HIGD1B, as a novel prognostic biomarker, is involved in regulating the tumor microenvironment and immune cell infiltration; its overexpression leads to poor prognosis in gastric cancer patients.
HIGD1B (HIG1 Hypoxia Inducible Domain Family Member 1B) is a protein-coding gene linked to the occurrence and progression of various illnesses. However, its precise function in gastric cancer (GC) remains unclear.
The expression of HIGD1B is determined through the TCGA and GEO databases and verified using experiments. The association between HIGD1B and GC patients' prognosis was analyzed via the Kaplan-Meier (K-M) curve. Subsequently, the researchers utilized ROC curves to assess the diagnostic capacity of HIGD1B and employed COX analysis to investigate risk factors for GC. The differentially expressed genes (DEGs) were then subjected to functional enrichment analysis, and a nomogram was generated to forecast the survival outcome and probability of GC patients. Additionally, we evaluated the interaction between HIGD1B and the immune cell infiltration and predicted the susceptibility of GC patients to therapy.
HIGD1B is markedly elevated in GC tissue and cell lines, and patients with high HIGD1B expression have a poorer outcome. In addition, HIGD1B is related to distinct grades, stages, and T stages. The survival ROC curves of HIGD1B and nomogram for five years were 0.741 and 0.735, suggesting appropriate levels of diagnostic efficacy. According to Cox regression analysis, HIGD1B represents a separate risk factor for the prognosis of gastric cancer (p<0.01). GSEA analysis demonstrated that the HIGD1B is closely related to cancer formation and advanced pathways. Moreover, patients with high HIGD1B expression exhibited a higher level of Tumor-infiltration immune cells (TIICs) and were more likely to experience immune escape and drug resistance after chemotherapy and immunotherapy.
This study explored the potential mechanisms and diagnostic and prognostic utility of HIGD1B in GC, as well as identified HIGD1B as a valuable biomarker and possible therapeutic target for GC.
Wang S
，Zhang S
，Li X
，Li X
，Zhao S
，Guo J
，Wang S
，Wang R
，Zhang M
，Qiu W
... -
《Frontiers in Immunology》
Downregulation of HIGD1B induces mitochondria-mediated apoptosis in gastric cancer cells by inactivating Akt and ERK pathways.
Gastric cancer (GC) is one of the most common malignancies worldwide. Hypoxia-inducible domain (HIGD) family members (e.g., HIGD1A) have been linked to tumor progression. However, the role of HIGD1B (another HIGD family member) in GC has yet to be fully understood. Based on data from TCGA_GC, GSE65801, and GSE65801 data sets, HIGD1B levels were evaluated in normal and GC tissues. Next, HIGD1B levels were validated by reverse transcription-quantitative PCR and western blot analysis analyses. Meanwhile, patients with GC in the TCGA_GC cohort were grouped into high- and low-HIGD1B level groups, and overall survival, functional enrichment, and immune infiltration were analyzed. Additionally, gain- and loss-of-function experiments were performed to determine the function of HIGD1B in GC cells. Compared to normal controls, HIGD1B mRNA levels were significantly elevated in GC tissues. Moreover, high HIGD1B levels may be an independent indicator of poor prognosis in patients with GC. Additionally, high HIGD1B levels were correlated with high stromal and ESTIMATE scores and elevated expression of immune checkpoints in patients with GC. Functional analyses showed that HIGD1B deficiency notably suppressed GC cell proliferation, migration, and invasion. Moreover, HIGD1B deficiency significantly induced mitochondria-mediated apoptosis in GC cells by inactivating Akt and ERK pathways. Collectively, HIGD1B may predict the prognosis of patients with GC and may function as an oncogene in GC. These findings suggest that HIGD1B may serve as a prognostic biomarker and potential therapeutic target in GC.
Chen X
，Sun B
，Li S
《-》
Identification of a novel 10 immune-related genes signature as a prognostic biomarker panel for gastric cancer.
Emerging evidence indicates that immune infiltrating cells in tumor microenvironment (TME) correlates with the development and progression of gastric cancer (GC). This study aimed to systematically investigate the immune-related genes (IRGs) to develop a prognostic signature to predict the overall survival (OS) in GC.
The gene expression profiles of training dataset (GSE62254), validation dataset I (GSE15459), and validation dataset II (GSE84437) were retrieved from GEO and TCGA databases. In the present study, we developed a 10 IRGs prognostic signature with the combination of weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator method (LASSO) COX model.
In the training dataset, the accuracy of the signature was 0.681, 0.741, and 0.72 in predicting 1, 3, and 5-year OS separately. The signature also had good performance in validation dataset Ⅰ with the accuracy of 0.57, 0.619, and 0.694, and in validation dataset Ⅱ with the accuracy of 0.559, 0.624, and 0.585. Then, we constructed a nomogram using the signature and clinical information which had strong discrimination ability with the c-index of 0.756. In the immune infiltration analysis, the signature was correlated with multiple immune infiltrating cells such as CD8 T cells, CD4 memory T cells, NK cells, and macrophages. Furthermore, several significant pathways were enriched in gene set enrichment analysis (GSEA) analysis, including TGF-beta signaling pathway and Wnt signaling pathway.
The signature of 10 IRGs we identified can effectively predict the prognosis of GC and provides new insight into discovering candidate prognostic biomarkers of GC.
Chen T
，Yang C
，Dou R
，Xiong B
... -
《Cancer Medicine》
Identification of cuproptosis-related subtypes, construction of a prognosis model, and tumor microenvironment landscape in gastric cancer.
Cuproptosis is a novel identified regulated cell death (RCD), which is correlated with the development, treatment response and prognosis of cancer. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of gastric cancer (GC) remains unknown.
Transcriptome profiling, somatic mutation, somatic copy number alteration and clinical data of GC samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database to describe the alterations of CRGs from genetic and transcriptional fields. Differential, survival and univariate cox regression analyses of CRGs were carried out to investigate the role of CRGs in GC. Cuproptosis molecular subtypes were identified by using consensus unsupervised clustering analysis based on the expression profiles of CRGs, and further analyzed by GO and KEGG gene set variation analyses (GSVA). Genes in distinct molecular subtypes were also analyzed by GO and KEGG gene enrichment analyses (GSEA). Differentially expressed genes (DEGs) were screened out from distinct molecular subtypes and further analyzed by GO enrichment analysis and univariate cox regression analysis. Consensus clustering analysis of prognostic DEGs was performed to identify genomic subtypes. Next, patients were randomly categorized into the training and testing group at a ratio of 1:1. CRG Risk scoring system was constructed through logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis, univariate and multivariate cox analyses in the training group and validated in the testing and combined groups. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to evaluate the expression of key Risk scoring genes. Sensitivity and specificity of Risk scoring system were examined by using receiver operating characteristic (ROC) curves. pRRophetic package in R was used to investigate the therapeutic effects of drugs in high- and low- risk score group. Finally, the nomogram scoring system was developed to predict patients' survival through incorporating the clinicopathological features and CRG Risk score.
Most CRGs were up-regulated in tumor tissues and showed a relatively high mutation frequency. Survival and univariate cox regression analysis revealed that LIAS and FDX1 were significantly associated with GC patients' survival. After consensus unsupervised clustering analysis, GC patients were classified into two cuproptosis molecular subtypes, which were significantly associated with clinical features (gender, age, grade and TNM stage), prognosis, metabolic related pathways and immune cell infiltration in TME of GC. GO enrichment analyses of 84 DEGs, obtained from distinct molecular subtypes, revealed that DEGs primarily enriched in the regulation of metabolism and intracellular/extracellular structure in GC. Univariate cox regression analysis of 84 DEGs further screened out 32 prognostic DEGs. According to the expression profiles of 32 prognostic DEGs, patients were re-classified into two gene subtypes, which were significantly associated with patients' age, grade, T and N stage, and survival of patients. Nest, the Risk score system was constructed with moderate sensitivity and specificity. A high CRG Risk score, characterized by decreased microsatellite instability-high (MSI-H), tumor mutation burden (TMB) and cancer stem cell (CSC) index, and high stromal and immune score in TME, indicated poor survival. Four of five key Risk scoring genes expression were dysregulated in tumor compared with normal samples. Moreover, CRG Risk score was greatly related with sensitivity of multiple drugs. Finally, we established a highly accurate nomogram for promoting the clinical applicability of the CRG Risk scoring system.
Our comprehensive analysis of CRGs in GC demonstrated their potential roles in TME, clinicopathological features, and prognosis. These findings may improve our understanding of CRGs in GC and provide new perceptions for doctors to predict prognosis and develop more effective and personalized therapy strategies.
Wang J
，Qin D
，Tao Z
，Wang B
，Xie Y
，Wang Y
，Li B
，Cao J
，Qiao X
，Zhong S
，Hu X
... -
《Frontiers in Immunology》
ResearchGate
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