Oncogenic Role of miR-217 During Clear Cell Renal Carcinoma Progression

Shuanglongjiegu pill promoted bone marrow mesenchymal stem cell osteogenic differentiation by regulating the miR-217/RUNX2 axis to activate Wnt/β-catenin pathway

This study aimed to investigate the effects of Shuanglongjiegu pill (SLJGP) on the osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) and explore its mechanism based on miR-217/RUNX2 axis. Results found that drug-containing serum of SLJGP promoted BMSCs viability with a dose-dependent effect. Under osteogenic differentiation conditions, SLJGP promoted the expression of ALP, OPN, BMP2, RUNX2, and the osteogenic differentiation ability of BMSCs. In addition, SLJGP significantly reduced miR-217 expression, and miR-217 directly targeted RUNX2. After treatment with miR-217 mimic, the promoting effects of SLJGP on proliferation and osteogenic differentiation of BMSCs were significantly inhibited. MiR-217 mimic co-treated with pcDNA-RUNX2 further confirmed that the miR-217/RUNX2 axis was involved in SLJGP to promote osteogenic differentiation of BMSCs. In addition, analysis of Wnt/β-catenin pathway protein expression showed that SLJGP activated the Wnt/β-catenin pathway through miR-217/RUNX2. In conclusion, SLJGP promoted osteogenic differentiation of BMSCs by regulating miR-217/RUNX2 axis and activating Wnt/β-catenin pathway.

The Role MicroRNA-135a in Suppressing Tumor Growth in Kidney Cancer Through the Regulation of Phosphoprotein Phosphatase2A and the Activation of the AKT and ERK1/2 Signaling Pathways

Background: Kidney cancer is a frequently occurring malignant tumor in the urinary system, with rising morbidity and mortality rates in recent times. Developing new biomarkers and therapeutic targets is essential to improve the prognosis of patients affected by kidney cancer. In recent years, miRNAs' role in tumorigenesis and development has received growing attention. miRNAs constitute a group of small non-coding RNA molecules that regulate gene expression, affecting various biological processes, including cell proliferation, differentiation, and apoptosis. Of the many miRNAs, miR-135a plays a pivotal role in several cancers. Nevertheless, the precise mechanisms and functions concerning miR-135a in renal cancer remain incompletely understood. Therefore, this study aims to analyze the effects of miR-135a on renal cancer replication and migration and its possible mechanisms, and to provide new strategies for the diagnosis and treatment of renal cancer. Methods: Renal cell lines (ACHN, A498) with stable hyperexpression of miR-135a and reduced expression of miR-135a were constructed by lentivirus packaging. The changes of replication, clone formation and migration ability of overexpressed miR-135a and overexpressed miR-135a in ACHN and A498 renal cell lines were detected. The possible mechanism of miR-135a affecting the replication of kidney cancer was analyzed by target gene prediction, double luciferase test, Western blotting and subcutaneous tumorigenicity assay in nude mice. Results: Hyperexpression of miR-135a can inhibit kidney cancer replication, whereas miR-135a knockdown potentially enhances replication. However, neither hyperexpression nor knockdown of miR-135a affects the migration ability of kidney cancer cells. The protein expression of PP2A-B56-γ, PP2A-Cα and PP2A-Cβ in renal cell line decreased after hyperexpression of miR-135a, while the protein expression of PP2A-B56-γ, PP2A-Cα and PP2A-Cβ increased after knockdown of miR-135a. In addition, the protein expression of p-Akt and p-ERK1/2 proteins in kidney cancer cells after hyperexpression of miR-135a were down-regulated, while the protein expression of p-Akt and p-ERK1/2 were up-regulated in kidney cancer cells after knockdown of miR-135a. In subcutaneous tumor formation experiments in nude mice, tumor size within nude mice in the miR-135a group was significantly smaller than in the control group. Conclusion: MiR-135a could suppress the replication of kidney cancer by modulating PP2A and AKT, ERK1/2 signaling pathways.

The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

The network structure of hematopoietic cancers

Hematopoietic cancers (HCs) are a heterogeneous group of malignancies that affect blood, bone marrow and lymphatic system. Here, by analyzing 1960 RNA-Seq samples from three independent datasets, we explored the co-expression landscape in HCs, by inferring gene co-expression networks (GCNs) with four cancer phenotypes (B and T-cell acute leukemia -BALL, TALL-, acute myeloid leukemia -AML-, and multiple myeloma -MM-) as well as non-cancer bone marrow. We characterized their structure (topological features) and function (enrichment analyses). We found that, as in other types of cancer, the highest co-expression interactions are intra-chromosomal, which is not the case for control GCNs. We also detected a highly co-expressed group of overexpressed pseudogenes in HC networks. The four GCNs present only a small fraction of common interactions, related to canonical functions, like immune response or erythrocyte differentiation. With this approach, we were able to reveal cancer-specific features useful for detection of disease manifestations.

Methylation-related genes involved in renal carcinoma progression

Renal carcinomas are a group of malignant tumors often originating in the cells lining the small tubes in the kidney responsible for filtering waste from the blood and urine production. Kidney tumors arise from the uncontrolled growth of cells in the kidneys and are responsible for a large share of global cancer-related morbidity and mortality. Understanding the molecular mechanisms driving renal carcinoma progression results crucial for the development of targeted therapies leading to an improvement of patient outcomes. Epigenetic mechanisms such as DNA methylation are known factors underlying the development of several cancer types. There is solid experimental evidence of relevant biological functions modulated by methylation-related genes, associated with the progression of different carcinomas. Those mechanisms can often be associated to different epigenetic marks, such as DNA methylation sites or chromatin conformation patterns. Currently, there is no definitive method to establish clear relations between genetic and epigenetic factors that influence the progression of cancer. Here, we developed a data-driven method to find methylation-related genes, so we could find relevant bonds between gene co-expression and methylation-wide-genome regulation patterns able to drive biological processes during the progression of clear cell renal carcinoma (ccRC). With this approach, we found out genes such as ITK oncogene that appear hypomethylated during all four stages of ccRC progression and are strongly involved in immune response functions. Also, we found out relevant tumor suppressor genes such as RAB25 hypermethylated, thus potentially avoiding repressed functions in the AKT signaling pathway during the evolution of ccRC. Our results have relevant implications to further understand some epigenetic-genetic-affected roles underlying the progression of renal cancer.

Pseudogenes in Cancer: State of the Art

Pseudogenes are duplicates of protein-coding genes that have accumulated multiple detrimental alterations, rendering them unable to produce the protein they encode. Initially disregarded as "junk DNA" due to their perceived lack of functionality, research on their biological roles has been hindered by this assumption. Nevertheless, recent focus has shifted towards these molecules due to their abnormal expression in cancer phenotypes. In this review, our objective is to provide a thorough overview of the current understanding of pseudogene formation, the mechanisms governing their expression, and the roles they may play in promoting tumorigenesis.

CNVs in 8q24.3 do not influence gene co-expression in breast cancer subtypes

Gene co-expression networks are a useful tool in the study of interactions that have allowed the visualization and quantification of diverse phenomena, including the loss of co-expression over long distances in cancerous samples. This characteristic, which could be considered fundamental to cancer, has been widely reported in various types of tumors. Since copy number variations (CNVs) have previously been identified as causing multiple genetic diseases, and gene expression is linked to them, they have often been mentioned as a probable cause of loss of co-expression in cancerous networks. In order to carry out a comparative study of the validity of this statement, we took 477 protein-coding genes from chromosome 8, and the CNVs of 101 genes, also protein-coding, belonging to the 8q24.3 region, a cytoband that is particularly active in the appearance of breast cancer. We created CNVS-conditioned co-expression networks of each of the 101 genes in the 8q24.3 region using conditional mutual information. The study was carried out using the four molecular subtypes of breast cancer (Luminal A, Luminal B, Her2, and Basal), as well as a case corresponding to healthy samples. We observed that in all cancer cases, the measurement of the Kolmogorov-Smirnov statistic shows that there are no significant differences between one and other values of the CNVs for any case. Furthermore, the co-expression interactions are stronger in all cancer subtypes than in the control networks. However, the control network presents a homogeneously distributed set of co-expression interactions, while for cancer networks, the highest interactions are more confined to specific cytobands, in particular 8q24.3 and 8p21.3. With this approach, we demonstrate that despite copy number alterations in the 8q24 region being a common trait in breast cancer, the loss of long-distance co-expression in breast cancer is not determined by CNVs.

Functional impact of multi-omic interactions in breast cancer subtypes

Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.