The ribosomal protein S6 in renal cell carcinoma: functional relevance and potential as biomarker

Functional impact of multi-omic interactions in lung cancer

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts

Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Here, we demonstrate strong stratification of 445 renal clear cell carcinoma tumors from The Cancer Genome Atlas (TCGA) into high and low risk groups using LASSO regression on RPPA data. Median cross-validated difference in 5-year overall survival was 32.8%, compared to 25.2% using a published receptor tyrosine kinase (RTK) prognostic signature (median hazard ratios of 3.3 and 2.4, respectively). Applicability and performance of our approach was demonstrated in three additional TCGA cohorts: ovarian serous cystadenocarcinoma (OVCA), sarcoma (SARC), and cutaneous melanoma (SKCM). The data-driven LASSO-based approach is versatile and well-suited for discovery of new protein/disease associations.

Ribosomes and Ribosomal Proteins Promote Plasticity and Stemness Induction in Glioma Cells via Reprogramming

Glioblastoma multiforme (GBM) is a lethal tumor that develops in the adult brain. Despite advances in therapeutic strategies related to surgical resection and chemo-radiotherapy, the overall survival of patients with GBM remains unsatisfactory. Genetic research on mutation, amplification, and deletion in GBM cells is important for understanding the biological aggressiveness, diagnosis, and prognosis of GBM. However, the efficacy of drugs targeting the genetic abnormalities in GBM cells is limited. Investigating special microenvironments that induce chemo-radioresistance in GBM cells is critical to improving the survival and quality of life of patients with GBM. GBM cells acquire and maintain stem-cell-like characteristics via their intrinsic potential and extrinsic factors from their special microenvironments. The acquisition of stem-cell-like phenotypes and aggressiveness may be referred to as a reprogramming of GBM cells. In addition to protein synthesis, deregulation of ribosome biogenesis is linked to several diseases including cancer. Ribosomal proteins possess both tumor-promotive and -suppressive functions as extra-ribosomal functions. Incorporation of ribosomes and overexpression of ribosomal protein S6 reprogram and induce stem-cell-like phenotypes in GBM cells. Herein, we review recent literature and our published data on the acquisition of aggressiveness by GBM and discuss therapeutic options through reprogramming.

Overexpressed p-S6 associates with lymph node metastasis and predicts poor prognosis in non-small cell lung cancer

Background

Ribosomal protein S6 (S6), a downstream effect media of the AKT/mTOR pathway, not only is a part of 40S small subunit of eukaryotic ribosome, but also involves in protein synthesis and cell proliferation during cancer development.

Methods

In present study, we explore the association between phosphorylated S6 (p-S6) protein expression and clinicopathological features as well as prognostic implications in NSCLC. P-S6 was detected in tissue microarrays (TMAs) containing 350 NSCLC, 53 non-cancerous lung tissues (Non-CLT), and 88 cases of matched metastatic lymph node lesions via immunohistochemistry (IHC). Transwell assays and wound healing assay were used to assess the effects of p-S6 inhibition on NSCLC cell metastasis.

Results

The p-S6 expression in NSCLC was more evident than that in Non-CLT (p < 0.05). Compared to NSCLC patients who have no lymph node metastasis (LNM), those with LNM had higher p-S6 expression (p = 0.001). Regardless of lung squamous cell carcinoma (SCC) or adenocarcinoma (ADC), p-S6 was increased obviously in metastatic lymph nodes compared with matched primary cancers (p = 0.001, p = 0.022, respectively). Inhibition of p-S6 decreased the metastasis ability of NSCLC cells. In addition, p-S6 was an independent predicted marker for LNM in patients with NSCLC (p < 0.001). According to survival analysis, patients with highly expressed p-S6 had a lower survival rate compared with that with lower expression (p = 0.013). P-S6 is an unfavorable independent prognostic factor for NSCLC patients (p = 0.011).

Conclusion

Increased expression of p-S6 is not only a novel predictive biomarker of LNM but also poor prognosis in NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09664-4.

Ribosomal proteins induce stem cell-like characteristics in glioma cells as an "extra-ribosomal function"

The characteristic features of plasticity and heterogeneity in glioblastoma (GB) cells cause therapeutic difficulties. GB cells are exposed to various stimuli from the tumor microenvironment and acquire the potential to resist chemoradiotherapy. To investigate how GB cells acquire stem cell-like phenotypes, we focused on ribosomal proteins, because ribosome incorporation has been reported to induce stem cell-like phenotypes in somatic cells. Furthermore, dysregulation of ribosome biogenesis has been reported in several types of cancer. We focused on ribosomal protein S6, which promotes sphere-forming ability and stem cell marker expression in GB cells. We expect that investigation of dysregulation of ribosome biogenesis and extra-ribosomal function in GB will provide new insights about the plasticity, heterogeneity, and therapeutic resistance of GB cells, which can potentially lead to revolutionary therapeutic strategies.

DLK2 Acts as a Potential Prognostic Biomarker for Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis

Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype with a high mortality. It has been reported that delta-like 1 homologue (DLK1) participates in the tumor microenvironmental remodeling of ccRCC, but the relationship between delta-like 2 homologue (DLK2, a DLK1 homologue) and ccRCC is still unclear. Thus, this study aims to investigate the role of DLK2 in the biological function and disease prognosis of ccRCC using bioinformatics analysis. The TNMplot database showed that DLK2 was upregulated in ccRCC tissues. From the UALCAN analysis, the overexpression of DLK2 was associated with advanced stage and high grade in ccRCC. Moreover, the Kaplan-Meier plotter (KM Plotter) database showed that DLK2 upregulation was associated with poor survival outcome in ccRCC. By the LinkedOmics analysis, DLK2 signaling may participated in the modulation of ccRCC extracellular matrix (ECM), cell metabolism, ribosome biogenesis, TGF-β signaling and Notch pathway. Besides, Tumor Immune Estimation Resource (TIMER) analysis showed that the macrophage and CD8⁺ T cell infiltrations were associated with good prognosis in ccRCC patients. Finally, DLK2 overexpression was associated with the reduced macrophage recruitments and the M1–M2 polarization of macrophage in ccRCC tissues. Together, DLK2 may acts as a novel biomarker, even therapeutic target in ccRCC. However, this study lacks experimental validation, and further studies are required to support this viewpoint.

Ribosomal Protein S6: A Potential Therapeutic Target against Cancer?

Ribosomal protein S6 (RPS6) is a component of the 40S small ribosomal subunit and participates in the control of mRNA translation. Additionally, phospho (p)-RPS6 has been recognized as a surrogate marker for the activated PI3K/AKT/mTORC1 pathway, which occurs in many cancer types. However, downstream mechanisms regulated by RPS6 or p-RPS remains elusive, and the therapeutic implication of RPS6 is underappreciated despite an approximately half a century history of research on this protein. In addition, substantial evidence from RPS6 knockdown experiments suggests the potential role of RPS6 in maintaining cancer cell proliferation. This motivates us to investigate the current knowledge of RPS6 functions in cancer. In this review article, we reviewed the current information about the transcriptional regulation, upstream regulators, and extra-ribosomal roles of RPS6, with a focus on its involvement in cancer. We also discussed the therapeutic potential of RPS6 in cancer.

TMEM176B Regulates AKT/mTOR Signaling and Tumor Growth in Triple-Negative Breast Cancer

TMEM176B is a member of the membrane spanning 4-domains (MS4) family of transmembrane proteins, and a putative ion channel that is expressed in immune cells and certain cancers. We aimed to understand the role of TMEM176B in cancer cell signaling, gene expression, cell proliferation, and migration in vitro, as well as tumor growth in vivo. We generated breast cancer cell lines with overexpressed and silenced TMEM176B, and a therapeutic antibody targeting TMEM176B. Proliferation and migration assays were performed in vitro, and tumor growth was evaluated in vivo. We performed gene expression and Western blot analyses to identify the most differentially regulated genes and signaling pathways in cells with TMEM176B overexpression and silencing. Silencing TMEM176B or inhibiting it with a therapeutic antibody impaired cell proliferation, while overexpression increased proliferation in vitro. Syngeneic and xenograft tumor studies revealed the attenuated growth of tumors with TMEM176B gene silencing compared with controls. We found that the AKT/mTOR signaling pathway was activated or repressed in cells overexpressing or silenced for TMEM176B, respectively. Overall, our results suggest that TMEM176B expression in breast cancer cells regulates key signaling pathways and genes that contribute to cancer cell growth and progression, and is a potential target for therapeutic antibodies.

modelBuildR: an R package for model building and feature selection with erroneous classifications

BACKGROUND

Model building is a crucial part of omics based biomedical research to transfer classifications and obtain insights into underlying mechanisms. Feature selection is often based on minimizing error between model predictions and given classification (maximizing accuracy). Human ratings/classifications, however, might be error prone, with discordance rates between experts of 5-15%. We therefore evaluate if a feature pre-filtering step might improve identification of features associated with true underlying groups.

METHODS

Data was simulated for up to 100 samples and up to 10,000 features, 10% of which were associated with the ground truth comprising 2-10 normally distributed populations. Binary and semi-quantitative ratings with varying error probabilities were used as classification. For feature preselection standard cross-validation (V2) was compared to a novel heuristic (V1) applying univariate testing, multiplicity adjustment and cross-validation on switched dependent (classification) and independent (features) variables. Preselected features were used to train logistic regression/linear models (backward selection, AIC). Predictions were compared against the ground truth (ROC, multiclass-ROC). As use case, multiple feature selection/classification methods were benchmarked against the novel heuristic to identify prognostically different G-CIMP negative glioblastoma tumors from the TCGA-GBM 450 k methylation array data cohort, starting from a fuzzy umap based rough and erroneous separation.

RESULTS

V1 yielded higher median AUC ranks for two true groups (ground truth), with smaller differences for true graduated differences (3-10 groups). Lower fractions of models were successfully fit with V1. Median AUCs for binary classification and two true groups were 0.91 (range: 0.54-1.00) for V1 (Benjamini-Hochberg) and 0.70 (0.28-1.00) for V2, 13% (n = 616) of V2 models showed AUCs < = 50% for 25 samples and 100 features. For larger numbers of features and samples, median AUCs were 0.75 (range 0.59-1.00) for V1 and 0.54 (range 0.32-0.75) for V2. In the TCGA-GBM data, modelBuildR allowed best prognostic separation of patients with highest median overall survival difference (7.51 months) followed a difference of 6.04 months for a random forest based method.

CONCLUSIONS

The proposed heuristic is beneficial for the retrieval of features associated with two true groups classified with errors. We provide the R package modelBuildR to simplify (comparative) evaluation/application of the proposed heuristic (http://github.com/mknoll/modelBuildR).

Integrating Phenotypic Search and Phosphoproteomic Profiling of Active Kinases for Optimization of Drug Mixtures for RCC Treatment

Combined application of multiple therapeutic agents presents the possibility of enhanced efficacy and reduced development of resistance. Definition of the most appropriate combination for any given disease phenotype is challenged by the vast number of theoretically possible combinations of drugs and doses, making extensive empirical testing a virtually impossible task. We have used the streamlined-feedback system control (s-FSC) technique, a phenotypic approach, which converges to optimized drug combinations (ODC) within a few experimental steps. Phosphoproteomics analysis coupled to kinase activity analysis using the novel INKA (integrative inferred kinase activity) pipeline was performed to evaluate ODC mechanisms in a panel of renal cell carcinoma (RCC) cell lines. We identified different ODC with up to 95% effectivity for each RCC cell line, with low doses (ED_5-25) of individual drugs. Global phosphoproteomics analysis demonstrated inhibition of relevant kinases, and targeting remaining active kinases with additional compounds improved efficacy. In addition, we identified a common RCC ODC, based on kinase activity data, to be effective in all RCC cell lines under study. Combining s-FSC with a phosphoproteomic profiling approach provides valuable insight in targetable kinase activity and allows for the identification of superior drug combinations for the treatment of RCC.

Knockdown of ribosomal protein S6 suppresses proliferation, migration, and invasion in epithelial ovarian cancer

Background

Ovarian cancer typically is diagnosed late because insensitivity and lack of specificity of current biomarkers prior to its clinical detection. Ribosomal protein S6 (RPS6) is a ribosomal protein involved in the ribosomal 40S subunit, but its biological role in epithelial ovarian cancer (EOC) is still unknown.

Results

RPS6 was elevated in EOC compared to normal ovarian tissues and adenomas. Higher expression of RPS6 predicted worse prognosis in EOC. The level of RPS6 was correlated with clinical stage, histological type and pathological grade. Knockdown of RPS6 reduced the proliferation of ovarian cancer cell lines SKOV-3 and HO8910, and inhibit the migration and invasion ability. It revealed that cells arrested at G0G1 phase after knockdown of RPS6, and the expressions of CyclinD1, Cyclin E, CDK2, CDK4, CDK6 and pRb were also reduced.

Conclusions

RPS6 is involved in EOC and knockdown of RPS6 could inhibit the proliferation, invasion and migration ability of EOC in vitro by inducing G0/G1 phase arrest. RPS6 is expected to be a novel biomarker and molecular target to the EOC.

Ribosomal protein S6 promotes stem-like characters in glioma cells

Glioblastoma multiforme (GBM), a lethal brain tumor developing in the white matter of the adult brain, contains a small population of GBM stem cells (GSCs), which potentially cause chemotherapeutic resistance and tumor recurrence. However, the mechanisms underlying the pathogenesis and maintenance of GSCs remain largely unknown. A recent study reported that incorporation of ribosomes and ribosomal proteins into somatic cells promoted lineage trans-differentiation toward multipotency. This study aimed to investigate the mechanism underlying stemness acquisition in GBM cells by focusing on 40S ribosomal protein S6 (RPS6). RPS6 was significantly upregulated in high-grade glioma and localized at perivascular, perinecrotic, and border niches in GBM tissues. siRNA-mediated RPS6 knock-down significantly suppressed the characteristics of GSCs, including their tumorsphere potential and GSC marker expression; STAT3 was downregulated in GBM cells. RPS6 overexpression enhanced the tumorsphere potential of GSCs and these effects were attenuated by STAT3 inhibitor (AG490). Moreover, RPS6 expression was significantly correlated with SOX2 expression in different glioma grades. Immunohistochemistry data herein indicated that RPS6 was predominant in GSC niches, concurrent with the data from IVY GAP databases. Furthermore, RPS6 and other ribosomal proteins were upregulated in GSC-predominant areas in this database. The present results indicate that, in GSC niches, ribosomal proteins play crucial roles in the development and maintenance of GSCs and are clinically associated with chemoradioresistance and GBM recurrence.

Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations

BACKGROUND

Gene regulatory networks (GRNs) can be inferred from both gene expression data and genetic perturbations. Under different conditions, the gene data of the same gene set may be different from each other, which results in different GRNs. Detecting structural difference between GRNs under different conditions is of great significance for understanding gene functions and biological mechanisms.

RESULTS

In this paper, we propose a Bayesian Fused algorithm to jointly infer differential structures of GRNs under two different conditions. The algorithm is developed for GRNs modeled with structural equation models (SEMs), which makes it possible to incorporate genetic perturbations into models to improve the inference accuracy, so we name it BFDSEM. Different from the naive approaches that separately infer pair-wise GRNs and identify the difference from the inferred GRNs, we first re-parameterize the two SEMs to form an integrated model that takes full advantage of the two groups of gene data, and then solve the re-parameterized model by developing a novel Bayesian fused prior following the criterion that separate GRNs and differential GRN are both sparse.

CONCLUSIONS

Computer simulations are run on synthetic data to compare BFDSEM to two state-of-the-art joint inference algorithms: FSSEM and ReDNet. The results demonstrate that the performance of BFDSEM is comparable to FSSEM, and is generally better than ReDNet. The BFDSEM algorithm is also applied to a real data set of lung cancer and adjacent normal tissues, the yielded normal GRN and differential GRN are consistent with the reported results in previous literatures. An open-source program implementing BFDSEM is freely available in Additional file 1.

Fluvastatin potentiates anticancer activity of vorinostat in renal cancer cells

Drug repositioning is an emerging approach to developing novel cancer treatments. Vorinostat is a histone deacetylase inhibitor approved for cancer treatment, but it could attenuate its anticancer activity by activating the mTOR pathway. The HMG‐CoA reductase inhibitor fluvastatin reportedly activates the mTOR inhibitor AMP‐activated protein kinase (AMPK), and we thought that it would potentiate vorinostat's anticancer activity in renal cancer cells. The combination of vorinostat and fluvastatin induced robust apoptosis and inhibited renal cancer growth effectively both in vitro and in vivo. Vorinostat activated the mTOR pathway, as evidenced by the phosphorylation of ribosomal protein S6, and fluvastatin inhibited this phosphorylation by activating AMPK. Fluvastatin also enhanced vorinostat‐induced histone acetylation. Furthermore, the combination induced endoplasmic reticulum (ER) stress that was accompanied by aggresome formation. We also found that there was a positive feedback cycle among AMPK activation, histone acetylation, and ER stress induction. This is the first study to report the beneficial combined effect of vorinostat and fluvastatin in cancer cells.

A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin

BACKGROUND

The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance.

METHODS

Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy.

RESULTS

Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. We analyzed human homologs of yeast genes exhibiting gene-doxorubicin interaction in cancer pharmacogenomics data to predict causality for differential gene expression associated with doxorubicin cytotoxicity in cancer cells. This analysis suggested conserved cellular responses to doxorubicin due to influences of homologous recombination, sphingolipid homeostasis, telomere tethering at nuclear periphery, actin cortical patch localization, and other gene functions.

CONCLUSIONS

Warburg status alters the genetic network required for yeast to buffer doxorubicin toxicity. Integration of yeast phenomic and cancer pharmacogenomics data suggests evolutionary conservation of gene-drug interaction networks and provides a new experimental approach to model their influence on chemotherapy response. Thus, yeast phenomic models could aid the development of precision oncology algorithms to predict efficacious cytotoxic drugs for cancer, based on genetic and metabolic profiles of individual tumors.

Aberrant modulation of ribosomal protein S6 phosphorylation confers acquired resistance to MAPK pathway inhibitors in BRAF-mutant melanoma

BRAF and MEK inhibitors have shown remarkable clinical efficacy in BRAF-mutant melanoma; however, most patients develop resistance, which limits the clinical benefit of these agents. In this study, we found that the human melanoma cell clones, A375-DR and A375-TR, with acquired resistance to BRAF inhibitor dabrafenib and MEK inhibitor trametinib, were cross resistant to other MAPK pathway inhibitors. In these resistant cells, phosphorylation of ribosomal protein S6 (rpS6) but not phosphorylation of ERK or p90 ribosomal S6 kinase (RSK) were unable to be inhibited by MAPK pathway inhibitors. Notably, knockdown of rpS6 in these cells effectively downregulated G₁ phase-related proteins, including RB, cyclin D1, and CDK6, induced cell cycle arrest, and inhibited proliferation, suggesting that aberrant modulation of rpS6 phosphorylation contributed to the acquired resistance. Interestingly, RSK inhibitor had little effect on rpS6 phosphorylation and cell proliferation in resistant cells, whereas P70S6K inhibitor showed stronger inhibitory effects on rpS6 phosphorylation and cell proliferation in resistant cells than in parental cells. Thus regulation of rpS6 phosphorylation, which is predominantly mediated by BRAF/MEK/ERK/RSK signaling in parental cells, was switched to mTOR/P70S6K signaling in resistant cells. Furthermore, mTOR inhibitors alone overcame acquired resistance and rescued the sensitivity of the resistant cells when combined with BRAF/MEK inhibitors. Taken together, our findings indicate that RSK-independent phosphorylation of rpS6 confers resistance to MAPK pathway inhibitors in BRAF-mutant melanoma, and that mTOR inhibitor-based regimens may provide alternative strategies to overcome this acquired resistance.

The roles of moonlight ribosomal proteins in the development of human cancers

"Moonlighting protein" is a term used to define a single protein with multiple functions and different activities that are not derived from gene fusions, multiple RNA splicing, or the proteolytic activity of promiscuous enzymes. Different proteinous constituents of ribosomes have been shown to have important moonlighting extra-ribosomal functions. In this review, we introduce the impact of key moonlight ribosomal proteins and dependent signal transduction in the initiation and progression of various cancers. As a future perspective, the potential role of these moonlight ribosomal proteins in the diagnosis, prognosis, and development of novel strategies to improve the efficacy of therapies for human cancers has been suggested.

Paternal factors and embryonic development: Role in recurrent pregnancy loss

The events occurring at the maternal-foetal interface define a successful pregnancy but the current paradigm has shifted towards assessing the contribution of spermatozoa for embryogenesis. Spermatozoa with defective DNA integrity may fertilise the oocyte but affect subsequent embryonic development. The present case-control study was conducted in male partners of couples experiencing recurrent pregnancy loss (RPL) to assess the gene expression of spermatozoal FOXG1, SOX3, OGG1, PARP1, RPS6, RBM9, RPS17 and RPL29. This was correlated with reactive oxygen species (ROS) levels and DNA Fragmentation Index (DFI). Semen samples were obtained from 60 cases and 30 fertile controls. Gene expression was done by qPCR analysis, and relative quantification was calculated by the 2^-ΔΔCt method. Chemiluminescence and the sperm chromatin structure assay were used to measure the ROS and DFI levels respectively. FOXG1, OGG1, RPS6 and RBM9 were seen to be upregulated, while SOX3 and PARP1 were downregulated. Relative expression of SOX3, OGG1, RPS6 and RPS17 showed a significant difference between patients and controls (p < 0.05). RPL patients were seen to have high ROS (>27.8; p = 0.001) and DFI (>30.7; p < 0.0001) with respect to controls. Sperm transcript dysregulation and oxidative DNA damage can be "carried over" after implantation, thus affecting embryogenesis and health of the future progeny.

Growth factor progranulin promotes tumorigenesis of cervical cancer via PI3K/Akt/mTOR signaling pathway

Progranulin (PGRN) is an autocrine growth factor with tumorigenic roles in various tumors including cervical cancer. In this study, we investigated mammalian target of rapamycin (mTOR) signaling in response to PGRN induction and the contribution of the PGRN-stimulated PI3K/Akt/mTOR signaling pathway in the transformation and progression of cervical cancer. Here we identified a strong linkage between PGRN and phosphorylated-mTOR in cervical cancer tissues. PGRN promoted the phosphorylation of mTOR and activated mTOR signaling in human cervical mucosa epithelial cells and cervical cancer cells, and TNFR2 was needed for PGRN-stimulated mTOR signaling. Inhibition of mTOR signaling with rapamycin decreased PGRN-stimulated protein synthesis, transformation and proliferation of cervical cells in vitro, and tumor formation and growth in vivo. Thus, our findings update the signal transduction pathways of PGRN by suggesting that mTOR signaling contributes to PGRN-stimulated carcinogenesis of cervical cancer. Inhibition of PGRN/PI3K/Akt/mTOR signaling may be targeted in treatment of cervical cancer.

The selective MEK1 inhibitor Selumetinib enhances the antitumor activity of everolimus against renal cell carcinoma in vitro and in vivo

Renal cell carcinoma (RCC) is a urologic malignant cancer and often diagnosed at an advanced stage, which results in high mortality. Targeted therapy may improve the quality of life and survival of patients who are not suitable for nephrectomy. Everolimus, an mTOR inhibitor, is currently used as sequential or second-line therapy for RCC refractory to Sunitinib or sorafenib. However, its efficiency is palliative. In this study, we evaluated whether the antitumor activity of everolimus against RCC is enhanced by Selumetinib, a selective MEK1 inhibitor. We discovered that everolimus in combination with Selumetinib synergistically inhibited the proliferation of Caki-1, 786-O and 769-P cells in vitro. Mechanistically, this combination decreased p-RPS6 and p-4E-BP1 dramatically, which causes G1 cell cycle arrest and prevents reactivation of AKT and ERK. In vivo, the antitumor efficacy and pharmacodynamic biomarkers of the combination therapy were recapitulated in Caki-1 xenograft model. In addition, this combination treatment potently inhibited angiogenesis in xenograft models by impairing VEGF secretion from tumor cells. Our findings provide a sound evidence that combination of everolimus and Selumetinib is a potential dual-targeted strategy for renal cell carcinoma.

Detection of RACK1 and CTNNBL1‑induced activation of mouse splenocytes using an immunoprecipitation‑based technique

Tumor cell lysates (TCLs) have been reported to induce antitumor immunity; however, it remains unclear which elements serve a role in this process. The present study identified 768 proteins that were upregulated in TCL prepared from Lewis lung cancer cells compared with the lysate from type II alveolar epithelial cells. Among the proteins that were upregulated in TCL, receptor for activated C kinase 1 (RACK1) and catenin β‑like 1 (CTNNBL1) are closely associated with cell proliferation and the inhibition of apoptosis. To determine the role of these proteins in TCL, a protein extraction method was designed, which was based on immunoprecipitation. Using this method, RACK1 and CTNNBL1 were extracted, whereas the other proteins within the TCL were not affected. The modified TCL exhibited a stronger ability to induce splenocyte apoptosis, whereas the ability to promote cell activation was reduced. These findings suggested that the TCL depends on RACK1 and CTNNBL1 to activate mouse immunocytes, including monocytes and B lymphocytes, and inhibit apoptosis. Therefore, the present study may provide information regarding the composition of TCLs and their positive regulatory effect on immunocytes.

HIF prolyl hydroxylase PHD3 regulates translational machinery and glucose metabolism in clear cell renal cell carcinoma

BACKGROUND

A key feature of clear cell renal cell carcinoma (ccRCC) is the inactivation of the von Hippel-Lindau tumour suppressor protein (pVHL) that leads to the activation of hypoxia-inducible factor (HIF) pathway also in well-oxygenated conditions. Important regulator of HIF-α, prolyl hydroxylase PHD3, is expressed in high amounts in ccRCC. Although several functions and downstream targets for PHD3 in cancer have been suggested, the role of elevated PHD3 expression in ccRCC is not clear.

METHODS

To gain insight into the functions of high PHD3 expression in ccRCC, we used PHD3 knockdown by siRNA in 786-O cells under normoxic and hypoxic conditions and performed discovery mass spectrometry (LC-MS/MS) of the purified peptide samples. The LC-MS/MS results were analysed by label-free quantification of proteome data using a peptide-level expression-change averaging procedure and subsequent gene ontology enrichment analysis.

RESULTS

Our data reveals an intriguingly widespread effect of PHD3 knockdown with 91 significantly regulated proteins. Under hypoxia, the response to PHD3 silencing was wider than under normoxia illustrated by both the number of regulated proteins and by the range of protein expression levels. The main cellular functions regulated by PHD3 expression were glucose metabolism, protein translation and messenger RNA (mRNA) processing. PHD3 silencing led to downregulation of most glycolytic enzymes from glucose transport to lactate production supported by the reduction in extracellular acidification and lactate production and increase in cellular oxygen consumption rate. Moreover, upregulation of mRNA processing-related proteins and alteration in a number of ribosomal proteins was seen as a response to PHD3 silencing. Further studies on upstream effectors of the translational machinery revealed a possible role for PHD3 in regulation of mTOR pathway signalling.

CONCLUSIONS

Our findings suggest crucial involvement of PHD3 in the maintenance of key cellular functions including glycolysis and protein synthesis in ccRCC.

Epsin Family Member 3 and Ribosome-Related Genes Are Associated with Late Metastasis in Estrogen Receptor-Positive Breast Cancer and Long-Term Survival in Non-Small Cell Lung Cancer Using a Genome-Wide Identification and Validation Strategy

BACKGROUND

In breast cancer, gene signatures that predict the risk of metastasis after surgical tumor resection are mainly indicative of early events. The purpose of this study was to identify genes linked to metastatic recurrence more than three years after surgery.

METHODS

Affymetrix HG U133A and Plus 2.0 array datasets with information on metastasis-free, disease-free or overall survival were accessed via public repositories. Time restricted Cox regression models were used to identify genes associated with metastasis during or after the first three years post-surgery (early- and late-type genes). A sequential validation study design, with two non-adjuvantly treated discovery cohorts (n = 409) and one validation cohort (n = 169) was applied and identified genes were further evaluated in tamoxifen-treated breast cancer patients (n = 923), as well as in patients with non-small cell lung (n = 1779), colon (n = 893) and ovarian (n = 922) cancer.

RESULTS

Ten late- and 243 early-type genes were identified in adjuvantly untreated breast cancer. Adjustment to clinicopathological factors and an established proliferation-related signature markedly reduced the number of early-type genes to 16, whereas nine late-type genes still remained significant. These nine genes were associated with metastasis-free survival (MFS) also in a non-time restricted model, but not in the early period alone, stressing that their prognostic impact was primarily based on MFS more than three years after surgery. Four of the ten late-type genes, the ribosome-related factors EIF4B, RPL5, RPL3, and the tumor angiogenesis modifier EPN3 were significantly associated with MFS in the late period also in a meta-analysis of tamoxifen-treated breast cancer cohorts. In contrast, only one late-type gene (EPN3) showed consistent survival associations in more than one cohort in the other cancer types, being associated with worse outcome in two non-small cell lung cancer cohorts. No late-type gene was validated in ovarian and colon cancer.

CONCLUSIONS

Ribosome-related genes were associated with decreased risk of late metastasis in both adjuvantly untreated and tamoxifen-treated breast cancer patients. In contrast, high expression of epsin (EPN3) was associated with increased risk of late metastasis. This is of clinical relevance considering the well-understood role of epsins in tumor angiogenesis and the ongoing development of epsin antagonizing therapies.

Everolimus in the management of metastatic renal cell carcinoma: an evidence-based review of its place in therapy

Introduction

Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults, and its pathogenesis is strictly related to altered cellular response to hypoxia, in which mTOR signaling pathway is implicated. Everolimus, an mTOR serine/threonine kinase inhibitor, represents a therapeutic option for the treatment of advanced RCC.

Aim

The objective of this article is to review the evidence for the treatment of metastatic RCC with everolimus.

Evidence review

Everolimus was approved for second- and third-line therapy in patients with advanced RCC according to the results of a Phase III pivotal trial that demonstrated a benefit in median progression-free survival of ~2 months compared to placebo after failure of previous lines of therapy, of which at least one was an anti-VEGFR tyrosine kinase inhibitor (TKI). The role of this drug in first-line setting has been investigated in Phase II trials, with no significant clinical benefit, even in combination with bevacizumab. Everolimus activity in non-clear cell RCC is supported by two randomized Phase II trials that confirmed the benefit in second-line setting but not in first line. Recently, two randomized Phase III trials (METEOR and CheckMate 025) demonstrated the inferiority of everolimus in second-line setting compared to the TKI cabozantinib and to the immune checkpoint inhibitor nivolumab, respectively. Moreover, a recent Phase II study demonstrated a significant benefit for the second-line combination treatment with everolimus plus lenvatinib (a novel TKI) in terms of progression-free survival and overall survival compared to the single-agent everolimus. Basing on preclinical data, the main downstream effectors of mTOR cascade, S6RP and its phosphorylated form, could be good predictive biomarkers of response to everolimus. The safety profile of the drug is favorable, with a good cost-effectiveness compared to second-line sorafenib or axitinib, and no significant impact on the quality of life of treated patients has been found.

Conclusion

Everolimus still represents a current standard of treatment for RCC progressive to previous treatment lines with VEGFR-TKI. The evidence about two new molecules, cabozantinib and nivolumab, successfully tested head-to-head with everolimus in recently published Phase III trials, will determine the shift of everolimus to the third-line setting and subsequent lines of treatment.