Identification of a five-gene signature of the RGS gene family with prognostic value in ovarian cancer

Exploring the oncogenic role of RGS19 in bladder cancer progression and prognosis

This study investigates the role of autophagy-related genes (ARGs) in bladder cancer (BLCA), focusing on the regulator of G protein signaling 19 (RGS19). Using data from The Cancer Genome Atlas (TCGA) and the Human Autophagy Database (HADb), we identified RGS19 as significantly upregulated and linked to poor prognosis in BLCA. Kaplan-Meier survival analysis confirmed its association with increased mortality and. In vitro, RGS19 knockdown in BLCA cell lines inhibited proliferation, migration, and invasion, while inducing apoptosis and autophagy. Transmission electron microscopy showed autophagic structures in RGS19-silenced cells. In vivo, a xenograft mouse model demonstrated reduced tumor growth with RGS19 knockdown. Immunohistochemical (IHC) analysis revealed decreased Ki67 and increased autophagy markers in tumors with reduced RGS19. Pathway analysis suggested RGS19 acts through the cGMP-PKG signaling pathway, validated by altered expression of soluble guanylate cyclase (sGC), protein kinase G (PKG1), phosphodiesterase 5 A (PDE5A), vasodilator-stimulated phosphoprotein (VASP), and phosphorylated VASP (p-VASP) upon RGS19 knockdown. These results highlight RGS19 as a potential biomarker and therapeutic target in BLCA.

RGS10 Deficiency Alleviated Intestinal Mucosal Inflammation Through Suppression of Th1/Th17 Cell Immune Responses in Ulcerative Colitis

Regulator of G-protein signalling (RGS) 10 plays critical roles in several immune related diseases. However, whether RGS10 is involved in colonic inflammation of ulcerative colitis (UC) is still obscure. This study aimed to investigate the role of RGS10 in UC. In this study, RGS10 expression was examined by quantitative real-time polymerase chain reaction (qRT-PCR), western blotting, immunohistochemistry, and immunofluorescent analysis. Single-cell RNA sequencing of intestinal mucosa was performed to identify key immune cells with differentially expressed RGS10. RGS10 knockout mice were generated and established dextran sulphate sodium (DSS)-induced colitis. Expression of inflammatory cytokines on mRNA and protein levels was detected by qRT-PCR, enzyme-linked immunosorbent assay, and flow cytometry. We found that RGS10 expression was significantly elevated in UC patients, especially in CD4+ T cells, compared with healthy subjects. Intriguingly, RGS10 deficiency markedly alleviated DSS-induced colitis and decreased the proportion of Th1 and Th17 cells in lamina propria mononuclear cells (LPMCs), peripheral blood (PB), spleens, and mesenteric lymph nodes (mLNs). Mechanistically, RGS10 deficiency blocked the differentiation of Th1 and Th17 cells by inhibiting the phosphorylation of signal transducer and activator of transcription (STAT) 1 and STAT3. The co-immunoprecipitation analysis further showed that RGS10 could interact with protein tyrosine phosphatase non-receptor type 2 (PTPN2), and further regulated Th1 and Th17 cells differentiation of CD4+ T cells. In conclusion, RGS10 deficiency alleviated intestinal mucosal inflammation through inhibition of Th1/Th17 cell-mediated immune responses via interaction with PTPN2 in CD4+ T cells. Therefore, targeting RGS10 may represent a novel therapeutic approach for UC treatment.

RGS10 deficiency facilitates distant metastasis by inducing epithelial-mesenchymal transition in breast cancer

Distant metastasis is the major cause of death in patients with breast cancer. Epithelial-mesenchymal transition (EMT) contributes to breast cancer metastasis. Regulator of G protein-signaling (RGS) proteins modulates metastasis in various cancers. This study identified a novel role for RGS10 in EMT and metastasis in breast cancer. RGS10 protein levels were significantly lower in breast cancer tissues compared to normal breast tissues, and deficiency in RGS10 protein predicted a worse prognosis in patients with breast cancer. RGS10 protein levels were lower in the highly aggressive cell line MDA-MB-231 than in the poorly aggressive, less invasive cell lines MCF7 and SKBR3. Silencing RGS10 in SKBR3 cells enhanced EMT and caused SKBR3 cell migration and invasion. The ability of RGS10 to suppress EMT and metastasis in breast cancer was dependent on lipocalin-2 and MIR539-5p. These findings identify RGS10 as a tumor suppressor, prognostic biomarker, and potential therapeutic target for breast cancer.

Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis

BACKGROUND

Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.

METHOD

We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.

RESULTS

Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.

CONCLUSIONS

Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.

Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq

Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.

Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach

Background

DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.

Method

Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.

Results

A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC.

Conclusions

This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

RGS19 activates the MYH9/β-catenin/c-Myc positive feedback loop in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common fatal cancers worldwide, and the identification of novel treatment targets and prognostic biomarkers is urgently needed because of its unsatisfactory prognosis. Regulator of G-protein signaling 19 (RGS19) is a multifunctional protein that regulates the progression of various cancers. However, the specific function of RGS19 in HCC remains unclear. The expression of RGS19 was determined in clinical HCC samples. Functional and molecular biology experiments involving RGS19 were performed to explore the potential mechanisms of RGS19 in HCC. The results showed that the expression of RGS19 is upregulated in HCC tissues and is significantly associated with poor prognosis in HCC patients. RGS19 promotes the proliferation and metastasis of HCC cells in vitro and in vivo. Mechanistically, RGS19, via its RGS domain, stabilizes the MYH9 protein by directly inhibiting the interaction of MYH9 with STUB1, which has been identified as an E3 ligase of MYH9. Moreover, RGS19 activates β-catenin/c-Myc signaling via MYH9, and RGS19 is also a transcriptional target gene of c-Myc. A positive feedback loop formed by RGS19, MYH9, and the β-catenin/c-Myc axis was found in HCC. In conclusion, our research revealed that competition between RGS19 and STUB1 is a critical mechanism of MYH9 regulation and that the RGS19/MYH9/β-catenin/c-Myc feedback loop may represent a promising strategy for HCC therapy.

Identification of a risk score model based on tertiary lymphoid structure-related genes for predicting immunotherapy efficacy in non-small cell lung cancer

BACKGROUND

Tertiary lymphoid structures (TLSs) affect the prognosis and efficacy of immunotherapy in patients with non-small cell lung cancer (NSCLC), but the underlying mechanisms are not well understood.

METHODS

TLSs were identified and categorized online from the Cancer Digital Slide Archive (CDSA). Overall survival (OS) and disease-free survival (DFS) were analyzed. GSE111414 and GSE136961 datasets were downloaded from the GEO database. GSVA, GO and KEGG were used to explore the signaling pathways. Immune cell infiltration was analyzed by xCell, ssGSEA and MCP-counter. The analysis of WGCNA, Lasso and multivariate cox regression were conducted to develop a gene risk score model based on the SU2C-MARK cohort.

RESULTS

TLS-positive was a protective factor for OS according to multivariate cox regression analysis (p = 0.029). Both the TLS-positive and TLS-mature groups exhibited genes enrichment in immune activation pathways. The TLS-mature group showed more activated dendritic cell infiltration than the TLS-immature group. We screened TLS-related genes using WGCNA. Lasso and multivariate cox regression analysis were used to construct a five-genes (RGS8, RUF4, HLA-DQB2, THEMIS, and TRBV12-5) risk score model, the progression free survival (PFS) and OS of patients in the low-risk group were markedly superior to those in the high-risk group (p < 0.0001; p = 0.0015, respectively). Calibration and ROC curves indicated that the combined model with gene risk score and clinical features could predict the PFS of patients who have received immunotherapy more accurately than a single clinical factor.

CONCLUSIONS

Our data suggested a pivotal role of TLSs formation in survival outcome and immunotherapy response of NSCLC patients. Tumors with mature TLS formation showed more activated immune microenvironment. In addition, the model constructed by TLS-related genes could predict the response to immunotherapy and is meaningful for clinical decision-making.

Novel prognostic marker TGFBI affects the migration and invasion function of ovarian cancer cells and activates the integrin αvβ3-PI3K-Akt signaling pathway

BACKGROUND

Individual patients with ovarian cancer show remarkably different prognosis. Present prognostic models for ovarian cancer mainly focus on clinico-pathological parameters, so quantifiable prognostic markers at molecular level are urgently needed. Platelets contribute to ovarian cancer progression, but have not been considered as biomarkers likely due to their instability. Here, we aimed to search for a stable prognostic marker from platelet-treated ovarian cancer cells, and explore its functions and mechanisms.

METHODS

Microarrays analysis was done with platelet-treated SKOV-3 ovarian cancer cells. Relevant studies were searched in the Gene Expression Omnibus (GEO) database. The candidate genes were determined by differentially expressed genes (DEGs), Venn diagram drawing, protein-protein interaction (PPI) network, Cox proportional hazards model and Kaplan-Meier analysis. The expression of TGFBI in clinical samples was assessed by immunehistochemical staining (IHC), and the association of TGFBI levels with the clinic-pathological characteristics and prognosis in ovarian cancer patients was evaluated by univariate and multivariate analysis. The functions of TGFBI were predicted using data from TCGA, and validated by in vitro and in vivo experiments. The mechanism exploration was performed based on proteomic analysis, molecular docking and intervention study.

RESULTS

TGFBI was significantly higher expressed in the platelet-treated ovarian cancer cells. An analysis of bioinformatics data revealed that increased expression of TGFBI led to significant decrease of overall survival (OS), progression-free survival (PFS) and post-progression survival (PPS) in ovarian cancer patients. Tissue microarray results showed that TGFBI was an independent factor for ovarian cancer, and TGFBI expression predict poor prognosis. Functionally, TGFBI affected the migration and invasion of ovarian cancer cells by regulation of epithelial mesenchymal transition (EMT) markers (CDH1 and CDH2) and extracellular matrix (ECM) degradation proteins (MMP-2). Mechanistically, TGFBI phosphorylated PI3K and Akt by combining integrin αvβ3.

CONCLUSIONS

We found out TGFBI as a novel prognostic indicator for ovarian cancer patients. TGFBI could promote metastasis in ovarian cancer by EMT induction and ECM remodeling, which might be associated with the activation of integrin αvβ3-PI3K-Akt signaling pathway.

Status and trends of RGS16 based on data visualization analysis: A review

G-protein signaling regulator 16 (RGS16) has been confirmed that RGS16 is associated with cancer, neurodegenerative diseases, and cardiovascular diseases. Moreover, many studies have shown that RGS16 can be used as a biomarker for cancer diagnosis and prognosis. We used CiteSpace and VOS viewer software to perform a bibliometric analysis of 290 publications in the core collection of Web of Science. All the articles come from 399 institutions, including 618 authors, 179 journals, 40 countries, 115 keywords, 1 language, two types of papers, and reviews. The United States has the largest number of publications. The Research Center of Allergy and Infectious Diseases (NIAID) publishes the most papers, Emory University is the most recent of all institutions with the most recent results in the RGS16 study. Cell biology is the most studied discipline, and the most studied topic is migration. Drury published RGS16-related articles with the most citations (n = 15), and Berman published articles with the most citations (n = 106). The biological applications of RGS16 are currently a hot area of RGS16 research, including inflammation, cancer, ulcerative colitis, metabolic acidosis, platelet activation, and thrombosis. The current scientometrics study provides an overview of RGS16 research from 1995 to 2022. This study provides an overview of current and potential future research hotspots in the field of RGS16 and can be used as a resource for interested researchers.

Identification of TFR2 as a novel ferroptosis‑related gene that serves an important role in prognosis and progression of triple‑negative breast cancer

Effective targeted therapeutic strategies for triple-negative breast cancer (TNBC), the most malignant subtype of breast cancer, are currently lacking. Ferroptosis has been reported to be associated with the onset and advancement of various cancer types, including TNBC. However, there are limited studies on the correlation between TNBC and ferroptosis-related genes. In addition, the potential biomarkers of ferroptosis in TNBC need further investigation. The present study aimed to assess the prognostic role of a novel ferroptosis-related gene signature in the context of TNBC. The signature was established utilizing The Cancer Genome Atlas dataset. This three-gene model [transferrin receptor 2 (TFR2), regulator of G protein signaling 4 and zinc finger protein 36] was developed utilizing least absolute shrinkage and selection operator regression analysis and demonstrated satisfactory predictive performance in TNBC. The area under the curve values of the receiver operating characteristic curves in this model concerning the 1-, 2- and 3-year survival prediction were 0.721, 0.840 and 0.856, respectively. The predictive performance of the model was verified using the TNBC dataset GSE25307. Gene set enrichment analysis (GSEA) demonstrated the enrichment of genes in the low-risk group in a number of important metabolic pathways. Single-sample GSEA demonstrated a variation in the expression levels of immune checkpoint molecules between the high- and low-risk groups. The inhibitory impact of TFR2 knockdown on the proliferative capacity of TNBC cells was verified through in vitro experiments. The data also demonstrated that TFR2 knockdown facilitated the ferroptosis of TNBC cells. Additional assessments indicated that the effects of TFR2 knockdown were partially reversed upon treatment with the ferroptosis inhibitor ferrostatin-1. In conclusion, in the present study, a novel and accurate ferroptosis-related predictive signature was established for TNBC with potential future clinical applications. To the best of our knowledge, the present study is the first to report that TFR2 regulated ferroptosis in TNBC cells in vitro.

Exploring the tumor micro-environment in primary and metastatic tumors of different ovarian cancer histotypes

Ovarian cancer is a highly heterogeneous disease consisting of at least five different histological subtypes with varying clinical features, cells of origin, molecular composition, risk factors, and treatments. While most single-cell studies have focused on High grade serous ovarian cancer, a comprehensive landscape of the constituent cell types and their interactions within the tumor microenvironment are yet to be established in the different ovarian cancer histotypes. Further characterization of tumor progression, metastasis, and various histotypes are also needed to connect molecular signatures to pathological grading for personalized diagnosis and tailored treatment. In this study, we leveraged high-resolution single-cell RNA sequencing technology to elucidate the cellular compositions on 21 solid tumor samples collected from 12 patients with six ovarian cancer histotypes and both primary (ovaries) and metastatic (omentum, rectum) sites. The diverse collection allowed us to deconstruct the histotypes and tumor site-specific expression patterns of cells in the tumor, and identify key marker genes and ligand-receptor pairs that are active in the ovarian tumor microenvironment. Our findings can be used in improving precision disease stratification and optimizing treatment options.

Blood regulator of G protein signalling 1 as a potential prognostic biomarker in surgical nonsmall cell lung cancer patients: Correlation with clinical features and survival

INTRODUCTION

Regulator of G protein signalling 1 (RGS1) closely regulates malignant phenotypes and tumour immunity in several cancers, while its clinical value in nonsmall cell lung cancer (NSCLC) is by far rarely reported. Consequently, this study aimed to explore the linkage of blood RGS1 with clinical features and prognosis in surgical NSCLC patients.

METHODS

Two-hundred and ten surgical NSCLC patients were consecutively enrolled in this study, whose RGS1 in peripheral blood mononuclear cells was determined before treatment via reverse transcriptional-quantitative polymerase chain reaction. Additionally, the blood RGS1 was also collected from 30 healthy controls (HCs).

RESULTS

Blood RGS1 was increased in NSCLC patients compared with HCs (P < 0.001). Elevated blood RGS1 was related to lymph node (LYN) metastasis (P = 0.001), higher tumour-nodes-metastasis (TNM) stage (P = 0.004), neoadjuvant chemotherapy administration (P = 0.044), shortened accumulative disease-free survival (DFS) (P = 0.008) and overall survival (OS) (P = 0.013) in NSCLC patients. A multivariate Cox's regression analysis showed that blood RGS1 high expression could independently reflect shortened DFS (hazard ratio = 1.499, P = 0.023), whereas it could not independently predict OS (P > 0.050). Furthermore, blood RGS1 high expression was associated with shortened OS (P = 0.020) in patients with neoadjuvant therapy and with worse DFS (P = 0.028) and OS (P = 0.026) in patients with adjuvant therapy, while blood RGS1 was not linked with DFS or OS in patients without neoadjuvant or adjuvant therapy (all P > 0.050).

CONCLUSION

Elevated blood RGS1 correlates with LYN metastasis, neoadjuvant chemotherapy administration, worse DFS and OS, which might serve as a useful prognostic biomarker for surgical NSCLC patients.

Identification of a cancer associated fibroblasts-related index to predict prognosis and immune landscape in ovarian cancer

Cancer-associated fibroblasts (CAFs) play a role in ovarian cancer (OV) evolution, immunosuppression and promotion of drug resistance. Exploring the value of CAFs-related biomarker in OV is of great importance. In the present work, we developed a CAFs-related index (CAFRI) based on an integrated analysis of single-cell and bulk RNA-sequencing and highlighted the value of CAFRI in predicting clinical outcomes in individuals with OV, tumour immune microenvironment (TIME) and response to immune checkpoint inhibitors (ICIs). The GSE151214 cohort was used for cell subpopulation localization and analysis, the TCGA-OV patients as a training set. Moreover, the ICGC-OV, GSE26193, GSE26712 and GSE19829 cohorts were used for the validation of CAFRI. The TIMER 2.0, CIBERSORT and ssGSEA algorithms were used for analysis of TIME characteristics based on the CAFRI. The GSVA, GSEA, GO, KEGG and tumour mutation burden (TMB) analyses were used for mechanistic exploration. Additionally, the IMvigor210 cohort was conducted to validate the predictive value of CAFRI on the efficacy of ICIs. Finally, CAFRI-based antitumour drug sensitivity was analysed. The findings demonstrate that the CAFRI can served as an excellent predictor of prognosis for individuals with OV, as well as identifying patients with different TIME characteristics, differentiating between immune 'hot' and 'cold' tumour populations, and providing new insights into the selection of ICIs and personalised treatment regimens. CAFRI provides new perspectives for the development of novel prognostic and immunotherapy efficacy predictive biomarkers for OV.

Tumor-intrinsic RGS1 potentiates checkpoint blockade response via ATF3-IFNGR1 axis

Background

Non-responsiveness is a major barrier in current cancer immune checkpoint blockade therapies, and the mechanism has not been elucidated yet. Therefore, it is necessary to discover the mechanism and biomarkers of tumor immunotherapeutic resistance.

Methods

Bioinformatics analysis was performed based on CD8+ T cell infiltration in multiple tumor databases to screen out genes related to anti-tumor immunity. Associations between Regulator of G-protein signaling 1 (RGS1) and IFNγ-STAT1 signaling, and MHCI antigen presentation pathway were examined by RT-qPCR, western blotting, and flow cytometry. The modulatory mechanisms of RGS1 were investigated via CHIP-qPCR and dual-luciferase assay. The clinical and therapeutic implications of RGS1 were comprehensively investigated using tumor cell lines, mouse models, and clinical samples receiving immunotherapy.

Results

RGS1 was identified as the highest gene positively correlated with immunogenicity among RGS family. Inhibition of RGS1 in neoplastic cells dampened anti-tumor immune response and elicited resistance to immunotherapy in both renal and lung murine subcutaneous tumors. Mechanistically, RGS1 enhanced the binding of activating transcription factor 3 (ATF3) to the promoter of interferon gamma receptor 1 (IFNGR1), activated STAT1 and the subsequent expression of IFNγ-inducible genes, especially CXCL9 and MHC class I (MHCI), thereby influenced CD8+ T cell infiltration and antigen presentation and processing. Clinically, lower expression level of RGS1 was associated with resistance of PD1 inhibition therapy and shortened progression-free survival among 21 NSCLC patients receiving immunotherapy.

Conclusions

Together, these findings uncover a novel mechanism that elicits immunotherapy resistance and highlight the function of tumor-intrinsic RGS1, which brings new insights for future strategies to sensitize anti-PD1 immunotherapy.

Function and regulation of RGS family members in solid tumours: a comprehensive review

G protein-coupled receptors (GPCRs) play a key role in regulating the homeostasis of the internal environment and are closely associated with tumour progression as major mediators of cellular signalling. As a diverse and multifunctional group of proteins, the G protein signalling regulator (RGS) family was proven to be involved in the cellular transduction of GPCRs. Growing evidence has revealed dysregulation of RGS proteins as a common phenomenon and highlighted the key roles of these proteins in human cancers. Furthermore, their differential expression may be a potential biomarker for tumour diagnosis, treatment and prognosis. Most importantly, there are few systematic reviews on the functional/mechanistic characteristics and clinical application of RGS family members at present. In this review, we focus on the G-protein signalling regulator (RGS) family, which includes more than 20 family members. We analysed the classification, basic structure, and major functions of the RGS family members. Moreover, we summarize the expression changes of each RGS family member in various human cancers and their important roles in regulating cancer cell proliferation, stem cell maintenance, tumorigenesis and cancer metastasis. On this basis, we outline the molecular signalling pathways in which some RGS family members are involved in tumour progression. Finally, their potential application in the precise diagnosis, prognosis and treatment of different types of cancers and the main possible problems for clinical application at present are discussed. Our review provides a comprehensive understanding of the role and potential mechanisms of RGS in regulating tumour progression. Video Abstract.

Chromosome instability region analysis and identification of the driver genes of the epithelial ovarian cancer cell lines A2780 and SKOV3

Epithelial ovarian cancer (EOC) is one of the most prevalent gynaecological cancers worldwide. The molecular mechanisms of serous ovarian cancer (SOC) remain unclear and not well understood. SOC cases are primarily diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to obtain a better understanding of precise molecular mechanisms and to identify the chromosome instability region and key driver genes in the carcinogenesis and progression of SOC. Whole-exome sequencing was performed on the normal ovarian cell line IOSE80 and the EOC cell lines SKOV3 and A2780. The single-nucleotide variation burden, distribution, frequency and signature followed the known ovarian mutation profiles, without chromosomal bias. Recurrently mutated ovarian cancer driver genes, including LRP1B, KMT2A, ARID1A, KMT2C and ATRX were also found in two cell lines. The genome distribution of copy number alterations was found by copy number variation (CNV) analysis, including amplification of 17q12 and 4p16.1 and deletion of 10q23.33. The CNVs of MED1, GRB7 and MIEN1 located at 17q12 were found to be correlated with the overall survival of SOC patients (MED1: p = 0.028, GRB7: p = 0.0048, MIEN1: p = 0.0051), and the expression of the three driver genes in the ovarian cell line IOSE80 and EOC cell lines SKOV3 and A2780 was confirmed by western blot and cell immunohistochemistry.

RGS16 regulates Hippo-YAP activity to promote esophageal cancer cell proliferation and migration

Esophageal Squamous Cell Carcinoma (ESCC) is a common malignant tumor of digestive tract, accounting for 90% of all pathological types of esophageal cancer. Despite the rapid development of multi-disciplinary treatment such as surgery, chemotherapy, radiotherapy and chemoradiotherapy, the prognosis of patients with ESCC is still poor. Regulators of G-protein signaling (RGSs) are involved in the processes of various cancers. The expression levels of its family member RGS16 are abnormally elevated in a variety of tumors, but its role in ESCC is still unclear. We found that RGS16 expression is aberrantly increased in ESCC tissues and correlated with poor prognosis of ESCC patients from The Cancer Genome Atlas (TCGA) database and our collected ESCC tissues. Moreover, knockdown of RGS16 in two ESCC cells could indeed inhibit their proliferation and migration. We further explored the molecular mechanism of RGS16 in ESCC, and the correlation analysis from TCGA database showed that the mRNA levels of RGS16 was positively correlated with that of CTGF and CYR61, two target genes of Hippo-YAP signaling. Consistently, RGS16- knockdown significantly inhibited the expression of CTGF and CYR61 in ESCC cells. We found that the phosphorylation levels of LATS1 and YAP were significantly increased and YAP translocated into the cytoplasm after depletion of RGS16 in ESCC cells. Also, RGS16-knockdown promoted the interaction between LATS1 and upstream kinase MST1. In addition, reintroduction of a constitutive active YAP5A mutant significantly rescued CTGF expression and cell proliferation in RGS16-knockdown cells. Together, our work revealed that RGS16 promoted YAP activity through disrupting the interaction between LATS1 and MST1, thus promoting the proliferation and migration of ESCC cells.

Endoplasmic Reticulum Stress-Related Ten-Biomarker Risk Classifier for Survival Evaluation in Epithelial Ovarian Cancer and TRPM2: A Potential Therapeutic Target of Ovarian Cancer

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignant tumor. Endoplasmic reticulum (ER) stress plays an important role in the malignant behaviors of several tumors. In this study, we established a risk classifier based on 10 differentially expressed genes related to ER stress to evaluate the prognosis of patients and help to develop novel medical decision-making for EOC cases. A total of 378 EOC cases with transcriptome data from the TCGA-OV public dataset were included. Cox regression analysis was used to establish a risk classifier based on 10 ER stress-related genes (ERGs). Then, through a variety of statistical methods, including survival analysis and receiver operating characteristic (ROC) methods, the prediction ability of the proposed classifier was tested and verified. Similar results were confirmed in the GEO cohort. In the immunoassay, the different subgroups showed different penetration levels of immune cells. Finally, we conducted loss-of-function experiments to silence TRPM2 in the human EOC cell line. We created a 10-ERG risk classifier that displays a powerful capability of survival evaluation for EOC cases, and TRPM2 could be a potential therapeutic target of ovarian cancer cells.

RGS2 and female common diseases: a guard of women's health

Currently, women around the world are still suffering from various female common diseases with the high incidence, such as ovarian cancer, uterine fibroids and preeclampsia (PE), and some diseases are even with the high mortality rate. As a negative feedback regulator in G Protein-Coupled Receptor signaling (GPCR), the Regulator of G-protein Signaling (RGS) protein family participates in regulating kinds of cell biological functions by destabilizing the enzyme-substrate complex through the transformation of hydrolysis of G Guanosine Triphosphate (GTP). Recent work has indicated that, the Regulator of G-protein Signaling 2 (RGS2), a member belonging to the RGS protein family, is closely associated with the occurrence and development of certain female diseases, providing with the evidence that RGS2 functions in sustaining women's health. In this review paper, we summarize the current knowledge of RGS2 in female common diseases, and also tap and discuss its therapeutic potential by targeting multiple mechanisms.

RGS proteins and their roles in cancer: friend or foe?

As negative modulators of G-protein-coupled receptors (GPCRs) signaling, regulators of G protein signaling (RGS) proteins facilitate various downstream cellular signalings through regulating kinds of heterotrimeric G proteins by stimulating the guanosine triphosphatase (GTPase) activity of G-protein α (Gα) subunits. The expression of RGS proteins is dynamically and precisely mediated by several different mechanisms including epigenetic regulation, transcriptional regulation -and post-translational regulation. Emerging evidence has shown that RGS proteins act as important mediators in controlling essential cellular processes including cell proliferation, survival -and death via regulating downstream cellular signaling activities, indicating that RGS proteins are fundamentally involved in sustaining normal physiological functions and dysregulation of RGS proteins (such as aberrant expression of RGS proteins) is closely associated with pathologies of many diseases such as cancer. In this review, we summarize the molecular mechanisms governing the expression of RGS proteins, and further discuss the relationship of RGS proteins and cancer.

Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data

The integration of transcriptome and proteome analysis can lead to the discovery of a myriad of biological insights into ovarian cancer. Proteome, clinical, and transcriptome data about ovarian cancer were downloaded from TCGA's database. A LASSO-Cox regression was used to uncover prognostic-related proteins and develop a new protein prognostic signature for patients with ovarian cancer to predict their prognosis. Patients were brought together in subgroups using a consensus clustering analysis of prognostic-related proteins. To further investigate the role of proteins and protein-coding genes in ovarian cancer, additional analyses were performed using multiple online databases (HPA, Sangerbox, TIMER, cBioPortal, TISCH, and CancerSEA). The final resulting prognosis factors consisted of seven protective factors (P38MAPK, RAB11, FOXO3A, AR, BETACATENIN, Sox2, and IGFRb) and two risk factors (AKT_pS473 and ERCC5), which can be used to construct a prognosis-related protein model. A significant difference in overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI) curves were found in the training, testing, and whole sets when analyzing the protein-based risk score (p < 0.05). We also illustrated a wide range of functions, immune checkpoints, and tumor-infiltrating immune cells in prognosis-related protein signatures. Additionally, the protein-coding genes were significantly correlated with each other. EMTAB8107 and GSE154600 single-cell data revealed that the genes were highly expressed. Furthermore, the genes were related to tumor functional states (angiogenesis, invasion, and quiescence). We reported and validated a survivability prediction model for ovarian cancer based on prognostic-related protein signatures. A strong correlation was found between the signatures, tumor-infiltrating immune cells, and immune checkpoints. The protein-coding genes were highly expressed in single-cell RNA and bulk RNA sequencing, correlating with both each other and tumor functional states.

Fatty acid metabolism is related to the immune microenvironment changes of gastric cancer and RGS2 is a new tumor biomarker

Background

Alterations in lipid metabolism promote tumor progression. However, the role of lipid metabolism in the occurrence and development of gastric cancer have not been fully clarified.

Method

Here, genes that are related to fatty acid metabolism and differentially-expressed between normal and gastric cancer tissues were identified in the TCGA-STAD cohort. The intersection of identified differentially-expressed genes with Geneset was determined to obtain 78 fatty acid metabolism-related genes. The ConsensusClusterPlus R package was used to perform differentially-expressed genes, which yielded divided two gastric cancer subtypes termed cluster 1 and cluster 2.

Results

Patients in cluster 2 was found to display poorer prognosis than patients in cluster 1. Using machine learning method to select 8 differentially expressed genes among subtypes to construct fatty acid prognostic risk score model (FARS), which was found to display good prognostic efficacy. We also identified that certain anticancer drugs, such as bortezomib, elesclomol, GW843682X, and nilotinib, showed significant sensitivity in the high FARS score group. RGS2 was selected as the core gene upon an analysis of the gastric cancer single-cell, and Western blotting and immunofluorescence staining results revealed high level of expression of this gene in gastric cancer cells. The results of immunohistochemical staining showed that a large amount of RGS2 was deposited in the stroma in gastric cancer. A pan-cancer analysis also revealed a significant association of RGS2 with TMB, TIDE, and CD8+ T-cell infiltration in other cancer types as well. RGS2 may thus be studied further as a new target for immunotherapy in future studies on gastric cancer.

Conclusion

In summary, the FARS model developed here enhances our understanding of lipid metabolism in the TME in gastric cancer, and provides a theoretical basis for predicting tumor prognosis and clinical treatment.

Functions of regulators of G protein signaling 16 in immunity, inflammation, and other diseases

Regulators of G protein signaling (RGS) act as guanosine triphosphatase activating proteins to accelerate guanosine triphosphate hydrolysis of the G protein α subunit, leading to the termination of the G protein-coupled receptor (GPCR) downstream signaling pathway. RGS16, which is expressed in a number of cells and tissues, belongs to one of the small B/R4 subfamilies of RGS proteins and consists of a conserved RGS structural domain with short, disordered amino- and carboxy-terminal extensions and an α-helix that classically binds and de-activates heterotrimeric G proteins. However, with the deepening of research, it has been revealed that RGS16 protein not only regulates the classical GPCR pathway, but also affects immune, inflammatory, tumor and metabolic processes through other signaling pathways including the mitogen-activated protein kinase, phosphoinositide 3-kinase/protein kinase B, Ras homolog family member A and stromal cell-derived factor 1/C-X-C motif chemokine receptor 4 pathways. Additionally, the RGS16 protein may be involved in the Hepatitis B Virus -induced inflammatory response. Therefore, given the continuous expansion of knowledge regarding its role and mechanism, the structure, characteristics, regulatory mechanisms and known functions of the small RGS proteinRGS16 are reviewed in this paper to prepare for diagnosis, treatment, and prognostic evaluation of different diseases such as inflammation, tumor, and metabolic disorders and to better study its function in other diseases.

Construction of a tumor immune infiltration macrophage signature for predicting prognosis and immunotherapy response in liver cancer

Liver cancer is an extraordinarily heterogeneous malignant disease. The tumor microenvironment (TME) and tumor-associated macrophages (TAMs) are the major drivers of liver cancer initiation and progression. It is critical to have a better understanding of the complicated interactions between liver cancer and the immune system for the development of cancer immunotherapy. Based on the gene expression profiles of tumor immune infiltration cells (TIICs), upregulated genes in TAMs and downregulated genes in other types of immune cells were identified as macrophage-specific genes (MSG). In this study, we combined MSG, immune subtypes, and clinical information on liver cancer to develop a tumor immune infiltration macrophage signature (TIMSig). A four-gene signature (S100A9, SLC22A15, TRIM54, and PPARGC1A) was identified as the TAM-related prognostic genes for liver cancer, independent of multiple clinicopathological parameters. Survival analyses showed that patients with low TIMSig had a superior survival rate than those with high TIMSig. Additionally, clinical immunotherapy response and TIMSig was observed as highly relevant. In addition, TIMSig could predict the response to chemotherapy. Collectively, the TIMSig could be a potential tool for risk-stratification, clinical decision making, treatment planning, and oncology immunotherapeutic drug development.

Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets

Background

Thyroid carcinoma (THCA) has a low mortality rate, but its incidence has been rising over the years. We need to pay attention to its progression and prognosis. In this study, a transcriptome sequencing analysis and bioinformatics methods were used to screen key genes associated with THCA development and analyse their clinical significance and diagnostic value.

Methods

We collected 10 pairs of THCA tissues and noncancerous tissues, these samples were used for transcriptome sequencing to identify disordered genes. The gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Comprehensive analysis of thyroid clinicopathological data using The Cancer Genome Atlas (TCGA). R software was used to carry out background correction, normalization and log2 conversion. We used quantitative real-time PCR (qRT–PCR) and Western blot to determine differentially expressed genes (DEGs) expression in samples. We integrated the DEGs expression, clinical features and progression-free interval (PFI). The related functions and immune infiltration degree were established by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA). The UALCAN database was used to analyse the methylation level.

Results

We evaluated DEGs between normal tissue and cancer. Three genes were identified: regulator of G protein signaling 8 (RGS8), diacylglycerol kinase iota (DGKI) and oculocutaneous albinism II (OCA2). The mRNA and protein expression levels of RGS8, DGKI and OCA2 in normal tissues were higher than those in THCA tissues. Better survival outcomes were associated with higher expression of RGS8 (HR=0.38, P=0.001), DGKI (HR=0.52, P=0.022), and OCA2 (HR=0.41, P=0.003). The GO analysis, KEGG analysis and GSEA proved that the coexpressed genes of RGS8, DGKI and OCA2 were related to thyroid hormone production and peripheral downstream signal transduction effects. The expression levels of RGS8, DGKI and OCA2 were linked to the infiltration of immune cells such as DC cells. The DNA methylation level of OCA2 in cancer tissues was higher than that in the normal samples.

Conclusions

RGS8, DGKI and OCA2 might be promising prognostic molecular markers in patients with THCA and reveal the clinical significance of RGS8, DGKI and OCA2 in THCA.

Integrating cell cycle score for precise risk stratification in ovarian cancer

Background: Ovarian cancer (OC) is a highly heterogeneous disease, of which the mesenchymal subtype has the worst prognosis, is the most aggressive, and has the highest drug resistance. The cell cycle pathway plays a vital role in ovarian cancer development and progression. We aimed to screen the key cell cycle genes that regulated the mesenchymal subtype and construct a robust signature for ovarian cancer risk stratification.

Methods: Network inference was conducted by integrating the differentially expressed cell cycle signature genes and target genes between the mesenchymal and non-mesenchymal subtypes of ovarian cancer and identifying the dominant cell cycle signature genes.

Results: Network analysis revealed that two cell cycle signature genes (POLA2 and KIF20B) predominantly regulated the mesenchymal modalities of OC and used to construct a prognostic model, termed the Cell Cycle Prognostic Signature of Ovarian Cancer (CCPOC). The CCPOC-high patients showed an unfavorable prognosis in the GSE26712 cohort, consistent with the results in the seven public validation cohorts and one independent internal cohort (BL-OC cohort, qRT-PCR, n = 51). Functional analysis, drug-sensitive analysis, and survival analysis showed that CCPOC-low patients were related to strengthened tumor immunogenicity and sensitive to the anti-PD-1/PD-L1 response rate in pan-cancer (r = −0.47, OC excluded), which indicated that CCPOC-low patients may be more sensitive to anti-PD-1/PD-L1.

Conclusion: We constructed and validated a subtype-specific, cell cycle-based prognostic signature for ovarian cancer, which has great potential for predicting the response of anti-PD-1/PD-L1.

Exploration of the Immunotyping Landscape and Immune Infiltration-Related Prognostic Markers in Ovarian Cancer Patients

Background

Increasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.

Methods

The ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.

Results

Three immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.

Conclusions

Our study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.

Identification of gene signatures for COAD using feature selection and Bayesian network approaches

The combination of TCGA and GTEx databases will provide more comprehensive information for characterizing the human genome in health and disease, especially for underlying the cancer genetic alterations. Here we analyzed the gene expression profile of COAD in both tumor samples from TCGA and normal colon tissues from GTEx. Using the SNR-PPFS feature selection algorithms, we discovered a 38 gene signatures that performed well in distinguishing COAD tumors from normal samples. Bayesian network of the 38 genes revealed that DEGs with similar expression patterns or functions interacted more closely. We identified 14 up-DEGs that were significantly correlated with tumor stages. Cox regression analysis demonstrated that tumor stage, STMN4 and FAM135B dysregulation were independent prognostic factors for COAD survival outcomes. Overall, this study indicates that using feature selection approaches to select key gene signatures from high-dimensional datasets can be an effective way for studying cancer genomic characteristics.

ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P > N, such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.

Screening Plasma Exosomal RNAs as Diagnostic Markers for Cervical Cancer: An Analysis of Patients Who Underwent Primary Chemoradiotherapy

This preliminary study aimed to screen non-coding RNAs (ncRNAs) from plasma exosomes as a new method for cervical cancer diagnosis. Differentially expressed RNAs were initially selected from among a group of 12 healthy individuals (normal group) and a pretreatment group of 30 patients with cervical cancer (cancer group). Then, we analyzed the association between an ncRNA-mRNA network and cancer using ingenuity pathway analysis after secondary selection according to the number and correlation of mRNAs (or ncRNAs) relative to changes in the expression of primarily selected ncRNAs (or mRNAs) before and after chemoradiotherapy. The number of RNAs selected from the initial RNAs was one from 13 miRNAs, four from 42 piRNAs, four from 28 lncRNAs, nine from 18 snoRNAs, 10 from 76 snRNAs, nine from 474 tRNAs, nine from 64 yRNAs, and five from 67 mRNAs. The combination of miRNA (miR-142-3p), mRNAs (CXCL5, KIF2A, RGS18, APL6IP5, and DAPP1), and snoRNAs (SNORD17, SCARNA12, SNORA6, SNORA12, SCRNA1, SNORD97, SNORD62, and SNORD38A) clearly distinguished the normal samples from the cancer group samples. We present a method for efficiently screening eight classes of RNAs isolated from exosomes for cervical cancer diagnosis using mRNAs (or ncRNAs) altered by chemoradiotherapy.

MicroRNA-376b-3p targets RGS1 mRNA to inhibit proliferation, metastasis, and apoptosis in osteosarcoma

Background

To investigate the role of microRNA-376b-3p (miR-376b-3p) and regulator of G protein signaling 1 (RGS1) in the proliferation, metastasis, and apoptosis of osteosarcoma.

Methods

Differentially expressed genes (DEGs) between tumor and normal tissues from GSE14359 and GSE33382 in the cancer genome atlas (TCGA) dataset were analyzed with GEO2R online. Similarly, differentially expressed miRNAs from GSE70367 were also analyzed with GEO2R. The interaction between the differentially expressed miRNAs and the shared distal metastasis-related DEGs from the two datasets were analyzed using miRWalk and Cytoscape. RGS1 and miR-376-3p were chosen to verify the prediction. RGS1 stably expressing and silencing cells were established based on the MG63 and U2OS cell lines. The targeting of RGS1 with miR-376b-3p was confirmed with Starbase prediction and luciferase reporter assay. Cell proliferation, metastasis, and apoptosis were characterized in vitro and in xenograft mice.

Results

A total of 10 up-regulated and 8 down-regulated DEGs were characterized as shared metastasis-related DEGs for GSE14359 and GSE33382. Among these DEGs, RGS1 was targeted with miR-376b-3p, a predicted down-regulated miRNA in GSE70367. High expression of RGS1 predicted proliferation, invasion, metastases, and poor prognosis in osteosarcoma. Overexpression of RGS1 promoted proliferation, invasion, mobility, and stemness in MG63 and U2OS cells, while silencing of RGS1 had the opposite effect in both cell lines. High expression of RGS1 promoted tumor growth in xenograft nude mice. RGS1 was targeted with miR-376b-3p; the addition of miR-376b-3p down-regulated RGS1, and suppressed cell proliferation, invasion, and metastasis. Meanwhile, sponging of miR-376b-3p had the opposite effect. The suppressive effects of miR-376b-3p could be abolished with RGS1, as cell proliferation, stemness, metastasis, and invasion were all promoted with RGS1 co-transfection in both cell lines.

Conclusions

Our study indicated that RGS1 is a tumor-promoting gene in osteosarcoma, which could be inhibited with miR-376b-3p.

Circular RNA-Related CeRNA Network and Prognostic Signature for Patients with Osteosarcoma

Introduction

Osteosarcoma (OSA) is characterized by its relatively high morbidity in children and adolescents. Patients usually have advanced disease at the time of diagnosis, resulting in poor outcomes. This study focused on building a circular RNA-based ceRNA network to develop a reliable model for OSA risk prediction.

Methods

We used the Gene Expression Omnibus (GEO) datasets to explore the expression patterns of circRNA, miRNA, and mRNA in OSA. The prognostic value of circRNA host genes was assessed with data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database using Kaplan–Meier survival analysis. We established a circRNA-related ceRNA network and annotated its biological functions. Next, we developed a prognostic risk signature based on mRNAs extracted from the ceRNA network. We also developed a prognostic model and constructed a nomogram to enhance the prediction of OSA prognosis.

Results

We identified 166 DEcircRNAs, 233 DEmiRNAs, and 1317 DEmRNAs and used them to create a circRNA-related ceRNA network. We then established a prognostic risk model consisting of four genes (MLLT11, TNFRSF11B, SLC7A7, and PARVA). Moreover, we found that inhibition of MLLT11 and SLC7A7 blocked OSA cell proliferation and migration in in vitro experiments.

Conclusion

Our study identifies crucial prognostic genes and provides a circRNA-related ceRNA network for OSA, which will contribute to the elucidation of the molecular mechanisms underlying the oncogenesis and development of OSA.

Transcriptome-based stemness indices analysis reveals platinum-based chemo-theraputic response indicators in advanced-stage serous ovarian cancer

Serous ovarian cancer (SOC) is a main histological subtype of ovarian cancer, in which cancer stem cells (CSC) are responsible for its chemoresistance. However, the underlying modulation mechanisms of chemoresistance led by cancer stemness are still undefined. We aimed to investigate potential drug-response indicators among stemness-associated biomarkers in advanced SOC samples. The mRNA expression-based stemness index (mRNAsi) of The Cancer Genome Atlas (TCGA) was evaluated and corrected by tumor purity. Weighted gene co-expression network analysis (WGCNA) was utilized to explore the gene modules and key genes involved in stemness characteristics. We found that mRNAsi and corrected mRNAsi scores were both greater in tumors of Grade 3 and 4 than that of Grade 1 and 2. Forty-two key genes were obtained from the most significant mRNAsi-related gene module. Functional annotation revealed that these key genes were mainly involved in the mitotic division. Thirteen potential platinum-response indicators were selected from the genes enriched to platinum-response associated pathways. Among them, we identified 11 genes with prognostic value of progression-free survival (PFS) in advanced SOC patients treated with platinum and 7 prognostic genes in patients treated with a combination of platinum and taxol. The expressions of the 13 key genes were also validated between platinum-resistant and -sensitive SOC samples of advanced stages in two Gene Expression Omnibus (GEO) datasets. The results revealed that CDC20 was a potential platinum-sensitivity indicator in advanced SOC. These findings may provide a new insight for chemotherapies in advanced SOC patients clinically.