RGS5 plays a significant role in renal cell carcinoma

Single-cell RNA sequencing in ovarian cancer: Current progress and future prospects

Ovarian cancer is one of the most prevalent gynaecological malignancies. The rapid development of single-cell RNA sequencing (scRNA-seq) has allowed scientists to use this technique to study ovarian cancer development, heterogeneity, and tumour environment. Although multiple original research articles have reported the use of scRNA-seq in understanding ovarian cancer and how therapy resistance occurs, there is a lack of a comprehensive review that could summarize the findings from multiple studies. Therefore, this review aimed to fill this gap by comparing and summarizing the results from different studies that have used scRNA-seq in understanding ovarian cancer development, heterogeneity, tumour microenvironment, and treatment resistance. This review will begin with an overview of scRNA-seq workflow, followed by a discussion of various applications of scRNA-seq in studying ovarian cancer. Next, the limitations and future directions of scRNA-seq in ovarian cancer research will be presented.

A pan-cancer analysis of the oncogenic and immunological roles of RGS5 in clear cell renal cell carcinomas based on in vitro experiment validation

BACKGROUND

RGS5, the first gene identified in tumor-resident pericytes, plays a crucial role in angiogenesis. However, its effects on immunology and prognosis in human cancer are still mostly unknown. This study investigates the carcinogenic and immunological roles of RGS5 through a comprehensive pan-cancer analysis.

METHODS

A standardized pan-cancer dataset for RGS5 was obtained from the public database. R software and relevant packages were utilized to analyze the oncogenic and immunological roles. Clinical samples and cellular experiments were conducted to validate RGS5 expression and its biological function in renal cancer.

RESULTS

Bioinformatics analysis revealed that RGS5 is dysregulated in a variety of human malignancies and is significantly associated with patient prognosis. Additionally, RGS5 expression is closely linked to tumor heterogeneity and stemness indicators across different cancer types. Co-expression of RGS5 with genes involved in MHC, immune activation, immunosuppressive proteins, chemokines, and chemokine receptors was observed in various tumors. High expression of RGS5 predicts a good prognosis in patients with renal cancer. In the renal cancer cohort, RGS5 expression strongly correlated with the distribution of tumor-associated fibroblasts. Silencing RGS5 expression can affect the proliferation, migration, and invasion of renal carcinoma cells.

CONCLUSIONS

RGS5 expression in tumors is intricately associated with various clinical features, particularly concerning tumor progression and patient prognosis.

The role of cell cycle-related genes in the tumorigenesis of adrenal and thyroid neuroendocrine tumors

The molecular mechanisms underlying adrenal and thyroid neuroendocrine tumors, including their tumorigenesis, progression, and metastasis, involve unique pathways regulating cell cycle progression. To better understand these mechanisms and pathways, extensive in-depth research on cell cycle-related genes is necessary. This review aims to describe and interpret current single-cell RNA sequencing studies on neuroblastoma, medullary thyroid cancer, and pheochromocytoma tumors. Our review summarizes differentially expressed cell cycle-related genes with distinct functions, highlighting their potential as therapeutic targets and components of panels used to determine tumor type or aggressiveness. Although some insights have been gained, there is still limited information on these topics, and further research is required to explore the regulatory mechanisms of these tumors.

Single-cell RNA-seq reveals heterogeneity in metastatic renal cell carcinoma and effect of anti-angiogenesis therapy in the pancreas metastatic lesion

Metastatic clear cell renal cell carcinoma has heterogenous tumor microenvironment (TME). Among the metastatic lesions, pancreas metastasis is rare and controversy in treatment approaches. Here, extensive primary and metastatic lesion samples were included by single-cell RNA-seq to decipher the distinct metastasis TME. The hypoxic and inflammatory TME of pancreas metastasis was decoded in this study, and the activation of PAX8-myc signaling, and metabolic reprogramming were observed. The active components including endothelial cells, fibroblasts and T cells were profiled. Meanwhile, we also evaluated the effect of anti-angiogenesis treatment in the pancreas metastasis patient. The potential mechanisms of pancreatic tropism, instability of genome, and the response of immunotherapy were also discussed in this work. Taken together, our findings suggest a clue to the heterogeneity in metastasis TME and provide evidence for the treatment of pancreas metastasis in renal cell carcinoma patients.

ML-GAP: machine learning-enhanced genomic analysis pipeline using autoencoders and data augmentation

Introduction

The advent of RNA sequencing (RNA-Seq) has significantly advanced our understanding of the transcriptomic landscape, revealing intricate gene expression patterns across biological states and conditions. However, the complexity and volume of RNA-Seq data pose challenges in identifying differentially expressed genes (DEGs), critical for understanding the molecular basis of diseases like cancer.

Methods

We introduce a novel Machine Learning-Enhanced Genomic Data Analysis Pipeline (ML-GAP) that incorporates autoencoders and innovative data augmentation strategies, notably the MixUp method, to overcome these challenges. By creating synthetic training examples through a linear combination of input pairs and their labels, MixUp significantly enhances the model's ability to generalize from the training data to unseen examples.

Results

Our results demonstrate the ML-GAP's superiority in accuracy, efficiency, and insights, particularly crediting the MixUp method for its substantial contribution to the pipeline's effectiveness, advancing greatly genomic data analysis and setting a new standard in the field.

Discussion

This, in turn, suggests that ML-GAP has the potential to perform more accurate detection of DEGs but also offers new avenues for therapeutic intervention and research. By integrating explainable artificial intelligence (XAI) techniques, ML-GAP ensures a transparent and interpretable analysis, highlighting the significance of identified genetic markers.

A novel cancer-associated fibroblast signature for kidney renal clear cell carcinoma via integrated analysis of single-cell and bulk RNA-sequencing

Cancer-associated fibroblasts (CAFs), integral components of the tumor microenvironment, play a pivotal role in tumor proliferation, metastasis, and clinical outcomes. However, its specific roles in Kidney Renal Clear Cell Carcinoma (KIRC) remain poorly understood. Employing the established Seurat single-cell analysis pipeline, we identified 21 CAFs marker genes. Subsequently, a prognostic signature consisting of 6 CAFs marker genes (RGS5, PGF, TPM2, GJA4, SEPT4, and PLXDC1) was developed in a cohort through univariate and LASSO Cox regression analyses. The model's efficacy was then validated in an external cohort, with a remarkable predictive performance in 1-, 3-, and 5-year. Patients in the high-risk group exhibited significantly inferior survival outcomes (p < 0.001), and the risk score was an independent prognostic factor (p < 0.05). Distinct differences in immune cell profiles and drug susceptibility were observed between the two risk groups. In KIRC, the PGF-VEGFR1 signaling pathway displayed a notable increase. PGF expression was significantly elevated in tumor tissues, as demonstrated by quantitative real-time polymerase chain reaction. In vitro, transwell assays and CCK8 revealed that recombinant-PGF could enhance the capability of cell proliferation, migration, and invasion in 769P and 786-O cells. This study firstly developed a novel predictive model based on 6 CAFs genes for KIRC. Additionally, PGF may present a potential therapeutic target to enhance KIRC treatment.

A quinoline derivative-based supramolecular gel for fluorescence 'turn-off' detection of Fe3+and Cu2

In this research, we synthesized and constructed a novel gelator (namedQN) combining quinoline and naphthalene that self-assembled in N, N-dimethylformamide (DMF) to form a stable supramolecular gel (namedOQN). Under UV light, gelOQNexhibited extremely bright yellow fluorescence. The gelOQNshowed excellent sensing performance for both Fe3+and Cu2+, with a fluorescence 'turn-off' detection mechanism and the lowest detection limit of 7.58 × 10-8M and 1.51 × 10-8M, respectively. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectra, x-ray powder diffraction (XRD), rheological measurements, x-ray photoelectron spectroscopy (XPS), and fluorescence spectroscopy were used to characterize the gelOQN. TheOQNion-responsive membrane created is an excellent fluorescent writing material.

Implication of biosignatures in the progression of endometriosis

Endometriosis is an estrogen-dependent chronic inflammatory disorder involving the placement and growth of endometrial tissue outside the uterine cavity. It is the most common multifactorial disease that affects the life quality of women in reproductive age. Due to its multicomponent nature, early diagnosis of the disease is challenging. Since many genetic, epigenetic alterations and non-genetic factors contribute to the pathology of endometriosis, devising a drug therapy that directly acts on the ectopic tissue is extremely difficult. Endometriosis is a hormone-driven disease with estrogen considered as a primary driver for the development of endometriotic lesions. This study aims to identify biosignatures involved in endometriosis with and without gonadotropin releasing hormone agonists (GnRHa). GnRHa is a short peptide analog of GnRH that causes inhibition of estrogen and androgen synthesis. Microarray based-gene expression profiling was performed on total RNA extracted from endometriotic tissue samples with and without GnRHa-treated patients already published in our previous paper. The untreated group were considered as the control. Genes were then selected for validation by quantitative real-time polymerase chain reaction (qRT-PCR). qRT-PCR analysis confirmed significant downregulation in(p < 0.05) expression of DARC (p = 0.0042), CDH1 (p = 0.0027), CDH5 (p = 0.0283), ATP2A3 (p < 0.001), RGS5 (p = 0.0032), and CD36 (p = 0.0162) in endometriosis patients treated with GnRHa analogs. Although, CTNNAL1 (p = 0.0136) also showed significant results but there was upregulation in their expression levels after GnRHa treatment. Thus, an altered expression of these genes makes them a possible candidate determinant of endometriosis treated with GnRHa.

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.

Identifying the critical state of complex biological systems by the directed-network rank score method

MOTIVATION

Catastrophic transitions are ubiquitous in the dynamic progression of complex biological systems; that is, a critical transition at which complex systems suddenly shift from one stable state to another occurs. Identifying such a critical point or tipping point is essential for revealing the underlying mechanism of complex biological systems. However, it is difficult to identify the tipping point since few significant differences in the critical state are detected in terms of traditional static measurements.

RESULTS

In this study, by exploring the dynamic changes in gene cooperative effects between the before-transition and critical states, we presented a model-free approach, the directed-network rank score (DNRS), to detect the early-warning signal of critical transition in complex biological systems. The proposed method is applicable to both bulk and single-cell RNA-sequencing (scRNA-seq) data. This computational method was validated by the successful identification of the critical or pre-transition state for both simulated and six real datasets, including three scRNA-seq datasets of embryonic development and three tumor datasets. In addition, the functional and pathway enrichment analyses suggested that the corresponding DNRS signaling biomarkers were involved in key biological processes.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at https://github.com/zhongjiayuan/DNRS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prognostic Impact of MITD1 and Associates With Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most malignant diseases with poor survival rate over the world. The tumor microenvironment (TME) is highly related to the oncogenesis, development, and prognosis of KIRC. Thus, making the identification of KIRC biomarkers and immune infiltrates critically important. Microtubule Interacting and Trafficking Domain containing 1(MITD1) was reported to participate in cytokinesis of cell division. In the present study, multiple bioinformatics tools and databases were applied to investigate the expression level and clinical value of MITD1 in KIRC. We found that the expression of MITD1 was significantly increased in KIRC tissues. Further, the KIRC patients with high MITD1 levels showed a worse overall survival (OS) rate and disease free survival (DFS) rate. Otherwise, we found a significant correlation MITD1 expression and the abundance of CD8+ T cells. Functional enrichment analyses revealed that immune response and cytokine-cytokine receptor are very critical signaling pathways which associated with MITD1 in KIRC. In conclusion, our findings indicated that MITD1 may be a potential biomarker and associated with immune infiltration in KIRC.

Immune classification of clear cell renal cell carcinoma

Since the outcome of treatments, particularly immunotherapeutic interventions, depends on the tumor immune micro-environment (TIM), several experimental and computational tools such as flow cytometry, immunohistochemistry, and digital cytometry have been developed and utilized to classify TIM variations. In this project, we identify immune pattern of clear cell renal cell carcinomas (ccRCC) by estimating the percentage of each immune cell type in 526 renal tumors using the new powerful technique of digital cytometry. The results, which are in agreement with the results of a large-scale mass cytometry analysis, show that the most frequent immune cell types in ccRCC tumors are CD8+ T-cells, macrophages, and CD4+ T-cells. Saliently, unsupervised clustering of ccRCC primary tumors based on their relative number of immune cells indicates the existence of four distinct groups of ccRCC tumors. Tumors in the first group consist of approximately the same numbers of macrophages and CD8+ T-cells and and a slightly smaller number of CD4+ T cells than CD8+ T cells, while tumors in the second group have a significantly high number of macrophages compared to any other immune cell type (P-value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<0.01$$\end{document}<0.01). The third group of ccRCC tumors have a significantly higher number of CD8+ T-cells than any other immune cell type (P-value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<0.01$$\end{document}<0.01), while tumors in the group 4 have approximately the same numbers of macrophages and CD4+ T-cells and a significantly smaller number of CD8+ T-cells than CD4+ T-cells (P-value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<0.01$$\end{document}<0.01). Moreover, there is a high positive correlation between the expression levels of IFNG and PDCD1 and the percentage of CD8+ T-cells, and higher stage and grade of tumors have a substantially higher percentage of CD8+ T-cells. Furthermore, the primary tumors of patients, who are tumor free at the last time of follow up, have a significantly higher percentage of mast cells (P-value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<0.01$$\end{document}<0.01) compared to the patients with tumors for all groups of tumors except group 3.

Immune classification of clear cell renal cell carcinoma

Since the outcome of treatments, particularly immunotherapeutic interventions, depends on the tumor immune micro-environment (TIM), several experimental and computational tools such as flow cytometry, immunohistochemistry, and digital cytometry have been developed and utilized to classify TIM variations. In this project, we identify immune pattern of clear cell renal cell carcinomas (ccRCC) by estimating the percentage of each immune cell type in 526 renal tumors using the new powerful technique of digital cytometry. The results, which are in agreement with the results of a large-scale mass cytometry analysis, show that the most frequent immune cell types in ccRCC tumors are CD8+ T-cells, macrophages, and CD4+ T-cells. Saliently, unsupervised clustering of ccRCC primary tumors based on their relative number of immune cells indicates the existence of four distinct groups of ccRCC tumors. Tumors in the first group consist of approximately the same numbers of macrophages and CD8+ T-cells and and a slightly smaller number of CD4+ T cells than CD8+ T cells, while tumors in the second group have a significantly high number of macrophages compared to any other immune cell type (P-value [Formula: see text]). The third group of ccRCC tumors have a significantly higher number of CD8+ T-cells than any other immune cell type (P-value [Formula: see text]), while tumors in the group 4 have approximately the same numbers of macrophages and CD4+ T-cells and a significantly smaller number of CD8+ T-cells than CD4+ T-cells (P-value [Formula: see text]). Moreover, there is a high positive correlation between the expression levels of IFNG and PDCD1 and the percentage of CD8+ T-cells, and higher stage and grade of tumors have a substantially higher percentage of CD8+ T-cells. Furthermore, the primary tumors of patients, who are tumor free at the last time of follow up, have a significantly higher percentage of mast cells (P-value [Formula: see text]) compared to the patients with tumors for all groups of tumors except group 3.

Genome-wide association study of nephrolithiasis in an Eastern European population

PURPOSE

Nephrolithiasis is a urological pathology that occurs at high rates and carries a great burden in terms of costs. The probability of recurrence is significant, necessitating improvements in prophylaxis and understanding of the disease mechanism. Despite the high heritability of this disease, only five genome-wide association studies (GWAS) of nephrolithiasis have been published.

METHODS

We selected 335 unrelated confirmed nephrolithiasis cases from two major sample collection projects (blood and buccal swabs) in Romania. DNA was extracted from whole blood and buccal swabs at deCODE Genetics (Reykjavik, Iceland) and genotyped.

RESULTS

Single-nucleotide polymorphisms identified from this GWAS implicated biological pathways and gene ontologies involving solute transport, renal physiology, and calcium homeostasis. Three loci especially emerged as candidates with a highly significant association with nephrolithiasis: RS10917682 in Regulator of G protein signaling 5, which has crucial roles in mRNA regulation and has been linked to renal cell carcinoma; RS1118528 in Solute carrier family 25 member 24, which encodes a mitochondrial ATP-Mg/phosphate carrier protein that likely influences a variety of important cellular pathways; and the TOX2-associated locus rs4437026, because TOX2 is upregulated in several tumor types and linked to tumor progression.

CONCLUSION

This study is the largest kidney stone-related GWAS reported in an Eastern European population and the first GWAS performed in a Romanian population to investigate the genetic risk factors for nephrolithiasis. We identified several loci that warrant further investigation for a better understanding of this highly heritable condition.