MRPS17 promotes invasion and metastasis through PI3K/AKT signal pathway and could be potential prognostic marker for gastric cancer

Nephron progenitors rhythmically alternate between renewal and differentiation phases that synchronize with kidney branching morphogenesis

The mammalian kidney achieves massive parallelization of function by exponentially duplicating nephron-forming niches during development. Each niche caps a tip of the ureteric bud epithelium (the future urinary collecting duct tree) as it undergoes branching morphogenesis, while nephron progenitors within niches balance self-renewal and differentiation to early nephron cells. Nephron formation rate approximately matches branching rate over a large fraction of mouse gestation, yet the nature of this apparent pace-maker is unknown. Here we correlate spatial transcriptomics data with branching 'life-cycle' to discover rhythmically alternating signatures of nephron progenitor differentiation and renewal across Wnt, Hippo-Yap, retinoic acid (RA), and other pathways. We then find in human stem-cell derived nephron progenitor organoids that Wnt/β-catenin-induced differentiation is converted to a renewal signal when it temporally overlaps with YAP activation. Similar experiments using RA activation indicate a role in setting nephron progenitor exit from the naive state, the spatial extent of differentiation, and nephron segment bias. Together the data suggest that nephron progenitor interpretation of consistent Wnt/β-catenin differentiation signaling in the niche may be modified by rhythmic activity in ancillary pathways to set the pace of nephron formation. This would synchronize nephron formation with ureteric bud branching, which creates new sites for nephron condensation. Our data bring temporal resolution to the renewal vs. differentiation balance in the nephrogenic niche and inform new strategies to achieve self-sustaining nephron formation in synthetic human kidney tissues.

SESN2 Could Be a Potential Marker for Diagnosis and Prognosis in Glioma

(1) Background: Glioma is among the most common brain tumors, and is difficult to eradicate with current therapeutic strategies due to its highly invasive and aggressive characteristics. Sestrin2 (SESN2) is an autophagy inducer. The effect of SESN2 on glioma is controversial and unclear. (2) Methods: We downloaded related RNA-seq data from the TCGA and GTEx databases. Bioinformatic analyses including differential gene expression analysis, KM survival curve analysis, univariate and multivariate Cox regression analyses, nomogram analysis, ROC curve analysis, gene function enrichment analysis, and immune cell infiltration analysis were conducted. In addition, data from the Human Protein Atlas (HPA) database were collected to validate SESN2 expression in glioma. (3) Results: In comparison with normal tissue, expression of SESN2 in glioma tissue was higher, and those with higher expressions had significantly lower overall survival rates. The results of univariate Cox regression analyses showed that SESN2 can be a disadvantageous factor in poor glioma prognosis. Both nomograms and ROC curves confirmed these findings. Meanwhile, according to gene function analysis, SESN2 may be involved in immune responses and the tumor microenvironment (TME). Based on the HPA database results, SESN2 is localized in the cytosol and shows high expression in glioma. (4) Conclusions: The expression of SESN2 in gliomas was positively relevant to a poorer prognosis, suggesting that SESN2 could be used as a prognostic gene.

Bioinformatics analysis of potential Key lncRNA-miRNA-mRNA molecules as prognostic markers and important ceRNA axes in gastric cancer

Gastric cancer (GC), the fifth most common malignancy worldwide, has an extremely poor prognosis at the advanced stage or the early stage if inadequately treated. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs all function as competing endogenous RNAs (ceRNAs) that target and regulate each other. Changes in their expression and their regulatory bioprocesses play important roles in GC. However, the roles of key RNAs and their regulatory networks remain unclear. In this study, RNA profiles were extracted from The Cancer Genome Atlas database, and R language was used to discover the differentially expressed (DE) lncRNAs, miRNAs and mRNAs in GC. Then, the DERNAs were paired by miRcode, miRDB, TargetScan and DIANA, and the ceRNA network was further constructed and visualized using Cytoscape. Moreover, a functional enrichment analysis was performed using Metascape. Afterward, the "survival" package was employed to identify candidate prognostic targets (DERNA-os) in the ceRNA network. Ultimately, the ceRNA network was analyzed to identify crucial lncRNA/miRNA/mRNA axes. Based on 374 gastric adenocarcinoma and gastric adenoma samples, 283 DEceRNAs (69 lncRNAs, 10 miRNAs, and 204 mRNAs) were identified. The 204 mRNAs were significantly enriched in some interesting functional clusters, such as the trans-synaptic signaling cluster and the protein digestion and absorption cluster. The ceRNA network consisted of 43 ceRNAs (13 lncRNAs, 2 miRNAs, and 28 mRNAs) that were related to prognosis. Among them, 2 lncRNAs (LNC00469 and AC010145.1) and 1 mRNA (PRRT4) were potential new biomarkers. In addition, according to the lncRNA/miRNA/mRNA regulatory relationships among the 43 ceRNAs, we identified four axes that might play important roles in the progression of GC and investigated the potential mechanism of the most promising axis (POU6F2-AS2/hsa-mir-137/OPCML) in promoting the proliferation and invasiveness of GC.

Genome-wide Exploration of a Pyroptosis-Related Long Non-Coding RNA Signature Associated With the Prognosis and Immune Response in Patients With Bladder Cancer

Background: Bladder cancer (BLCA) is a malignant tumor with a complex molecular mechanism and high recurrence rate in the urinary system. Studies have shown that pyroptosis regulates tumor cell proliferation and metastasis and affects the prognosis of cancer patients. However, the role of pyroptosis-related (PR) genes or long non-coding RNAs (lncRNAs) in BLCA development is not fully understood.

Methods: We comprehensively analyzed the molecular biological characteristics of PR genes in BLCA, including copy number variation, mutations, expression and prognostic value based on TCGA database. We then identified PR lncRNAs with prognostic value based on the expression of PR genes and performed a consistent clustering analysis of 407 BLCA patients according to the expression of prognosis-related PR lncRNAs and identified two clusters. The least absolute shrinkage and selection operator (LASSO) regression was used to establish a PR lncRNA signature and calculate the risk score associated with the prognosis of patients with BLCA. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were used to evaluate the possible functions of PR lncRNA signature. We also evaluated the relationship between the risk score and tumor immune microenvironment (TIME).

Results: A total of 33 PR genes were obtained in our study and 194 prognosis-related PR lncRNAs were identified. We also constructed a signature consisting of eight-PR-lncRNAs and divided patients into high- and low-risk groups. The overall survival rate of patients with a high risk was significantly lower than patients with a low risk. The risk score was significantly correlated with the degree of infiltration of multiple immune cell subtypes and positively correlated with multiple immune checkpoint genes expression in BLCA. Enrichment analyses showed that these lncRNAs are involved in human immune regulatory functions and immune-related pathways.

Conclusion: Our study comprehensively studied the molecular biological characteristics of PR genes BLCA, and the eight-PR-lncRNA signature we identified might play a crucial role in tumor immunity and may be able to predict the prognosis of BLCA patients, providing a theoretical basis for an in-depth study of the relationship between the prognosis and TIME.

mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes

Background

Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown.

Methods

Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC.

Results

We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes: low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients.

Conclusions

These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.