Frontiers in Immunology
·
2022
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IF 8.786
Hu Yu,Zhou Xuyue,Chen Lihao,Li Rong,Jin Shuang,Liu Lingxi,Ju Mei,Luan Chao,Chen Hongying,Wang Ziwei,Huang Dan,Chen Kun,Zhang Jiaan
Background: Keloids are a fibroproliferative disease characterized by unsatisfactory therapeutic effects and a high recurrence rate.Objective: This study aimed to investigate keloid-related circulating metabolic signatures.Methods: Untargeted metabolomic analysis was performed to compare the metabolic features of 15 keloid patients with those of paired healthy volunteers in the discovery cohort. The circulating metabolic signatures were selected using the least absolute shrinkage. Furthermore, the selection operators were quantified using multiple reaction monitoring-based target metabolite detection methods in the training and test cohorts.Results: More than ten thousand metabolic features were consistently observed in all the plasma samples from the discovery cohort, and 30 significantly different metabolites were identified. Four differentially expressed metabolites including palmitoylcarnitine, sphingosine, phosphocholine, and phenylalanylisoleucine, were discovered to be related to keloid risk in the training and test cohorts. In addition, using linear and logistic regression models, the respective risk scores for keloids based on a 4-metabolite fingerprint classifier were established to distinguish keloids from healthy volunteers.Conclusions: In summary, our findings show that the characteristics of circulating metabolic fingerprinting manifest phenotypic variation in keloid onset.
研究领域:
医学
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代谢组学
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皮肤病学
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DOI
PubMed
高影响因子
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
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2023
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IF 8.2
Qiqi Bu,Yangxu Deng,Qing Wang,Rongzhen Deng,Shaofan Hu,Zhigang Pei,Yiguo Zhang
Nrf1 (encoded by Nfe2l1 ) and Nrf2 (encoded by Nfe2l2 ), as two key members of the CNC-bZIP transcription factor, exhibit significant functional differences in their pathophysiology. Our previous findings demonstrated that loss of Nrf1α (i.e., a full-length isoform of Nrf1) promotes HepG2-derived tumor growth in xenograft mice, but malgrowth of the xenograft tumor is significantly suppressed by knockout of Nrf2. To gain insights into the mechanism underlying such marked distinctions in their pathologic phenotypes, we mined transcriptome data from liver cancer in the TCGA database to establish a prognostic model and calculate predicted risk scores for each cell line. The results revealed that knockout of Nrf1α markedly increased the risk score in HepG2 cells, whereas the risk score was reduced by knockout of Nrf2. Notably, stanniocalcin 2 (STC2), a biomarker associated with liver cancer, that is upexpressed in hepatocellular carcinoma (HCC) tissues with a reduction in the overall survival ratio of those patients. We observed increased expression levels of STC2 in Nrf1α −/− cells but decreased expression in Nrf2 −/− cells. These findings suggested that STC2 may play a role in mediating the distinction between Nrf1α −/− and Nrf2 −/− . Such potential function of STC2 was further corroborated through a series of experiments combined with transcriptomic sequencing. The results revealed that STC2 functions as a dominant tumor-promoter, because the STC2-leading increases in clonogenicity of hepatoma cells and malgrowth of relevant xenograft tumor were almost completely abolished in STC2 −/− cells. Together, these demonstrate that STC2 could be paved as a potential therapeutic target, albeit as a diagnostic marker, for HCC.
研究领域:
肿瘤学
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分子生物学
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遗传学
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转化医学
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产品关联
DOI
高影响因子
HUMAN MUTATION
·
2025
·Vol. 2025
·期 1
·5319444
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IF 3.7
Xin Wu,Linde Sun,Peifa Liu,Qiong Wang,Yu Wang
Background PANoptosis, as an inflammatory programmed cell death, is involved in tumor development. This study set out to discover novel PANoptosis-correlated prognostic signatures in stomach adenocarcinoma (STAD), a prevalent malignancy of the digestive system. Methods STAD samples were derived from a public database, and PANoptosis-related genes (PRGs) were acquired from existing reports. Prognosis-related PRGs were screened by univariate Cox regression analysis. Molecular subtypes of STAD were identified by the “ConsensusClusterPlus” package. The “Limma” package was employed to filter differentially expressed genes (DEGs) between different subtypes. PANoptosis-related prognostic signatures in STAD were identified to establish the RiskScore model. The RiskScore and some of the clinical features were integrated to establish a nomogram. Immune cell infiltration and TIDE score in different risk groups were compared. Correlation between immune checkpoint genes, drug sensitivity, and RiskScore was analyzed by the Spearman method. The biological function of PANoptosis-related signature genes in STAD was preliminarily explored by in vitro cell experiments. Results Based on 18 prognosis-related PRGs, two molecular subtypes of STAD were recognized, and the C1 subtype showed a lower overall survival (OS) rate than the C2 subtype. Further, three PANoptosis-related signature genes ( APOD , GPC3 , and SERPINE1 ) were determined to establish a RiskScore model that could accurately assess the prognostic outcomes for STAD patients. Then, by integrating RiskScore with clinical features, a nomogram was established. The high-risk group had higher immune cell infiltration and TIDE score and lower OS rate than those with a low risk. RiskScore was positively correlated with nine immune checkpoint genes. Besides, we screened 23 drugs that significantly correlated with RiskScore. In vitro cell experiments showed that the mRNA and protein levels of APOD , GPC3 , and SERPINE1 were upregulated in the STAD cell line and that APOD knockout significantly reduced cancer cell proliferation, migration, and invasion levels and increased the apoptotic capacity of the STAD cell line. Conclusion This study established a PANoptosis-related RiskScore model for assessing STAD patient prognosis, which could contribute to the personalized treatment of STAD.
研究领域:
肿瘤学
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分子生物学
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生物信息学
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免疫治疗
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精准医学
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DOI
PubMed
高影响因子
Frontiers in Oncology
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2020
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IF 4.848
Chen Zhongjian,Dai Yalan,Huang Xiancong,Chen Keke,Gao Yun,Li Na,Wang Ding,Chen Aiping,Yang Qingxia,Hong Yanjun,Zeng Su,Mao Weimin
Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with a poor prognosis. Although studies have shown metabolic reprogramming to be linked to ESCC development, no prognostic metabolic biomarkers or potential therapeutic metabolic targets have been identified.Method: The present study investigated some circulating metabolites associated with overall survival in 276 curatively resected ESCC patients using liquid chromatography/mass spectrometry metabolomics and Kaplan-Meier analysis. Tissue metabolomic analysis of 23-paired ESCC tissue samples was performed to discover metabolic dysregulation in ESCC cancerous tissue. A method consisting of support vector machine recursive feature elimination and LIMMA differential expression analysis was utilized to select promising feature genes within transcriptomic data from 179-paired ESCC tissue samples. Joint pathway analysis with genes and metabolites identified relevant metabolic pathways and targets for ESCC.Results: Four metabolites, kynurenine, 1-myristoyl-glycero-3-phosphocholine (LPC(14:0)sn-1), 2-piperidinone, and hippuric acid, were identified as prognostic factors in the preoperative plasma from ESCC patients. A risk score consisting of kynurenine and LPC(14:0)sn-1 significantly improved the prognostic performance of the tumor-node-metastasis staging system and was able to stratify risk for ESCC. Combined tissue metabolomic analysis and support vector machine recursive feature elimination gene selection revealed dysregulated kynurenine pathway as an important metabolic feature of ESCC, including accumulation of tryptophan, formylkynurenine, and kynurenine, as well as up-regulated indoleamine 2,3-dioxygenase 1 in ESCC cancerous tissue.Conclusions: This work identified for the first time four potential prognostic circulating metabolites. In addition, kynurenine pathway metabolism was shown to be up-regulated tryptophan-kynurenine metabolism in ESCC. Results not only provide a metabolite-based risk score system for prognosis, but also improve the understanding of the molecular basis of ESCC onset and progression, and as well as novel potential therapeutic targets for ESCC.
研究领域:
肿瘤学
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代谢组学
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生物标志物发现
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分子生物学
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产品关联
DOI
PubMed
高影响因子
ONCOLOGY RESEARCH
·
2024
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IF 2
QIAN HU,MENGYAO WANG,JINJIN WANG,YALI TAO,TING NIU
Multiple myeloma (MM) is a hematologic malignancy notorious for its high relapse rate and development of drug resistance, in which cell adhesion-mediated drug resistance plays a critical role. This study integrated four RNA sequencing datasets (CoMMpass, {"type":"entrez-geo","attrs":{"text":"GSE136337","term_id":"136337"}} GSE136337 , {"type":"entrez-geo","attrs":{"text":"GSE9782","term_id":"9782"}} GSE9782 , and {"type":"entrez-geo","attrs":{"text":"GSE2658","term_id":"2658"}} GSE2658 ) and focused on analyzing 1706 adhesion-related genes. Rigorous univariate Cox regression analysis identified 18 key prognosis-related genes, including KIF14, TROAP, FLNA, MSN, LGALS1, PECAM1, and ALCAM, which demonstrated the strongest associations with poor overall survival (OS) in MM patients. To comprehensively evaluate the impact of cell adhesion on MM prognosis, an adhesion-related risk score (ARRS) model was constructed using Lasso Cox regression analysis. The ARRS model emerged as an independent prognostic factor for predicting OS. Furthermore, our findings revealed that a heightened cell adhesion effect correlated with tumor resistance to DNA-damaging drugs, protein kinase inhibitors, and drugs targeting the PI3K/Akt/mTOR signaling pathway. Nevertheless, we identified promising drug candidates, such as tirofiban, pirenzepine, erlotinib, and bosutinib, which exhibit potential in reversing this resistance. In vitro , experiments employing NCIH929, RPMI8226, and AMO1 cell lines confirmed that MM cell lines with high ARRS exhibited poor sensitivity to the aforementioned candidate drugs. By employing siRNA-mediated knockdown of the key ARRS model gene KIF14, we observed suppressed proliferation of NCIH929 cells, along with decreased adhesion to BMSCs and fibronectin. This study presents compelling evidence establishing cell adhesion as a significant prognostic factor in MM. Additionally, potential molecular mechanisms underlying adhesion-related resistance are proposed, along with viable strategies to overcome such resistance. These findings provide a solid scientific foundation for facilitating clinically stratified treatment of MM.
研究领域:
肿瘤学
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血液学
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分子生物学
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药理学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Heliyon
·
2024
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IF 3.4
Zhihao Zhang,Feng Liu,Xin Lan,Fuhai Wang,Jiahao Sun,Honglong Wei
Objective This study explores the impact of pyroptosis-related genes (PRG) on the prognosis of liver cancer (LC). Methods 421 samples (371 tumor samples and 50 normal samples) from the Cancer Genome Atlas (TCGA) were included in this study. GSE14520 dataset (data of RNA expression and relevant clinicopathological features), GSE125449 dataset (single-cell data in LC) and HCCDB18 dataset (validation on the reliability of the model) were downloaded as appropriate. Download the PRG and its corresponding pathway information from the gene set enrichment analysis (GSEA) website. The consensus clustering was performed by ConsensusClusterPlus package. Differentially expressed genes (DEGs) were identified using limma package, and prognostic features were constructed using un/multivariate and Lasso Cox regression. Pathway enrichment analysis was conducted by ssGSEA method. Receiver Operating Characteristic and the survival analysis were conducted by timeROC and Survminer packages. The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validation, following the quantification on the key genes via reverse-transcription quantitative PCR, the Transwell and scratch assays were applied to evaluate the in-vitro invasion and migration of LC cells Huh-7. Results 12 prognosis-related genes were identified to be related to the progression of LC. Three subtypes including C1, C2 and C3 were categorized using the 12 prognosis-related genes and PRGs significantly related to the prognosis of LC patients. The worst and best prognosis was seen in C3 subtype and C2 subtype, respectively. Hallmark pathway enrichment analysis has shown the concurrent immunoactivation and immune escape in C3 subtype. A RiskScore model was constructed using 8 key genes (KPNA2, UCK2, FTCD, CBX2, RAB32, HMMR, S100A9 and ANXA10) from the DEGs of three subtypes. The RiskScore system as an independent prognostic factor dividing the patients into high and low risk groups, and patients of the high-risk group had poor prognosis in both test set and validation set. A nomogram model combining the risk score had the extreme higher benefit. Further, 6 subclusters were identified from scRNA-seq analysis, where the highest PYROPTOSIS score was seen in Monocytic-Macrophages. The quantification on the key genes has suggested the high expressions of KPNA2, UCK2, CBX2, RAB32, HMMR and S100A9 and the low expressions of FTCD and ANXA10 in LC cells Huh-7. Particularly, UCK2 knockdown evidently diminished the number of invaded and migrated LC cells in vitro . Conclusion The risk model associated with pyproptosis is crucial for the tumor immunity of LC and may serve as a prognostic indicator for patients suffering from LC. Our findings will offer new perspectives for immunotherapies targeting LC.
研究领域:
肿瘤学
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分子生物学
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生物信息学
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癌症基因组学
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细胞免疫学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
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