Loss of sorting nexin 5 stabilizes internalized growth factor receptors to promote thyroid cancer progression

SNX16 is required for hepatocellular carcinoma survival via modulating the EGFR-AKT signaling pathway

Sorting nexin 16 (SNX16), a pivotal sorting nexin, emerges in tumor progression complexity, fueling research interest. However, SNX16's biological impact and molecular underpinnings in hepatocellular carcinoma (HCC) remain elusive. This study probes SNX16's function, clinical relevance via mRNA, and protein expression in HCC. Overexpression/knockdown assays of SNX16 were employed to elucidate impacts on HCC cell invasion, proliferation, and EMT. Additionally, the study delved into SNX16's regulation of the EGFR-AKT signaling cascade mechanism. SNX16 overexpression in HCC correlates with poor patient survival; enhancing proliferation, migration, invasion, and tumorigenicity, while SNX16 knockdown suppresses these processes. SNX16 downregulation curbs phospho-EGFR, dampening AKT signaling. EGFR suppression counters SNX16-overexpression-induced HCC proliferation, motility, and invasiveness. Our findings delineate SNX16's regulatory role in HCC, implicating it as a prospective therapeutic target.

Endosomal Sorting Protein SNX27 and Its Emerging Roles in Human Cancers

SNX27 belongs to the sorting nexin (SNX) family of proteins that play a critical role in protein sorting and trafficking in the endocytosis pathway. This protein family is characterized by the presence of a Phox (PX) domain; however, SNX27 is unique in containing an additional PDZ domain. Recently, SNX27 has gained popularity as an important sorting protein that is associated with the retromer complex and mediates the recycling of internalized proteins from endosomes to the plasma membrane in a PDZ domain-dependent manner. Over 100 cell surface proteins have been identified as binding partners of the SNX27-retromer complex. However, the roles and underlying mechanisms governed by SNX27 in tumorigenesis remains to be poorly understood. Many of its known binding partners include several G-protein coupled receptors, such as β2-andrenergic receptor and parathyroid hormone receptor, are associated with multiple pathways implicated in oncogenic signaling and tumorigenesis. Additionally, SNX27 mediates the recycling of GLUT1 and the activation of mTORC1, both of which can regulate intracellular energy balance and promote cell survival and proliferation under conditions of nutrient deprivation. In this review, we summarize the structure and fundamental roles of SNX proteins, with a focus on SNX27, and provide the current evidence indicating towards the role of SNX27 in human cancers. We also discuss the gap in the field and future direction of SNX27 research. Insights into the emerging roles and mechanism of SNX27 in cancers will provide better development strategies to prevent and treat tumorigenesis.

Repulsion and attraction in searching: A hybrid algorithm based on gravitational kernel and vital few for cancer driver gene prediction

By taking a new perspective to combine a machine learning method with an evolutionary algorithm, a new hybrid algorithm is developed to predict cancer driver genes. Firstly, inspired by the search strategy with the capability of global search in evolutionary algorithms, a gravitational kernel is proposed to act on the full range of gene features. Constructed by fusing PPI and mutation features, the gravitational kernel is capable to produce repulsion effects. The candidate genes with greater mutation effects and PPI have higher similarity scores. According to repulsion, the similarity score of these promising genes is larger than ordinary genes, which is beneficial to search for these promising genes. Secondly, inspired by the idea of elite populations related to evolutionary algorithms, the concept of vital few is proposed. Targeted at a local scale, it acts on the candidate genes associated with vital few genes. Under attraction effect, these vital few driver genes attract those with similar mutational effects to them, which leads to greater similarity scores. Lastly, the model and parameters are optimized by using an evolutionary algorithm, so as to obtain the optimal model and parameters for cancer driver gene prediction. Herein, a comparison is performed with six other advanced methods of cancer driver gene prediction. According to the experimental results, the method proposed in this study outperforms these six state-of-the-art algorithms on the pan-oncogene dataset.

Exosomal noncoding RNAs in central nervous system diseases: biological functions and potential clinical applications

Central nervous system (CNS) disease is a general term for a series of complex and diverse diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), CNS tumors, stroke, epilepsy, and amyotrophic lateral sclerosis (ALS). Interneuron and neuron-glia cells communicate with each other through their homeostatic microenvironment. Exosomes in the microenvironment have crucial impacts on interneuron and neuron-glia cells by transferring their contents, such as proteins, lipids, and ncRNAs, constituting a novel form of cell-to-cell interaction and communication. Exosomal noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and PIWI-interacting RNAs (piRNAs), regulate physiological functions and maintain CNS homeostasis. Exosomes are regarded as extracellular messengers that transfer ncRNAs between neurons and body fluids due to their ability to cross the blood-brain barrier. This review aims to summarize the current understanding of exosomal ncRNAs in CNS diseases, including prospective diagnostic biomarkers, pathological regulators, therapeutic strategies and clinical applications. We also provide an all-sided discussion of the comparison with some similar CNS diseases and the main limitations and challenges for exosomal ncRNAs in clinical applications.

Temporal associations between leukocytes DNA methylation and blood lipids: a longitudinal study

BACKGROUND

The associations between blood lipids and DNA methylation have been investigated in epigenome-wide association studies mainly among European ancestry populations. Several studies have explored the direction of the association using cross-sectional data, while evidence of longitudinal data is still lacking.

RESULTS

We tested the associations between peripheral blood leukocytes DNA methylation and four lipid measures from Illumina 450 K or EPIC arrays in 1084 participants from the Chinese National Twin Registry and replicated the result in 988 participants from the China Kadoorie Biobank. A total of 23 associations of 19 CpG sites were identified, with 4 CpG sites located in or adjacent to 3 genes (TMEM49, SNX5/SNORD17 and CCDC7) being novel. Among the validated associations, we conducted a cross-lagged analysis to explore the temporal sequence and found temporal associations of methylation levels of 2 CpG sites with triglyceride and 2 CpG sites with high-density lipoprotein-cholesterol (HDL-C) in all twins. In addition, methylation levels of cg11024682 located in SREBF1 at baseline were temporally associated with triglyceride at follow-up in only monozygotic twins. We then performed a mediation analysis with the longitudinal data and the result showed that the association between body mass index and HDL-C was partially mediated by the methylation level of cg06500161 (ABCG1), with a mediation proportion of 10.1%.

CONCLUSIONS

Our study indicated that the DNA methylation levels of ABCG1, AKAP1 and SREBF1 may be involved in lipid metabolism and provided evidence for elucidating the regulatory mechanism of lipid homeostasis.

Sorting nexins as a promising therapeutic target for cardiovascular disorders: An updated overview

Sorting nexins (SNXs) are involved in sorting the protein cargo within the endolysosomal system. Recently, several studies have shown the role of SNXs in cardiovascular pathology. SNXs exert both physiologic and pathologic functions in the cardiovascular system by regulating protein sorting and trafficking, maintaining protein homeostasis, and participating in multiple signaling pathways. SNX deficiency results in blood pressure response to dopamine 5 receptor [D5R] stimulation. SNX knockout protected against atherosclerosis lesions by suppressing foam cell formation. Moreover, SNXs can act as endogenous anti-arrhythmic agents via maintenance of calcium homeostasis. Overexpression SNXs also can reduce cardiac fibrosis in atrial fibrillation. The SNX-STAT3 interaction in cardiac cells promoted heart failure. SNXs may have the potential to act as a pharmacological target against specific cardiovascular diseases.

Identification of specific role of SNX family in gastric cancer prognosis evaluation

We here perform a systematic bioinformatic analysis to uncover the role of sorting nexin (SNX) family in clinical outcome of gastric cancer (GC). Comprehensive bioinformatic analysis were realized with online tools such as TCGA, GEO, String, Timer, cBioportal and Kaplan-Meier Plotter. Statistical analysis was conducted with R language or Perl, and artificial neural network (ANN) model was established using Python. Our analysis demonstrated that SNX4/5/6/7/8/10/13/14/15/16/20/22/25/27/30 were higher expressed in GC, whereas SNX1/17/21/24/33 were in the opposite expression profiles. GSE66229 was employed as verification of the differential expression analysis based on TCGA. Clustering results gave the relative transcriptional levels of 30 SNXs in tumor, and it was totally consistent to the inner relevance of SNXs at mRNA level. Protein-Protein Interaction map showed closely and complex connection among 33 SNXs. Tumor immune infiltration analysis asserted that SNX1/3/9/18/19/21/29/33, SNX1/17/18/20/21/29/31/33, SNX1/2/3/6/10/18/29/33, and SNX1/2/6/10/17/18/20/29 were strongly correlated with four kinds of survival related tumor-infiltrating immune cells, including cancer associated fibroblast, endothelial cells, macrophages and Tregs. Kaplan-Meier survival analysis based on GEO presented more satisfactory results than that based on TCGA-STAD did, and all the 29 SNXs were statistically significant, SNX23/26/28 excluded. SNXs alteration contributed to microsatellite instability (MSI) or higher level of MSI-H (hyper-mutated MSI or high level of MSI), and other malignancy encompassing mutation of TP53 and ARID1A, as well as methylation of MLH1.The multivariate cox model, visualized as a nomogram, performed excellently in patients risk classification, for those with higher risk-score suffered from shorter overall survival (OS). Compared to previous researches, our ANN models showed a predictive power at a middle-upper level, with AUC of 0.87/0.72, 0.84/0.72, 0.90/0.71 (GSE84437), 0.98/0.66, 0.86/0.70, 0.98/0.71 (GSE66229), 0.94/0.66, 0.83/0.71, 0.88/0.72 (GSE26253) corresponding to one-, three- and five-year OS and recurrence free survival (RFS) estimation, especially ANN model built with GSE66229 including exclusively SNXs as input data. The SNX family shows great value in postoperative survival evaluation of GC, and ANN models constructed using SNXs transcriptional data manifesting excellent predictive power in both OS and RFS prediction works as convincing verification to that.

SNX5 suppresses clear cell renal cell carcinoma progression by inducing CD44 internalization and epithelial-to-mesenchymal transition

Aberrant expression of SNX5 can contribute to tumorigenesis, invasion, and metastasis of several human cancers. However, the clinicopathological and biological significance of SNX5 in clear cell renal cell carcinoma (ccRCC) remain unclear. In this study, we found that SNX5 expression was downregulated and negatively correlated with tumor size, American Joint Committee on Cancer stage, tumor thrombus of inferior vena cava, and poor prognosis in human ccRCC. Ectopic expression of SNX5 inhibited ccRCC cell proliferation and metastasis, whereas knockdown of SNX5 increased these activities both in vitro and in vivo. Mechanistically, overexpression of SNX5 blocked internalization and intracellular trafficking of CD44 in ccRCC cells. Knockdown of SNX5 was associated with epithelial-to-mesenchymal transition (EMT) in ccRCC cells. Overexpression of SNX5 inhibited TGF-β-induced migration, invasion, and EMT in ccRCC cells. KLF9 directly bound to the SNX5 promoter and increased SNX5 transcription. Moreover, we found that the combination of SNX5 and CD44 or E-cadherin or KLF9 was a more powerful predictor of poor prognosis than either parameter alone. Collectively, our data reveal a mechanism that KLF9-mediated SNX5 expression was associated with poor prognosis via trafficking of CD44 and promoting EMT in ccRCC. SNX5 may be a potential prognostic biomarker and therapeutic target for patients with ccRCC.

Sorting nexin 6 interacts with Cullin3 and regulates programmed death ligand 1 expression

Programmed death ligand 1 (PD-L1) is critical for the ability of cancer cells to evade attacks by the host immune system. However, the molecular mechanisms controlling PD-L1 expression have not been fully understood. Here, we demonstrate that sorting nexin 6 (SNX6) is a novel regulator of PD-L1 expression. Knockdown of SNX6 in cancer cells significantly decreases PD-L1 protein levels. In contrast, loss of SNX6 does not reduce PD-L1 mRNA levels. Instead, SNX6 interacts with Cullin3, an E3 ubiquitin ligase responsible for PD-L1 ubiquitination and subsequent degradation. By binding with Cullin3, SNX6 decreases the interaction between the adaptor protein speckle-type POZ protein and Cullin3, which in turn downregulates Cullin3-mediated PD-L1 ubiquitination. This research reveals a novel molecular nexus in modulating PD-L1.

Identification of prognostic and bone metastasis​-related alternative splicing signatures in mesothelioma

Mesothelioma (MESO) is an infrequent tumor derived from mesothelial cells of pleura, peritoneum, pericardium, and tunica vaginalis testis. Despite advancement in technologies and better understanding of tumor progression mechanism, the prognosis of MESO remains poor. The role of alternative splicing events (ASEs) in the oncogenesis, tumor metastasis and drug resistance has been widely discussed in multiple cancers. But the prognosis and potential therapeutic value of ASEs in MESO were not clearly studied by now. We constructed a prognostic model using RNA sequencing data and matched ASE data of MESO patients obtained from the TCGA and TCGASpliceSeq database. A total of 3,993 ASEs were identified associated with overall survival using Cox regression analysis. Eight of them were finally figured out to institute the model by lasso regression analysis. The risk score of the model can predict the prognosis independently. Among the identified 390 splicing factors (SF), HSPA1A and DDX3Y was significantly associated with 43 OS-SEs. Among these OS-SEs, SNX5-58744-AT (p = 0.048) and SNX5-58745-AT (p = 0.048) were significantly associated with bone metastasis. Co-expression analysis of signal pathways and SNX5-58744-AT, SNX5-58745-AT was also depicted using GSVA. Finally, we proposed that splicing factor (SF) HSPA1A could regulate SNX5-58744-AT (R = -0.414) and SNX5-58745-AT (R = 0.414) through the pathway "Class I MHC mediated antigen processing and presentation" (R = 0.400). In this way, tumorigenesis and bone metastasis of MESO were controlled.

Exosomal circRNA 0001445 promotes glioma progression through miRNA-127-5p/SNX5 pathway

Background: Glioma is one of the most wide-spreading brain cancers worldwide. Exosomes have emerged as essential regulators in intercellular communication, and exosomal circular RNAs (circRNAs) are critical for cancer progression. In this study, we aimed to investigate the role of exosomal circRNAs in glioma progression and associated mechanisms.

Methods: Exosomes derived from glioma cells were isolated and identified by transmission electron microscopy and nanoparticle tracking analysis (NTA). CCK-8, wound healing assays, transwell invasion assays, and flow cytometry assays were performed to assess glioma progression. RNA sequencing, RT-qPCR, western blotting, fluorescence in situ hybridization assay, luciferase assays, and cell transfection assay were performed to investigate related molecular mechanisms.

Results: The results demonstrated that exosomes derived from glioma cells promoted glioma progression. Also, exosomal circRNA 0001445 was taken up and upregulated in glioma cells treated with exosomes. In addition, exosomal circRNA 0001445 acted as a sponge for miRNA-127-5p to upregulate the expression of sorting nexin 5 (SNX5). Lastly, the effect of exosomal circRNA 0001445 was mediated by miRNA-127-5p/ SNX5 signaling pathway.

Conclusion: These results demonstrated that exosomal circRNA 0001445 promoted glioma progression through miRNA-127-5p/SNX5 signaling pathway. This study provides a novel understanding of the molecular mechanism of glioma progression.

Combined RNA/tissue profiling identifies novel Cancer/testis genes

Cancer/Testis (CT) genes are induced in germ cells, repressed in somatic cells, and derepressed in somatic tumors, where these genes can contribute to cancer progression. CT gene identification requires data obtained using standardized protocols and technologies. This is a challenge because data for germ cells, gonads, normal somatic tissues, and a wide range of cancer samples stem from multiple sources and were generated over substantial periods of time. We carried out a GeneChip‐based RNA profiling analysis using our own data for testis and enriched germ cells, data for somatic cancers from the Expression Project for Oncology, and data for normal somatic tissues from the Gene Omnibus Repository. We identified 478 candidate loci that include known CT genes, numerous genes associated with oncogenic processes, and novel candidates that are not referenced in the Cancer/Testis Database (www.cta.lncc.br). We complemented RNA expression data at the protein level for SPESP1, GALNTL5, PDCL2, and C11orf42 using cancer tissue microarrays covering malignant tumors of breast, uterus, thyroid, and kidney, as well as published RNA profiling and immunohistochemical data provided by the Human Protein Atlas (www.proteinatlas.org). We report that combined RNA/tissue profiling identifies novel CT genes that may be of clinical interest as therapeutical targets or biomarkers. Our findings also highlight the challenges of detecting truly germ cell‐specific mRNAs and the proteins they encode in highly heterogenous testicular, somatic, and tumor tissues.

Sorting nexins: A novel promising therapy target for cancerous/neoplastic diseases

Sorting nexins (SNXs) are a diverse group of cytoplasmic- and membrane-associated phosphoinositide-binding proteins containing the PX domain proteins. The function of SNX proteins in regulating intracellular protein trafficking consists of endocytosis, endosomal sorting, and endosomal signaling. Dysfunctions of SNX proteins are demonstrated to be involved in several cancerous/neoplastic diseases. Here, we review the accumulated evidence of the molecular structure and biological function of SNX proteins and discuss the regulatory role of SNX proteins in distinct cancerous/neoplastic diseases. SNX family proteins may be a valuable potential biomarker and therapeutic strategy for diagnostics and treatment of cancerous/neoplastic diseases.

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets or biomarkers. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types. In this paper, we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types. We empirically show that LOTUS outperforms five other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types.

Cancer development is driven by mutations and dysfunction of important, so-called cancer driver genes, that could be targeted by specific therapies. While a number of such cancer genes have already been identified, it is believed that many more remain to be discovered. To help prioritize experimental investigations of candidate genes, several computational methods have been proposed to rank promising candidates based on their mutations in large cohorts of cancer cases, or on their interactions with known driver genes in biological networks. We propose LOTUS, a new computational approach to identify genes with high oncogenic potential. LOTUS implements a machine learning approach to learn an oncogenic potential score from known driver genes, and brings two novelties compared to existing methods. First, it allows to easily combine heterogeneous sources of information into the scoring function, which we illustrate by learning a scoring function from both known mutations in large cancer cohorts and interactions in biological networks. Second, using a multitask learning strategy, it can predict different driver genes for different cancer types, while sharing information between them to improve the prediction for every type. We provide experimental results showing that LOTUS significantly outperforms several state-of-the-art cancer gene prediction software.

lncRNA TMEM51-AS1 and RUSC1-AS1 function as ceRNAs for induction of laryngeal squamous cell carcinoma and prediction of prognosis

BACKGROUND

Long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) to interact with miRNAs to regulate target genes and promote cancer initiation and progression. The expression of lncRNAs and miRNAs can be epigenetically regulated. The goal of this study was to construct an lncRNA-miRNA-mRNA ceRNA network in laryngeal squamous cell carcinoma (LSCC) and reveal their methylation patterns, which was not investigated previously.

METHODS

Microarray datasets available from the Gene Expression Omnibus database were used to identify differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) between LSCC and controls, which were then overlapped with differentially methylated regions (DMRs). The ceRNA network was established by screening the interaction relationships between miRNAs and lncRNAs/mRNAs by corresponding databases. TCGA database was used to identify prognostic biomarkers.

RESULTS

Five DELs (downregulated: TMEM51-AS1, SND1-IT1; upregulated: HCP5, RUSC1-AS1, LINC00324) and no DEMs were overlapped with the DMRs, but only a negative relationship occurred in the expression and methylation level of TMEM51-AS1. Five DELs could interact with 11 DEMs to regulate 242 DEGs, which was used to construct the ceRNA network, including TMEM51-AS1-miR-106b-SNX21/ TRAPPC10, LINC00324/RUSC1-AS1-miR-16-SPRY4/MICAL2/ SLC39A14, RUSC1-AS1-miR-10-SCG5 and RUSC1-AS1-miR-7-ZFP1 ceRNAs axes. Univariate Cox regression analysis showed RUSC1-AS1 and SNX21 were associated with overall survival (OS); LINC00324, miR-7 and ZFP1 correlated with recurrence-free survival (RFS); miR-16, miR-10, SCG5, SPRY4, MICAL2 and SLC39A14 were both OS and RFS-related. Furthermore, TRAPPC10 and SLC39A14 were identified as independent OS prognostic factors by multivariate Cox regression analysis.

CONCLUSION

DNA methylation-mediated TMEM51-AS1 and non-methylation-mediated RUSC1-AS1 may function as ceRNAs for induction of LSCC. They and their ceRNA axis genes (particularly TMEM51-AS1-miR-106b-TRAPPC10; RUSC1-AS1-miR-16-SLC39A14) may be potentially important prognostic biomarkers for LSCC.

Sorting Nexin 5 Controls Head and Neck Squamous Cell Carcinoma Progression by Modulating FBW7

Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer worldwide. Long-term survival rates in patients with HNSCC have not increased significantly in the past 30 years. Therefore, looking for novel molecular targets that control HNSCC progression is urgently required to improve the treatment of HNSCC. Here, we identified Sorting Nexin 5 (SNX5) as a new regulator that plays an oncogenic function in HNSCC progression. Analyzing HNSCC patients' data from the Cancer Genome Atlas (TCGA) indicates that the expression levels of SNX5 in HNSCC are significantly elevated compared to normal tissues. Furthermore, higher SNX5 expression correlates with a worse prognosis for HNSCC patients. These results suggest that SNX5 has an oncogenic role. Consistently, loss of SNX5 in HNSCC cells dramatically reduces colony formation and significantly decreases tumor growth in xenograft mouse models. SNX5 interacts with the tumor suppressor F-box/WD repeat-containing protein 7 (FBW7), an E3 ubiquitin ligase that mediates ubiquitination and degradation of oncoproteins such as c-Myc, NOTCH1, and Cyclin E1. By interacting with FBW7, SNX5 inhibits FBW7-mediated oncoproteins ubiquitination. In this way, SNX5 decreases the FBW7-mediated oncoproteins degradation to promote HNSCC progression.

CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies.