Identification of candidate diagnostic and prognostic biomarkers for human prostate cancer: RPL22L1 and RPS21

RPL22L1, a novel candidate oncogene promotes temozolomide resistance by activating STAT3 in glioblastoma

Aggressiveness and drug resistance are major challenges in the clinical treatment of glioblastoma (GBM). Our previously research reported a novel candidate oncogene ribosomal protein L22 like 1 (RPL22L1). The aim of this study was to elucidate the potential role and mechanism of RPL22L1 in progression and temozolomide (TMZ) resistance of GBM. Online database, tissue microarrays and clinical tissue specimens were used to evaluate the expression and clinical implication of RPL22L1 in GBM. We performed cell function assays, orthotopic and subcutaneous xenograft tumor models to evaluate the effects and molecular mechanisms of RPL22L1 on GBM. RPL22L1 expression was significantly upregulated in GBM and associated with poorer prognosis. RPL22L1 overexpression enhanced GBM cell proliferation, migration, invasion, TMZ resistance and tumorigenicity, which could be reduced by RPL22L1 knockdown. Further, we found RPL22L1 promoted mesenchymal phenotype of GBM and the impact of these effects was closely related to EGFR/STAT3 pathway. Importantly, we observed that STAT3 specific inhibitor (Stattic) significantly inhibited the malignant functions of RPL22L1, especially on TMZ resistance. RPL22L1 overexpressed increased combination drug sensitive of Stattic and TMZ both in vitro and in vivo. Moreover, Stattic effectively restored the sensitive of RPL22L1 induced TMZ resistance in vitro and in vivo. Our study identified a novel candidate oncogene RPL22L1 which promoted the GBM malignancy through STAT3 pathway. And we highlighted that Stattic combined with TMZ therapy might be an effective treatment strategy in RPL22L1 high-expressed GBM patients.

Joint analysis of WES and RNA-Seq identify signature genes related to metastasis in prostate cancer

Prostate cancer (PCa) has a certain degree of heritability, and metastasis occurs as cancer progresses. However, its underlying mechanism remains largely unknown. We sequenced four cases of cancer without metastasis, four metastatic cancer, and four benign hyperplasia tissues as controls. A total of 1839 damaging mutations were identified. Pathway analysis, gene clustering, and weighted gene co-expression network analysis were employed to find characteristics associated with metastasis. Chr19 had the most mutation density and 1p36 had the highest mutation frequency across the genome. These mutations occurred in 1630 genes, including the most frequently mutated genes TTN and PLEC, and dozens of metastasis-related genes, such as FOXA1, NCOA1, CD34, and BRCA2. Ras signalling and arachidonic acid metabolism were uniquely enriched in metastatic cancer. Gene programmes 10 and 11 showed the signatures indicating the occurrence of metastasis better. A module (135 genes) was specifically associated with metastasis. Of them, 67.41% reoccurred in program 10, with 26 genes further retained as the signature genes related to PCa metastasis, including AGR3, RAPH1, SOX14, DPEP1, and UBL4A. Our study provides new molecular perspectives on PCa metastasis. The signature genes and pathways could be served as potential therapeutic targets for metastasis or cancer progression.

SpiderLearner: An ensemble approach to Gaussian graphical model estimation

Gaussian graphical models (GGMs) are a popular form of network model in which nodes represent features in multivariate normal data and edges reflect conditional dependencies between these features. GGM estimation is an active area of research. Currently available tools for GGM estimation require investigators to make several choices regarding algorithms, scoring criteria, and tuning parameters. An estimated GGM may be highly sensitive to these choices, and the accuracy of each method can vary based on structural characteristics of the network such as topology, degree distribution, and density. Because these characteristics are a priori unknown, it is not straightforward to establish universal guidelines for choosing a GGM estimation method. We address this problem by introducing SpiderLearner, an ensemble method that constructs a consensus network from multiple estimated GGMs. Given a set of candidate methods, SpiderLearner estimates the optimal convex combination of results from each method using a likelihood-based loss function.K $$ K $$ -fold cross-validation is applied in this process, reducing the risk of overfitting. In simulations, SpiderLearner performs better than or comparably to the best candidate methods according to a variety of metrics, including relative Frobenius norm and out-of-sample likelihood. We apply SpiderLearner to publicly available ovarian cancer gene expression data including 2013 participants from 13 diverse studies, demonstrating our tool's potential to identify biomarkers of complex disease. SpiderLearner is implemented as flexible, extensible, open-source code in the R package ensembleGGM at https://github.com/katehoffshutta/ensembleGGM.

Ribosomal protein L22-like1 promotes prostate cancer progression by activating PI3K/Akt/mTOR signalling pathway

Prostate cancer (PCa) is one of the most common malignancies in men. Ribosomal protein L22-like1 (RPL22L1), a component of the ribosomal 60 S subunit, is associated with cancer progression, but the role and potential mechanism of RPL22L1 in PCa remain unclear. The aim of this study was to investigate the role of RPL22L1 in PCa progression and the mechanisms involved. Bioinformatics and immunohistochemistry analysis showed that the expression of RPL22L1 was significantly higher in PCa tissues than in normal prostate tissues. The cell function analysis revealed that RPL22L1 significantly promoted the proliferation, migration and invasion of PCa cells. The data of xenograft tumour assay suggested that the low expression of RPL22L1 inhibited the growth and invasion of PCa cells in vivo. Mechanistically, the results of Western blot proved that RPL22L1 activated PI3K/Akt/mTOR pathway in PCa cells. Additionally, LY294002, an inhibitor of PI3K/Akt pathway, was used to block this pathway. The results showed that LY294002 remarkably abrogated the oncogenic effect of RPL22L1 on PCa cell proliferation and invasion. Taken together, our study demonstrated that RPL22L1 is a key gene in PCa progression and promotes PCa cell proliferation and invasion via PI3K/Akt/mTOR pathway, thus potentially providing a new target for PCa therapy.

Microcystin‑leucine arginine promotes colorectal cancer cell proliferation by activating the PI3K/Akt/Wnt/β‑catenin pathway

Microcystin‑leucine arginine (MC‑LR) is an environmental toxin produced by cyanobacteria and is considered to be a potent carcinogen. However, to the best of our knowledge, the effect of MC‑LR on colorectal cancer (CRC) cell proliferation has never been studied. The aim of the present study was to investigate the effect of MC‑LR on CRC cell proliferation and the underlying mechanisms. Firstly, a Cell Counting Kit‑8 (CCK‑8) assay was conducted to determine cell viability at different concentrations, and 50 nM MC‑LR was chosen for further study. Subsequently, a longer CCK‑8 assay and a cell colony formation assay showed that MC‑LR promoted SW620 and HT29 cell proliferation. Furthermore, western blotting analysis showed that MC‑LR significantly upregulated protein expression of PI3K, p‑Akt (Ser473), p‑GSK3β (Ser9), β‑catenin, c‑myc and cyclin D1, suggesting that MC‑LR activated the PI3K/Akt and Wnt/β‑catenin pathways in SW620 and HT29 cells. Finally, the pathway inhibitors LY294002 and ICG001 were used to validate the role of the PI3K/Akt and Wnt/β‑catenin pathways in MC‑LR‑accelerated cell proliferation. The results revealed that MC‑LR activated Wnt/β‑catenin through the PI3K/Akt pathway to promote cell proliferation. Taken together, these data showed that MC‑LR promoted CRC cell proliferation by activating the PI3K/Akt/Wnt/β‑catenin pathway. The present study provided a novel insight into the toxicological mechanism of MC‑LR.

The Role of Alternative Splicing Factors hnRNP G and Fox-2 in the Progression and Prognosis of Esophageal Cancer

Aim. Alternative splicing (AS) has been widely demonstrated in the occurrence and progression of many cancers. Nevertheless, the involvement of cancer-associated splicing factors in the development of esophageal carcinoma (ESCA) remains to be explored. Method. RNA-Seq data and the corresponding clinical information of the ESCA cohort were downloaded from The Cancer Genome Atlas database. Bioinformatics methods were used to further analyzed the differently expressed AS (DEAS) events and their splicing network. Kaplan–Meier, Cox regression, and unsupervised cluster analyses were used to assess the association between AS events and clinical characteristics of ESCA patients. The splicing factors screened out were verified in vitro at the cellular level. Results. A total of 50,342 AS events were identified, of which 3,988 were DEAS events and 46 of these were associated with overall survival (OS) of ESCA patients, with a 5-year OS rate of 0.941. By constructing a network of AS events with survival-related splicing factors, the AS factors related to prognosis can be further identified. In vitro experiments and database analysis confirmed that the high expression of hnRNP G in ESCA is related to the high invasion ability of ESCA cells and the poor prognosis of ESCA patients. In contrast, the low expression of fox-2 in esophageal cancer is related to a better prognosis. Conclusion. ESCA-associated AS factors hnRNP G and Fox-2 are of great value in deciphering the underlying mechanisms of AS in ESCA and providing clues for therapeutic goals for further validation.

Comparative proteomic analysis of insulin receptor isoform A and B signaling

The insulin receptor (IR) gene undergoes differential splicing generating two IR isoforms, IR-A and IR-B. The roles of IR-A in cancer and of IR-B in metabolic regulation are well known but the molecular mechanisms responsible for their different biological effects are poorly understood. We aimed to identify different or similar protein substrates and signaling linked to each IR isoforms. We employed mouse fibroblasts lacking IGF1R gene and expressing exclusively either IR-A or IR-B. By proteomic analysis a total of 2530 proteins were identified and quantified. Proteins and pathways mostly associated with insulin-activated IR-A were involved in cancer, stemness and interferon signaling. Instead, proteins and pathways associated with insulin-stimulated IR-B-expressing cells were mostly involved in metabolic or tumor suppressive functions. These results show that IR-A and IR-B recruit partially different multiprotein complexes in response to insulin, suggesting partially different functions of IR isoforms in physiology and in disease.

Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells

Glioblastoma (GBM) is characterized by exceptionally high intratumoral heterogeneity. However, the molecular mechanisms underlying the origin of different GBM cell populations remain unclear. Here, we found that the compositions of ribosomes of GBM cells in the tumour core and edge differ due to alternative RNA splicing. The acidic pH in the core switches before messenger RNA splicing of the ribosomal gene RPL22L1 towards the RPL22L1b isoform. This allows cells to survive acidosis, increases stemness and correlates with worse patient outcome. Mechanistically, RPL22L1b promotes RNA splicing by interacting with lncMALAT1 in the nucleus and inducing its degradation. Contrarily, in the tumour edge region, RPL22L1a interacts with ribosomes in the cytoplasm and upregulates the translation of multiple messenger RNAs including TP53. We found that the RPL22L1 isoform switch is regulated by SRSF4 and identified a compound that inhibits this process and decreases tumour growth. These findings demonstrate how distinct GBM cell populations arise during tumour growth. Targeting this mechanism may decrease GBM heterogeneity and facilitate therapy.

Ribosomal protein L22-like1 (RPL22L1) mediates sorafenib sensitivity via ERK in hepatocellular carcinoma

Precision medicine in hepatocellular carcinoma (HCC) relies on validated biomarkers that help subgroup patients for targeted treatment. Here, we identified a novel candidate oncogene, ribosomal protein L22-like1 (RPL22L1), which was markedly elevated in HCC, contributed to HCC malignancy and adverse patient survival. Functional studies indicated RPL22L1 overexpression accelerated cell proliferation, migration, invasion and sorafenib resistance. Mechanism studies revealed that RPL22L1 activated ERK to induce atypical epithelial-to-mesenchymal transition (EMT) progress. Importantly, the ERK inhibitor (ERKi) could potentiate sorafenib efficiency in RPL22L1-high HCC cells. In summary, these data uncover RPL22L1 is a potential marker to guide precision therapy for utilizing ERKi to enhance the sorafenib efficacy in RPL22L1-high HCC patients.

Construction of an Epithelial-Mesenchymal Transition-Related Model for Clear Cell Renal Cell Carcinoma Prognosis Prediction

Background. A rising amount of data demonstrates that the epithelial-mesenchymal transition (EMT) in clear cell renal cell carcinomas (ccRCC) is connected with the advancement of the cancer. In order to understand the role of EMT in ccRCC, it is critical to integrate molecules involved in EMT into prognosis prediction. The objective of this project was to establish a prognosis prediction model using genes associated with EMT in ccRCC. Methods. We acquired the mRNA expression profiles and clinical information about ccRCC from TCGA database. In this study, we measured differentially expressed EMT-related genes (DEEGs) by two comparison groups (tumor versus normal tissues; “stages I-II” versus “stages III-IV” tumor tissues). Based on classification and regression random forest models, we identified the most important DEEGs in predicting prognosis. Afterwards, a risk-score model was created using the identified important DEEGs. The prediction ability of the risk-score model was calculated by the area under the curve (AUC). A nomogram for prognosis prediction was built using the risk-score in combination with clinical factors. Results. Among the 72 DEEGs, the classification and regression random forest models identified six hub genes (DKK1, DLX4, IL6, KCNN4, RPL22L1, and SPDEF), which exhibited the highest importance values in both models. Through the expression of these six hub genes, a novel risk-score was developed for the prognosis prediction of ccRCC. ROC curves showed the risk-score performed well in both the training (0.749) and testing (0.777) datasets. According to the survival analysis, individuals who were separated into high/low-risk groups had statistically different outcomes in terms of prognosis. Besides, the risk-score model also showed outstanding ability in assessing the progression of ccRCC after treatment. In terms of nomogram, the concordance index (C-index) was 0.79. Additionally, we predicted the differences in response to chemotherapy drugs among patients from low- and high-risk groups. Conclusion. Gene signatures related to EMT could be useful in predicting ccRCC prognosis.

Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma

BACKGROUND

The dysfunction of RNA binding proteins (RBPs) is associated with various inflammation and cancer. The occurrence and progression of tumors are closely related to the abnormal expression of RBPs. There are few studies on RBPs in clear cell renal carcinoma (ccRCC), which allows us to explore the role of RBPs in ccRCC.

METHODS

We obtained the gene expression data and clinical data of ccRCC from the Cancer Genome Atlas (TCGA) database and extracted all the information of RBPs. We performed differential expression analysis of RBPs. Risk model were constructed based on the differentially expressed RBPs (DERBPs). The expression levels of model markers were examined by reverse transcription-quantitative PCR (RT-qPCR) and analyzed for model-clinical relevance. Finally, we mapped the model's nomograms to predict the 1, 3 and 5-year survival rates for ccRCC patients.

RESULTS

The results showed that the five-year survival rate for the high-risk group was 40.2% (95% CI = 0.313 ~ 0.518), while the five-year survival rate for the low-risk group was 84.3% (95% CI = 0.767 ~ 0.926). The ROC curves (AUC = 0.748) also showed that our model had stable predictive power. Further RT-qPCR results were in accordance with our analysis (p < 0.05). The results of the independent prognostic analysis showed that the model could be an independent prognostic factor for ccRCC. The results of the correlation analysis also demonstrated the good predictive ability of the model.

CONCLUSION

In summary, the 4-RBPs (EZH2, RPL22L1, RNASE2, U2AF1L4) risk model could be used as a prognostic indicator of ccRCC. Our study provides a possibility for predicting the survival of ccRCC.

Deregulation of ribosomal proteins in human cancers

The ribosome, the site for protein synthesis, is composed of ribosomal RNAs (rRNAs) and ribosomal proteins (RPs). The latter have been shown to have many ribosomal and extraribosomal functions. RPs are implicated in a variety of pathological processes, especially tumorigenesis and cell transformation. In this review, we will focus on the recent advances that shed light on the effects of RPs deregulation in different types of cancer and their roles in regulating the tumor cell fate.

Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma

Background

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy with varied outcomes. However, the fundamental mechanisms remain to be fully defined.

Aim

We aimed to identify core differentially co-expressed hub genes and perturbed pathways relevant to the pathogenesis and prognosis of DLBCL.

Methods

We retrieved the raw gene expression profile and clinical information of GSE12453 from the Gene Expression Omnibus (GEO) database. We used integrated bioinformatics analysis to identify differentially co-expressed genes. The CIBERSORT analysis was also applied to predict tumor-infiltrating immune cells (TIICs) in the GSE12453 dataset. We performed survival and ssGSEA (single-sample Gene Set Enrichment Analysis) (for TIICs) analyses and validated the hub genes using GEPIA2 and an independent GSE31312 dataset.

Results

We identified 46 differentially co-expressed hub genes in the GSE12453 dataset. Gene expression levels and survival analysis found 15 differentially co-expressed core hub genes. The core genes prognostic values and expression levels were further validated in the GEPIA2 database and GSE31312 dataset to be reliable (p < 0.01). The core genes’ main KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichments were Ribosome and Coronavirus disease-COVID-19. High expressions of the 15 core hub genes had prognostic value in DLBCL. The core genes showed significant predictive accuracy in distinguishing DLBCL cases from non-tumor controls, with the area under the curve (AUC) ranging from 0.992 to 1.00. Finally, CIBERSORT analysis on GSE12453 revealed immune cells, including activated memory CD4⁺ T cells and M0, M1, and M2-macrophages as the infiltrates in the DLBCL microenvironment.

Conclusion

Our study found differentially co-expressed core hub genes and relevant pathways involved in ribosome and COVID-19 disease that may be potential targets for prognosis and novel therapeutic intervention in DLBCL.

A genome-wide analysis of the molecular alterations occurring in the adenomatous and carcinomatous components of the same tumor based on the adenoma-carcinoma sequence

Identification of molecular alterations occurring in the adenomatous and carcinomatous components within the same tumor would greatly enhance understanding of the neoplastic progression of colorectal cancer. We examined somatic copy number alterations (SCNAs) and mRNA expression at the corresponding loci involved in the adenoma–carcinoma sequence in the isolated adenomatous and cancer glands of the same tumor in 15 cases of microsatellite‐stable “carcinoma in adenoma,” using genome‐wide SNP and global gene expression arrays. Multiple copy‐neutral loss of heterozygosity events were detected at 4q13.2, 15q15.1, and 14q24.3 in the adenomatous component and at 4q13.2, 15q15.1, and 14q24.3 in the carcinomatous component. There were significant differences in the copy number (CN) gain frequencies at 20q11.21–q13.33, 8q13.3, 8p23.1, and 8q21.2–q22.2 between the adenomatous and carcinomatous components. Finally, we found a high frequency of five genotypes involving CN gain with upregulated expression of the corresponding gene (RPS21, MIR3654, RSP20, SNORD54, or ASPH) in the carcinomatous component, whereas none of these genotypes were detected in the adenomatous component. This finding is interesting in that CN gain with upregulated gene expression may enhance gene function and play a crucial role in the progression of an adenoma into a carcinomatous lesion.

Transcriptomic Analysis of Gene Networks Regulated by U11 Small Nuclear RNA in Bladder Cancer

Small nuclear RNA is a class of non-coding RNA that widely exist in the nucleus of eukaryotes. Accumulated evidences have shown that small nuclear RNAs are associated with the regulation of gene expression in various tumor types. To explore the gene expression changes and its potential effects mediated by U11 snRNA in bladder cancer cells, U11 snRNA knockout and overexpressed cell lines were constructed and further used to analyze the gene expression changes by RNA sequencing. The differentially expressed genes were found to be mainly enriched in tumor-related pathways both in the U11 knockout and overexpression cell lines, such as NF-kappa B signaling pathway, bladder cancer and PI3K-Akt signaling pathway. Furthermore, alternative splicing events were proposed to participate in the potential regulatory mechanism induced by the U11 knockout or overexpression. In conclusion, U11 may be involved in the regulation of gene expression in bladder cancer cells, which may provide a potentially new biomarker for clinical diagnosis and treatment of bladder cancer.

Correlations Between the Characteristics of Alternative Splicing Events, Prognosis, and the Immune Microenvironment in Breast Cancer

Objective

Alternative splicing (AS) is the mechanism by which a few genes encode numerous proteins, and it redefines the concept of gene expression regulation. Recent studies showed that dysregulation of AS was an important cause of tumorigenesis and microenvironment formation. Therefore, we performed a systematic analysis to examine the role of AS in breast cancer (Breast Cancer, BrCa) progression.

Methods

The present study included 993 BrCa patients from The Cancer Genome Atlas (TCGA) database in the genome-wide analysis of AS events. We used differential and prognostic analyses and found differentially expressed alternative splicing (DEAS) events and independent prognostic factors related to patients' overall survival (OS) and disease-free survival (DFS). We divided the patients into two groups based on these AS events and analyzed their clinical features, molecular subtyping and immune characteristics. We also constructed a splicing factor (SF) regulation network for key AS events and verified the existence of AS events in tissue samples using real-time quantitative PCR.

Results

A total of 678 AS events were identified as differentially expressed, of which 13 and 10 AS events were independent prognostic factors of patients' OS and DFS, respectively. Unsupervised clustering analysis based on these prognostic factors indicated that the Cluster 1 group had a better prognosis and more immune cell infiltration. SFs were significantly related to the expression of AS events, and AA-RPS21 was significantly upregulated in tumors.

Conclusion

Alternative splicing expands the mechanism of breast cancer progression from a new perspective. Notably, alternative splicing may affect the patient's prognosis by affecting the infiltration of immune cells. Our research provides important guidance for subsequent studies of AS in breast cancer.

Disruption of entire Cables2 locus leads to embryonic lethality by diminished Rps21 gene expression and enhanced p53 pathway

In vivo function of CDK5 and Abl enzyme substrate 2 (Cables2), belonging to the Cables protein family, is unknown. Here, we found that targeted disruption of the entire Cables2 locus (Cables2d) caused growth retardation and enhanced apoptosis at the gastrulation stage and then induced embryonic lethality in mice. Comparative transcriptome analysis revealed disruption of Cables2, 50% down-regulation of Rps21 abutting on the Cables2 locus, and up-regulation of p53-target genes in Cables2d gastrulas. We further revealed the lethality phenotype in Rps21-deleted mice and unexpectedly, the exon 1-deleted Cables2 mice survived. Interestingly, chimeric mice derived from Cables2d ESCs carrying exogenous Cables2 and tetraploid wild-type embryo overcame gastrulation. These results suggest that the diminished expression of Rps21 and the completed lack of Cables2 expression are intricately involved in the embryonic lethality via the p53 pathway. This study sheds light on the importance of Cables2 locus in mouse embryonic development.

Proteomics reveals the potential mechanism of Mrps35 controlling Listeria monocytogenes intracellular proliferation in macrophages

Macrophages are sentinels in the organism which can resist and destroy various bacteria through direct phagocytosis. Here, we reported that expression level of mitochondrial ribosomal protein S35 (Mrps35) continued to decrease over infection time after Listeria monocytogenes (L. monocytogenes) infected macrophages. Our results indicated that knockdown Mrps35 increased the load of L. monocytogenes in macrophages. This result supported that Mrps35 played the crucial roles in L. monocytogenes infection. Moreover, we performed the comprehensive proteomics to analyze the differentially expressed protein of wild type and Mrps35 Knockdown Raw264.7 cells by L. monocytogenes infection over 6 h. Based on the results of mass spectrometry, we presented a wide variety of hypotheses about the mechanism of Mrps35 controlling the L. monocytogenes intracellular proliferation. Among them, experiments confirmed that Mrps35 and 60S ribosomal protein L22-like 1 (Rpl22l1) were a functional correlation or potentially a compensatory mechanism during L. monocytogenes infection. This study provided new insights into understanding that L. monocytogenes infection changed the basic synthesis or metabolism-related proteins of host cells.

Identification of RNA-binding protein SNRPA1 for prognosis in prostate cancer

Prostate cancer is one of the deadliest cancers in men. RNA-binding proteins play a critical role in human cancers; however, whether they have a significant effect on the prognosis of prostate cancer has yet to be elucidated. In the present study, we performed a comprehensive analysis of RNA sequencing and clinical data from the Cancer Genome Atlas dataset and obtained differentially expressed RNA-binding proteins between prostate cancer and benign tissues. We constructed a protein-protein interaction network and Cox regression analyses were conducted to identify prognostic hub RNA-binding proteins. SNRPA1 was associated with the highest risk of poor prognosis and was therefore selected for further analysis. SNRPA1 expression was positively correlated with Gleason score and pathological TNM stage in prostate cancer patients. Furthermore, the expression profile of SNRPA1 was validated using the Oncomine, Human Protein Atlas, and Cancer Cell Line Encyclopedia databases. Meanwhile, the prognostic profile of SNRPA1 was successfully verified in GSE70769. Additionally, the results of molecular experiments revealed the proliferative role of SNRPA1 in prostate cancer cells. In summary, our findings evidenced a relationship between RNA-binding proteins and prostate cancer and indicated the prognostic significance of SNRPA1 in prostate cancer.

Development of prognostic signature based on RNA binding proteins related genes analysis in clear cell renal cell carcinoma

RNA binding proteins (RBPs) play significant roles in the development of tumors. However, a comprehensive analysis of the biological functions of RBPs in clear cell renal cell carcinoma (ccRCC) has not been performed. Our study aimed to construct an RBP-related risk model for prognosis prediction in ccRCC patients. First, RNA sequencing data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database. Three RBP genes (EIF4A1, CARS, and RPL22L1) were validated as prognosis-related hub genes by univariate and multivariate Cox regression analyses and were integrated into a prognostic model by least absolute shrinkage and selection operator (LASSO) Cox regression analysis. According to this model, patients with high risk scores displayed significantly worse overall survival (OS) than those with low risk scores. Moreover, the multivariate Cox analysis results indicated that risk score, tumor grade, and tumor stage were significantly correlated with patient OS. A nomogram was constructed based on the three RBP genes and showed a good ability to predict outcomes in ccRCC patients. In conclusion, this study identified a three-RBP gene risk model for predicting the prognosis of patients, which is conducive to the identification of novel diagnostic and prognostic molecular markers.

Integrated analysis of the functions of RNA binding proteins in clear cell renal cell carcinoma

RNA binding proteins (RBPs) dysregulation is involved in the processes of various tumors. However, the roles of RBPs in clear cell renal cell carcinoma (ccRCC) remain poorly understand. In present study, we first performed consensus clustering and identified two clusters, of which cluster 2 was closely correlated with the malignancy of ccRCC. Differentially expressed RBPs between normal and tumor tissues were obtained, comprising 71 up-regulated and 44 down-regulated ones. Then, ten hub genes were selected and validated using The Human Protein Atlas database and receiver operating characteristic curves, showing good diagnostic value for cancers. Besides, we identified ten RBPs with the most useful prognostic values, and were used to construct a risk score model. The model could be used to stratify patients with different prognosis and phenotype distributions. The model showed good performance and can be used as a complementation for clinical factors to guide clinical practice in the future.

Down-Regulation of Ribosomal Protein RPS21 Inhibits Invasive Behavior of Osteosarcoma Cells Through the Inactivation of MAPK Pathway

Objective

The goal of our present study was to explore the expression level, biological function, and underlying molecular mechanism of ribosomal protein s21 (RPS21) in human osteosarcoma (OS).

Methods

Firstly, we evaluated the expression of RPS21 in OS tissue samples based on the Gene Expression Omnibus (GEO) datasets and also measured the RPS21 expression of OS cell lines (MG63, and U2OS) by quantitative real-time polymerase chain reaction (qRT-PCR). siRNA interference method was used to reduce the expression of RSP21 in the OS cells. Cell Counting Kit-8 (CCK-8), colony formation, wound-healing, and transwell assays were conducted to measure the proliferation, migration, and invasion of OS cells. The mitogen-activated protein kinase (MAPK) pathway-related proteins levels were examined by Western blot.

Results

Our analyses showed that the expression of RPS21 was significantly increased in OS, compared with normal samples. Upregulation of RPS21 was associated with worse outcomes of OS patients. Knockdown of RPS21 suppressed OS cell proliferation, colony-forming ability, migration, and invasion capacities. Moreover, down-regulation of RPS21 inactivated the MAPK signaling pathway.

Conclusion

RPS21 plays an oncogenic candidate in OS development via regulating the activity of MAPK pathway; therefore, it may serve as a novel therapeutic target for OS treatment.