Feng Yi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Feng Yi has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Kai Fu
Institute of Molecular Precision Medicine and Hunan Key Laboratory of Molecular Precision Medicine, Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
1 shared publication Shichao Long
Institute of Molecular Precision Medicine and Hunan Key Laboratory of Molecular Precision Medicine, Department of General Surgery, Xiangya Hospital, Central South University, 410083 Changsha, Hunan, China
1 shared publication- Yuanbing Yao
Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
1 shared publication
Latest Publications
- Open Access
- 21 April 2025
- Published by IMR Press in Frontiers in Bioscience-Landmark
- Vol. 30 (4) , 33422
A Novel Signature Composed of Hypoxia, Glycolysis, Lactylation Related Genes to Predict Prognosis and Immunotherapy in Hepatocellular Carcinoma
Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. The hypoxic microenvironment in HCC enhances glycolysis and co-directed lactate accumulation, which leads to increased lactylation. However, the exact biological pattern remains to be elucidated. Therefore, we sought to identify hypoxia-glycolysis-lactylation (HGL) prognosis-related signatures and validate this in vitro. Methods: Transcriptomic data of patients with HCC were collected from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Differentially expressed HGL genes between HCC and normal tissues were obtained by DEseq2. The consensus clustering algorithm was employed to stratify patients into two distinct clusters. Subsequently, the single sample Gene Set Enrichment Analysis (ssGSEA), Tumor Immune Estimation Resource (TIMER) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to assess immune infiltration and immune evasion. Least Absolute Shrinkage and Selection Operator (LASSO) and COX regression analysis were used to identify an HGL prognosis-related signature. Based on spatial transcriptome and histological data, we analyzed the expression of these genes in HCC and explored the function of Homer Scaffold Protein 1 (HOMER1) in HCC cells. Results: We identified 72 differentially expressed HGL genes and two HGL clusters. Cluster2, with better survival (p < 0.001), was significantly enriched in metabolic-related pathways. The HGL prognosis-related signature exhibited great predictive efficacy for patients in TCGA, ICGC, and GSE148355 databases (3-year area under the curve (AUC) = 0.822, 0.738, and 0.707, respectively). The elevated expression of HOMER1 in HCC was revealed by the combination of spatial transcriptome and histological data. Knocking down HOMER1 significantly inhibited the malignant progression of HCC cells. Conclusions: We identified a signature with great predictive efficacy and discovered a gene, HOMER1, that influences the malignant progression of HCC with the potential to become a novel therapeutic target.
- 21 April 2025
- Published by IMR Press in Frontiers in Bioscience-Landmark
- Vol. 30 (4) , 33422
Open AccessA Novel Signature Composed of Hypoxia, Glycolysis, Lactylation Related Genes to Predict Prognosis and Immunotherapy in Hepatocellular Carcinoma
Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. The hypoxic microenvironment in HCC enhances glycolysis and co-directed lactate accumulation, which leads to increased lactylation. However, the exact biological pattern remains to be elucidated. Therefore, we sought to identify hypoxia-glycolysis-lactylation (HGL) prognosis-related signatures and validate this in vitro. Methods: Transcriptomic data of patients with HCC were collected from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Differentially expressed HGL genes between HCC and normal tissues were obtained by DEseq2. The consensus clustering algorithm was employed to stratify patients into two distinct clusters. Subsequently, the single sample Gene Set Enrichment Analysis (ssGSEA), Tumor Immune Estimation Resource (TIMER) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to assess immune infiltration and immune evasion. Least Absolute Shrinkage and Selection Operator (LASSO) and COX regression analysis were used to identify an HGL prognosis-related signature. Based on spatial transcriptome and histological data, we analyzed the expression of these genes in HCC and explored the function of Homer Scaffold Protein 1 (HOMER1) in HCC cells. Results: We identified 72 differentially expressed HGL genes and two HGL clusters. Cluster2, with better survival (p < 0.001), was significantly enriched in metabolic-related pathways. The HGL prognosis-related signature exhibited great predictive efficacy for patients in TCGA, ICGC, and GSE148355 databases (3-year area under the curve (AUC) = 0.822, 0.738, and 0.707, respectively). The elevated expression of HOMER1 in HCC was revealed by the combination of spatial transcriptome and histological data. Knocking down HOMER1 significantly inhibited the malignant progression of HCC cells. Conclusions: We identified a signature with great predictive efficacy and discovered a gene, HOMER1, that influences the malignant progression of HCC with the potential to become a novel therapeutic target.