GNA15 expression in small intestinal neuroendocrine neoplasia: Functional and signalling pathway analyses

A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer

PURPOSE

This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis.

METHODS

SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses.

RESULTS

Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets.

CONCLUSION

GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.

Exploring the Role of G Protein Expression in Sodium Butyrate-Enhanced Pancreas Development of Dairy Calves: A Proteomic Perspective

The present study evaluated the effects of sodium butyrate (SB) supplementation on exocrine and endocrine pancreatic development in dairy calves. Fourteen male Holstein calves were alimented with either milk or milk supplemented with SB for 70 days. Pancreases were collected for analysis including staining, immunofluorescence, electron microscopy, qRT-PCR, Western blotting, and proteomics. Results indicated increased development in the SB group with increases in organ size, protein levels, and cell growth. There were also exocrine enhancements manifested as higher enzyme activities and gene expressions along with larger zymogen granules. Endocrine benefits included elevated gene expression, more insulin secretion, and larger islets, indicating a rise in β-cell proliferation. Proteomics and pathway analyses pinpointed the G protein subunit alpha-15 as a pivotal factor in pancreatic and insulin secretion pathways. Overall, SB supplementation enhances pancreatic development by promoting its exocrine and endocrine functions through G protein regulation in dairy calves.

The inflammatory response-related robust machine learning signature in endometrial cancer: Based on multi-cohort studies

Uterine corpus endometrial carcinoma (UCEC) is a prevalent form of cancer in women, affecting the inner lining of the uterus. Inflammation plays a crucial role in the progression and prognosis of cancer, making it important to identify inflammatory response-related subtypes in UCEC for targeted therapy and personalized medicine. This study discovered significant variation in immune response within UCEC tumors based on molecular subtypes of inflammatory response-related genes. Subtype A showed a more favorable prognosis and better response to immunotherapies like anti-CTLA4 and anti-PDCD1 therapy. Functional analysis revealed subtype-specific differences in immune response, with subtype A exhibiting higher expression of genes related to cytokine signaling pathways, NK cell-mediated cytotoxicity pathways and inflammatory processes. Subtype A also showed increased sensitivity to three chemotherapeutic agents. A 12-gene inflammatory response-related signature was found to have prognostic value for 1, 2 and 3 year survival in UCEC patients. Additionally, a validated machine learning-based signature demonstrated significant differences in clinical traits between low-risk and high-risk cohorts. Elevated risk scores were associated with higher pathological grading, older age, advanced stage and immune subtype C2. Low-risk groups had higher infiltration of immune cell types such as CD8 + T cells and activated CD4 + cells. However, the abundance of cytotoxic immune cells decreased with increasing risk scores. Finally, PCR was applied to test the different expression in P2PX4. P2RX4 knockdown inhibited the proliferation and proliferation of the endometrial carcinoma Ishikawa cell line. In conclusion, this developed signature can serve as a clinical prediction index and reveal distinct immune expression patterns. Ultimately, this study has the potential to enhance targeted therapy and personalized medicine for UCEC patients.

Tumor-Promoting Role of GNA14 in Colon Cancer Development

Recent studies have shown that mutations in members of the G-protein α family contribute to the onset and progression of cancer. However, the role of GNA14 in CRC remains unknown. In this study, we examined the effect of GNA14 on CRC through genetic approaches in vitro and in vivo. We found that GNA14 knockdown by small interfering RNA (siRNA) inhibited the proliferation of CRC cells SW403 and HT29. Gna14 knockout mice developed normally without obvious abnormalities. However, the number of polyps in the small intestine was significantly reduced in Gna14 knockout mice compared to control mice after mating with ApcMin mice, a representative CRC mouse model. In particular, deletion of the Gna14 inhibited polyp growth, especially in the distal end of the small intestine. Histological examination showed that Gna14 knockout mice suppressed malignant tumor progression due to decreased proliferation and increased apoptosis in polyps compared to controls. In addition, GNA14 knockdown in CRC cells resulted in downregulation of ERK phosphorylation and β-catenin and β-catenin phosphorylation at S675. Similarly, ERK phosphorylation and phospho-β-catenin phosphorylation at S675 were decreased in polyps of Gna14 knockout mice. Collectively, these analyses show that GNA14 may accelerate CRC cell proliferation and malignant tumor progression through ERK and β-catenin pathways.

GATA3 mediates doxorubicin resistance by inhibiting CYB5R2-catalyzed iron reduction in breast cancer cells

AIMS

Neoadjuvant chemotherapy (NAC) is the primary preoperative therapy for breast cancer. The luminal subtype of breast cancer shows less NAC response than the basal subtype, with an inefficient NAC treatment effect. Understanding of the molecular and cellular mechanisms responsible for this chemoresistance is an important issue when determining optimal treatment.

METHODS

Doxorubicin-induced apoptosis and ferroptosis was investigated using cytotoxicity, western blotting, and flow cytometry assays. The role of GATA3 in modulating doxorubicin-induced cell death was investigated both in vitro and in vivo. RNA-seq, qPCR, ChIP, and luciferase assay and association analyses were performed to investigate the regulation of CYB5R2 by GATA3. The function of GATA3 and CYB5R2 in regulating doxorubicin-induced ferroptosis was evaluated with iron, ROS, and lipid peroxidation detection assays. Immunohistochemistry was performed for results validation.

RESULTS

Doxorubicin-induced basal breast cancer cell death is dependent on iron-mediated ferroptosis. Overexpression of the luminal signature transcriptional factor GATA3 mediates doxorubicin resistance. GATA3 promotes cell viability by decreasing ferroptosis-related gene CYB5R2 expression and by maintaining iron homeostasis. Analyzing data from the public and our cohorts demonstrates that GATA3 and CYB5R2 are associated with NAC response.

CONCLUSIONS

GATA3 promotes doxorubicin resistance by inhibiting CYB5R2-mediated iron metabolism and ferroptosis. Therefore, patients with breast cancer who display high GATA3 expression do not benefit from doxorubicin-based NAC regimens.

Histaminergic System and Inflammation-Related Genes in Normal Large Intestine and Adenocarcinoma Tissues: Transcriptional Profiles and Relations

Transcriptional analyses such as microarray data have contributed to the progress in the diagnostics and therapy of colorectal cancer (CRC). The need for such research is still present because of the disease being common in both men and women with a high second position in cancer rankings. Little is known about the relations between the histaminergic system and inflammation in the large intestine and CRC. Therefore, the aim of this study was to evaluate the expression of genes related to the histaminergic system and inflammation in the CRC tissues at three cancer development designs: all tested CRC samples, low (LCS) and high (HCS) clinical stage, and four clinical stages (CSI-CSIV), to the control. The research was carried out at the transcriptomic level, analysing hundreds of mRNAs from microarrays, as well as carrying out RT-PCR analysis of histaminergic receptors. The following histaminergic mRNAs: GNA15, MAOA, WASF2A, and inflammation-related: AEBP1, CXCL1, CXCL2, CXCL3, CXCL8, SPHK1, TNFAIP6, were distinguished. Among all analysed transcripts, AEBP1 can be considered the most promising diagnostic marker in the early stage of CRC. The results showed 59 correlations between differentiating genes of the histaminergic system and inflammation in the control, control and CRC, and CRC. The tests confirmed the presence of all histamine receptor transcripts in both the control and colorectal adenocarcinoma. Significant differences in expression were stated for HRH2 and HRH3 in the advanced stages of CRC adenocarcinoma. The relations between the histaminergic system and inflammation-linked genes in both the control and the CRC have been observed.

Identification of a Diagnosis and Therapeutic Inflammatory Response-Related Gene Signature Associated with Esophageal Adenocarcinoma

The purpose of this study is to identify the key regulatory genes related to the inflammatory response of esophageal adenocarcinoma (EAC) and to find new diagnosis and therapeutic options. We downloaded the dataset GSE72874 from the Gene Expression Omnibus database for this study. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to find common inflammatory response-related genes (IRRGs) in EAC. The relationship between normal and tumor immune infiltration was analyzed using an online database of CIBERSORTx. Finally, 920 DEGs were identified, of which 5 genes were key IRRGs associated with EAC, including three down-regulated genes GNA15, MXD1, and NOD2, and two down-regulated genes PLAUR and TIMP1. Further research found that GNA15, MXD1, and NOD2 were down-regulated, PLAUR and TIMP1 were up-regulated in Barrett's esophagus (BE). In addition, we found that the expression of GNA15 and MXD1 in normal esophageal squamous epithelial cells decreased after ethanol treatment, while the expression of PLAUR and TIMP1 increased after ethanol treatment. Compared with normal esophageal tissue, immune cells infiltrated such as plasma cells, macrophages M0, macrophages M1, macrophages M2, dendritic cells activated, and mast cells activated were significantly increased in EAC, while immune cells infiltrated such as T cells CD4 memory resting, T cells follicular helper, NK cells resting, and dendritic cells resting were significantly reduced. The receiver operating characteristic curve indicated that GNA15, MXD1, NOD2, PLAUR and TIMP1 expression had a performed well in diagnosing EAC from healthy control. GNA15, MXD1, NOD2, PLAUR and TIMP1 were identified and validated as novel potential biomarkers for early diagnosis and may be new molecular targets for treatment of EAC.

Downregulation of GNA15 Inhibits Cell Proliferation via P38 MAPK Pathway and Correlates with Prognosis of Adult Acute Myeloid Leukemia With Normal Karyotype

Background

The prognosis of acute myeloid leukemia (AML) with a normal karyotype is highly heterogonous, and the current risk stratification is still insufficient to differentiate patients from high-risk to standard-risk. Changes in some genetic profiles may contribute to the poor prognosis of AML. Although the prognostic value of G protein subunit alpha 15 (GNA15) in AML has been reported based on the GEO (Gene Expression Omnibus) database, the prognostic significance of GNA15 has not been verified in clinical samples. The biological functions of GNA15 in AML development remain open to investigation. This study explored the clinical significance, biological effects and molecular mechanism of GNA15 in AML.

Methods

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the mRNA expression level of GNA15 in blasts of bone marrow specimens from 154 newly diagnosed adult AML patients and 26 healthy volunteers. AML cell lines, Kasumi-1 and SKNO-1, were used for lentiviral transfection. Cell Counting Kit-8 (CCK8) and colony formation assays were used to determine cell proliferation. Cell cycle and apoptosis were analyzed by flow cytometry. The relevant signaling pathways were evaluated by Western blot. The Log-Rank test and Kaplan-Meier were used to evaluate survival rate, and the Cox regression model was used to analyze multivariate analysis. Xenograft tumor mouse model was used for in vivo experiments.

Results

The expression of GNA15 in adult AML was significantly higher than that in healthy individuals. Subjects with high GNA15 expression showed lower overall survival and relapse-free survival in adult AML with normal karyotype. High GNA15 expression was independently correlated with a worse prognosis in multivariate analysis. Knockdown of GNA15 inhibited cell proliferation and cell cycle progression, and induced cell apoptosis in AML cells. GNA15-knockdown induced down-regulation of p-P38 MAPK and its downstream p-MAPKAPK2 and p-CREB. Rescue assays confirmed that P38 MAPK signaling pathway was involved in the inhibition of proliferation mediated by GNA15 knockdown.

Conclusions

In summary, GNA15 was highly expressed in adult AML, and high GNA15 expression was independently correlated with a worse prognosis in adult AML with normal karyotype. Knockdown of GNA15 inhibited the proliferation of AML regulated by the P38 MAPK signaling pathway. Therefore, GNA15 may serve as a potential prognostic marker and a therapeutic target for AML in the future.

The Rac2 GTPase contributes to cathepsin H-mediated protection against cytokine-induced apoptosis in insulin-secreting cells

The type 1 diabetes (T1D) risk locus on chromosome 15q25.1 harbors the candidate gene CTSH (cathepsin H). We previously demonstrated that CTSH regulates β-cell function in vitro and in vivo. CTSH overexpression protected insulin-secreting INS-1 cells against cytokine-induced apoptosis. The purpose of the present study was to identify the genes through which CTSH mediates its protective effects. Microarray analysis identified 63 annotated genes differentially expressed between CTSH-overexpressing INS-1 cells and control cells treated with interleukin-1β and interferon-γ for up to 16h. Permutation test identified 10 significant genes across all time-points: Elmod1, Fam49a, Gas7, Gna15, Msrb3, Nox1, Ptgs1, Rac2, Scn7a and Ttn. Pathway analysis identified the "Inflammation mediated by chemokine and cytokine signaling pathway" with Gna15, Ptgs1 and Rac2 as significant. Knockdown of Rac2 abolished the protective effect of CTSH overexpression on cytokine-induced apoptosis, suggesting that the small GTPase and T1D candidate gene Rac2 contributes to the anti-apoptotic effect of CTSH.

Bioinformatic evidences and analysis of putative biomarkers in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. Aberrant expression of genes plays important role in the procession of PDAC. The analysis of gene expression profile will contribute to the research of carcinoma mechanism.

OBJECTIVE

This present study is focused to investigate the differentially expressed genes (DEGs) from 3 PDAC microarray datasets, which would provide candidate genes for putative biomarkers to understand the mechanism of PDAC and potential targets of treatment.

METHOD

Based on the overlap genes obtained from 3 GEO datasets, the hub genes were identified using STRING and Cytoscape plugin MCODE. The enrichment and function analysis were applied using DAVID. The protein-protein interaction network was performed using cBioPortal and UCSC Xena. The Oncomine was finally used to determine the candidate gene by analyzing their expression between pancreas sample and PDAC sample.

RESULTS

25 hub genes were selected from a total of 1006 DEGs from 3 GEO datasets, consisting of 14 upregulated genes and 11 downregulated genes. The overall decline of hub gene expression enriched in G1 phase of cell cycle in other subtypes of pancreatic cancer. Oncomine database was ultimately performed to determine the 8 candidate genes, including CXCL5, CCL20, NMU, F2R, ANXA1, EDNRA, LPAR6, and GNA15.

CONCLUSIONS

Conclusively, 8 candidate genes would become the potential PDAC combined biomarkers for diagnosis and therapeutic strategies.

GNA14 silencing suppresses the proliferation of endometrial carcinoma cells through inducing apoptosis and G2/M cell cycle arrest

Endometrial carcinoma is the most common gynecological malignancy. The pathological factors triggering this disease are largely unknown. Although the role of guanine nucleotide-binding protein subunit α (GNA) 11 (GNA11) in melanoma has been described, the involvement of GNA14 in endometrial carcinoma remains to be determined. Here, we found that GNA14 expression was increased in endometrial carcinoma tissues compared with simple hyperplasia tissues. Based on lentivirus-mediated knockdown assay, we showed that GNA14 silencing significantly suppressed the proliferation of both HEC-1-A and Ishikawa cells. The caspase 3/caspase 7 activity and apoptosis were enhanced by GNA14 knockdown. GNA14 depletion led to cell cycle arrest at the G₂/M phase. In addition, Apoptosis Array analysis revealed that caspase-3 and Fas were up-regulated by GNA14 knockdown. Our study suggests that GNA14 silencing blunts endometrial carcinoma cell proliferation. Targetting GNA14 may bring help for the patients of endometrial carcinoma.

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.