Identification of PLAUR-related ceRNA and immune prognostic signature for kidney renal clear cell carcinoma

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to clinical outcomes, tumor progression, and treatment sensitivity. However, the role of angiogenesis in the COAD microenvironment and its interaction with immunotherapy remains unclear. In this study, an integrative machine learning approach, including ten algorithms, was used to construct a prognostic consensus angiogenesis-related signature (CARS) for COAD. The optimal CARS constructed using the RSF + StepCox [forward] algorithm had superior performance for clinical prognostic prediction and served as an independent risk predictor for COAD. Patients in the low-CARS group, characterized by immune activation, elevated tumor mutation/neoantigen burden, and greater responsiveness to immunotherapy, had a superior prognosis. Patients in the high-CARS group exhibited a poor prognosis with higher angiogenesis activity and immunosuppressive status, indicating lower immunotherapy benefits. However, axitinib and olaparib may be promising treatment options for such patients. Taken together, we constructed a prognostic CARS that provides prognostic stratification and elucidates the characteristics of the tumor microenvironment, which might guide the selection of personalized treatments for patients with COAD.

High Glucose Increases Lactate and Induces the Transforming Growth Factor Beta-Smad 1/5 Atherogenic Pathway in Primary Human Macrophages

Hundreds of millions of people worldwide are expected to suffer from diabetes mellitus. Diabetes is characterized as a dynamic and heterogeneous disease that requires deeper understanding of the pathophysiology, genetics, and metabolic shaping of this disease and its macro/microvascular complications. Macrophages play an essential role in regulating local immune responses, tissue homeostasis, and disease pathogenesis. Here, we have analyzed transforming growth factor beta 1 (TGFβ1)/Smad signaling in primary human macrophages grown in normal (NG) and high-glucose (HG; +25 mM glucose) conditions. Cell culture lactate concentration and cellular phosphofructokinase (PFK) activity were increased in HG concentrations. High glucose levels in the growth media led to increased macrophage mRNA expression of TGFβ1, and TGFβ-regulated HAMP and PLAUR mRNA levels, while the expression of TGFβ receptor II remained unchanged. Stimulation of cells with TGFβ1 protein lead to Smad2 phosphorylation in both NG and HG conditions, while the phosphorylation of Smad1/5 was detected only in response to TGFβ1 stimulation in HG conditions. The use of the specific Alk1/2 inhibitor dorsomorphin and the Alk5 inhibitor SB431542, respectively, revealed that HG conditions led TGFβ1 to activation of Smad1/5 signaling and its downstream target genes. Thus, high-glucose activates TGFβ1 signaling to the Smad1/5 pathway in primary human macrophages, which may contribute to cellular homeostasis in a harmful manner, priming the tissues for diabetic complications.

PLAUR facilitates the progression of clear cell renal cell carcinoma by activating the PI3K/AKT/mTOR signaling pathway

Background

PLAUR has been found upregulated in various tumors and closely correlated with the malignant phenotype of tumor cells. The aim of this study was to investigate the relationship between PLAUR and clear cell renal cell carcinoma (ccRCC) and its potential mechanism of promoting tumor progression.

Methods

The expression levels and clinical significance of PLAUR, along with the associated signaling pathways, were extensively investigated in ccRCC samples obtained from The Cancer Genome Atlas (TCGA). PLAUR expression in 20 pairs of ccRCC tumor tissues and the adjacent tissues was assessed using qRT-PCR and IHC staining. Additionally, a series of in vitro experiments were conducted to investigate the impact of PLAUR suppression on cellular proliferation, migration, invasion, cell cycle progression, and apoptosis in ccRCC. The Western blot analysis was employed to investigate the expression levels of pivotal genes associated with the PI3K/AKT/mTOR signaling pathway.

Results

The expression of PLAUR was significantly upregulated in ccRCC compared to normal renal tissues, and higher PLAUR expression in ccRCC was associated with a poorer prognosis than low expression. The in-vitro functional investigations demonstrated that knockdown of PLAUR significantly attenuated the proliferation, migration, and invasion capabilities of ccRCC cells. Concurrently, PLAUR knockdown effectively induced cellular apoptosis, modulated the cell cycle, inhibited the EMT process, and attenuated the activation of the PI3K/AKT/mTOR signaling pathway. PLAUR may represent a key mechanism underlying ccRCC progression.

Conclusions

The involvement of PLAUR in ccRCC progression may be achieved through the activation of the PI3K/AKT/mTOR signaling pathway, making it a reliable biomarker for the identification and prediction of ccRCC.

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient's survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as "sponges" upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

Multiple databases analyzed the prognosis prediction of renin secretion pathway-related genes in renal clear cell carcinoma and immunotherapy

Background

Clear cell renal cell carcinoma (ccRCC) is a malignant kidney tumour and its progression is associated with the renin secretion pathway, so this study aimed to develop a prognostic model based on renin secretion pathway-related genes.

Methods

First, 453 renin secretion pathway-related genes were acquired [|log fold change (FC)| >1.5, false discovery rate (FDR) <0.05] from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The data were combined and further screened for 188 genes associated with ccRCC prognosis (P<0.05) by univariate independent prognostic analysis. These genes were subjected to least absolute shrinkage and selection operator regression to identify potential prognostic genes to construct the prognostic model. The stability of the model was externally validated. Combined risk scores and clinical information were used to create nomograms to accurately reflect patient survival. The model-related genes were further mined for subsequent analysis.

Results

A prognostic model of six renin secretion pathway genes (IGFBP3, PLAUR, CHKB-CPT1B, HOXA13, CDH13, and CDC20) was developed. Its reliability in predicting disease prognosis was confirmed by survival analysis, receiver operating characteristic (ROC) curve analysis and a risk curve. The nomogram and calibration curve showed good accuracy. The immune-related analyses revealed that the low-risk group would benefit more from immunotherapy.

Conclusions

The prognostic model of ccRCC based on six renin secretion pathway-related genes can be used to guide the precise treatment of ccRCC patients.

A novel immune-related gene signature for predicting immunotherapy outcomes and survival in clear cell renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is one of the most common cancers worldwide. In this study, a new model of immune-related genes was developed to predict the overall survival and immunotherapy efficacy in patients with ccRCC. Immune-related genes were obtained from the ImmPort database. Clinical data and transcriptomics of ccRCC samples were downloaded from GSE29609 and The Cancer Genome Atlas. An immune-related gene-based prognostic model (IRGPM) was developed using the least absolute shrinkage and selection operator regression algorithm and multivariate Cox regression. The reliability of the developed models was evaluated by Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves. Furthermore, we constructed a nomogram based on the IRGPM and multiple clinicopathological factors, along with a calibration curve to examine the predictive power of the nomogram. Overall, this study investigated the association of IRGPM with immunotherapeutic efficacy, immune checkpoints, and immune cell infiltration. Eleven IRGs based on 528 ccRCC samples significantly associated with survival were used to construct the IRGPM. Remarkably, the IRGPM, which consists of 11 hub genes (SAA1, IL4, PLAUR, PLXNB3, ANGPTL3, AMH, KLRC2, NR3C2, KL, CSF2, and SEMA3G), was found to predict the survival of ccRCC patients accurately. The calibration curve revealed that the nomogram developed with the IRGPM showed high predictive performance for the survival probability of ccRCC patients. Moreover, the IRGPM subgroups showed different levels of immune checkpoints and immune cell infiltration in patients with ccRCC. IRGPM might be a promising biomarker of immunotherapeutic responses in patients with ccRCC. Overall, the established IRGPM was valuable for predicting survival, reflecting the immunotherapy response and immune microenvironment in patients with ccRCC.