The Identification and Analysis of a miRNA Risk Score Model for Hepatocellular Carcinoma Prognosis" . | PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS 47 . 4 (2020) : 344-360 . |
| APA | Men Jing-Rui , Tan Jian-Jun , Sun Hong-Liang . The Identification and Analysis of a miRNA Risk Score Model for Hepatocellular Carcinoma Prognosis . | PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS , 2020 , 47 (4) , 344-360 . |
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