Integrated Bioinformatics Data Analysis Reveals Prognostic Significance Of SIDT1 In Triple-Negative Breast Cancer

Comprehensive analysis of the functions, prognostic and diagnostic values of RNA binding proteins in head and neck squamous cell carcinoma

BACKGROUND

Accumulating evidence has suggested that RNA binding protein (RBP) dysregulation plays an essential role during tumorigenesis. Here, we sought to explore the potential biological functions and clinical significance of RBP and develop diagnostic and prognostic signatures based on RBP in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

The differently expressed RBPs between HNSCC samples and their normal counterparts were identified using the Limma package. The immunohistochemistry (IHC) images of several RBPs were collected from the Human Protein Atlas database. The diagnostic signature based on RBP was built by LASSO-logistic regression and random forest. The prognostic signature based on RBP was constructed by LASSO and stepwise Cox regression analysis in the training cohort and validated in the validation cohort.

RESULTS

Eighty-four aberrantly expressed RBPs were obtained, comprising 41 up-regulated and 43 down-regulated RBPs. Seven RBP genes (CPEB3, PDCD4, ENDOU, PARP12, DNMT3B, IGF2BP1, EXO1) were identified as diagnostic-related hub genes. They were used to establish a diagnostic RBP signature risk score (DRBPS) model by the coefficients in least absolute shrinkage and selection operator (LASSO)-logistic regression analysis and showed high specificity and sensitivity in the training (area under the receiver operating characteristic curve (AUC) = 0.998), and in all validation cohorts (AUC > 0.95 for all). Similarly, seven RBP genes (MKRN3, ZC3H12D, EIF5A2, AFF3, SIDT1, RBM24, and NR0B1) were identified as prognosis-associated hub genes by LASSO and stepwise multiple Cox regression analyses and were used to construct the prognostic model named as PRBPS. The AUC of the time-dependent receiver operator characteristic curve of the prognostic model was 0.664 at 3 years and 0.635 at 5 years in the training cohort and 0.720, 0.777 in the validation cohort, showing a favorable predictive efficacy for prognosis in HNSCC.

CONCLUSIONS

Our results demonstrate the value of consideration of RBP in the diagnosis and prognosis for HNSCC and provide a novel insight into understanding the potential role of dysregulated RBP in HNSCC.

Role of NuMA1 in breast cancer stem cells with implications for combination therapy of PIM1 and autophagy inhibition in triple negative breast cancer

Background

Nuclear mitotic apparatus protein 1 (NuMA1) is a cell cycle protein and upregulated in breast cancer. However, the role of NuMA1 in TNBC and its regulation in heterogenous populations remains elusive.

Methods

We performed CRISPR mediated deletion of NuMA1 in mouse TNBC cells, BF3M. FACS was utilized to isolate BCSCs, and bulk cells based on CD29 and CD61 markers. Cell viability, migration, and invasion ability of BCSCs and bulk cells was evaluated using MTT, wound healing and transwell invasion assays, respectively. In vivo mouse breast cancer and lung metastatic models were generated to evaluate the combination treatment of SMI-4a and Lys-o5 inhibitors.

Results

We identified that high expression of NuMA1 associated with poor survival of breast cancer patients. Further, human tissue microarray results depicted high expression of NuMA1 in TNBC relative to non-adjacent normal tissues. Therefore, we performed CRISPR mediated deletion of NuMA1 in a mouse mammary tumor cell line, BF3M and revealed that NuMA1 deletion reduced mammary tumorigenesis. We also showed that NuMA1 deletion reduced ALDH+ and CD29hiCD61+ breast cancer stem cells (BCSCs), indicating a role of NuMA1 in BCSCs. Further, sorted and characterized BCSCs from BF3M depicted reduced metastasis with NuMA1 KO cells. Moreover, we found that PIM1, an upstream kinase of NuMA1 plays a preferential role in maintenance of BCSCs associated phenotypes, but not in bulk cells. In contrast, PIM1 kinase inhibition in bulk cells depicted increased autophagy (FIP200). Therefore, we applied a combination treatment strategy of PIM1 and autophagy inhibition using SMI-4a and Lys05 respectively, showed higher efficacy against cell viability of both these populations and further reduced breast tumor formation and metastasis. Together, our study demonstrated NuMA1 as a potential therapeutic target and combination treatment using inhibitors for an upstream kinase PIM1 and autophagy inhibitors could be a potentially new therapeutic approach for TNBC.

Conclusions

Our study demonstrated that combination treatment of PIM1 inhibitor and autophagy inhibitor depicted reduced mammary tumorigenesis and metastasis by targeting NuMA1 in BCSCs and bulk cells of TNBC, demonstrating this combination treatment approach could be a potentially effective therapy for TNBC patients.

Characterization of N-glycosylation and its functional role in SIDT1-Mediated RNA uptake

The mammalian SID-1 transmembrane family members, SIDT1 and SIDT2, are multipass transmembrane proteins that mediate the cellular uptake and intracellular trafficking of nucleic acids, playing important roles in the immune response and tumorigenesis. Previous work has suggested that human SIDT1 and SIDT2 are N-glycosylated, but the precise site-specific N-glycosylation information and its functional contribution remain unclear. In this study, we use high-resolution liquid chromatography tandem mass spectrometry to comprehensively map the N-glycosites and quantify the N-glycosylation profiles of SIDT1 and SIDT2. Further molecular mechanistic probing elucidates the essential role of N-linked glycans in regulating cell surface expression, RNA binding, protein stability, and RNA uptake of SIDT1. Our results provide crucial information about the potential functional impact of N-glycosylation in the regulation of SIDT1-mediated RNA uptake and provide insights into the molecular mechanisms of this promising nucleic acid delivery system with potential implications for therapeutic applications.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

The Role of Cocaine- and Amphetamine-Regulated Transcript (CART) in Cancer: A Systematic Review

The functions of cocaine- and amphetamine-regulated transcript (CART) neuropeptide encoded by the CARTPT gene vary from modifying behavior and pain sensitivity to being an antioxidant. Putative CART peptide receptor GPR160 was implicated recently in the pathogenesis of cancer. However, the exact role of CART protein in the development of neoplasms remains unclear. This systematic review includes articles retrieved from the Scopus, PubMed, Web of Science and Medline Complete databases. Nineteen publications that met the inclusion criteria and describe the association of CART and cancer were analyzed. CART is expressed in various types of cancer, e.g., in breast cancer and neuroendocrine tumors (NETs). The role of CART as a potential biomarker in breast cancer, stomach adenocarcinoma, glioma and some types of NETs was suggested. In various cancer cell lines, CARTPT acts an oncogene, enhancing cellular survival by the activation of the ERK pathway, the stimulation of other pro-survival molecules, the inhibition of apoptosis or the increase in cyclin D1 levels. In breast cancer, CART was reported to protect tumor cells from tamoxifen-mediated death. Taken together, these data support the role of CART activity in the pathogenesis of cancer, thus opening new diagnostic and therapeutic approaches in neoplastic disorders.

Development and validation of novel prognostic models based on RNA-binding proteins in breast cancer

Objectives

We aimed to construct novel prognostic models based on RNA-binding proteins (RBPs) in breast cancer (BRCA) and explore their roles in this disease and their effects on tumor-infiltrating immune cells (TIICs).

Methods

Datasets were downloaded from the Gene Expression Omnibus (GEO) database. Functions and prognostic values of RBPs were systematically investigated using a series of bioinformatics analysis methods. TIICs were assessed using CIBERSORT.

Results

Overall, 138 differentially expressed RBPs were identified, of which 86 were upregulated and 52 were downregulated. Of these, 13 RBPs were identified as prognosis-related and adopted to construct an overall survival (OS) model, while 12 RBPs were used for the relapse-free survival (RFS) model. High-risk patients had poorer OS and RFS rates than low-risk patients. The results indicate that the OS and RFS models are good prognostic models with reliable predictive abilities. In addition, the proportions of CD8, CD4 naïve, and CD4 memory resting T cells, as well as resting dendritic cells, were significantly different between the low-risk and high-risk groups in the OS model.

Conclusions

OS and RFS signatures can be used as reliable BRCA prognostic biomarkers. This work will help understand the prognostic roles and functions of RBPs in BRCA.

Prognostic Significance of CCNB2 Expression in Triple-Negative Breast Cancer

Purpose

The aim of this study was to explore potential gene therapy targets for triple-negative breast cancer (TNBC).

Patients and Methods

Three gene expression profiles (GSE64790, GSE62931, and GSE38959) from the Gene Expression Omnibus (GEO) database were analyzed. The GEO2R analysis tool was used to screen for differentially expressed genes (DEGs) between TNBC and normal tissues, followed by Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DEGs. The protein–protein interaction network of DEGs was visualized using Metascape to identify the core genes. Subsequently, transcriptional data for the core genes in patients with breast cancer were investigated in the ONCOMINE database. Kaplan–Meier survival analysis was used to evaluate the prognostic value of core gene expression levels in patients with TNBC. Finally, the clinicopathological and long-term follow-up data of 39 patients with TNBC were retrospectively analyzed at the First Affiliated Hospital of the Bengbu Medical College between January 2014 and July 2020. Immunohistochemistry was used to evaluate the expression and subcellular localization of CCNB2 in TNBC tissues.

Results

A total of 66 DEGs were identified between TNBC and normal tissues, including 33 upregulated and 33 downregulated genes in TNBC. Furthermore, a potential protein complex was identified for five core genes. The high expression of these core genes, especially the overexpression of CCNB2, was correlated with a poor prognosis of patients with TNBC. The CCNB2 protein was expressed in the cytoplasm, and its expression was significantly higher in TNBC tissues than that in the adjacent nontumor tissues. Overall survival of patients was significantly correlated with the expression of CCNB2 (p < 0.05).

Conclusion

CCNB2 may play a crucial role in the development of TNBC and has the potential to be used as a prognostic biomarker for TNBC.

An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data

High dimensionality and noise have made it difficult to detect related biomarkers in omics data. Through previous study, penalized maximum trimmed likelihood estimation is effective in identifying mislabeled samples in high-dimensional data with mislabeled error. However, the algorithm commonly used in these studies is the concentration step (C-step), and the C-step algorithm that is applied to robust penalized regression does not ensure that the criterion function is gradually optimized iteratively, because the regularized parameters change during the iteration. This makes the C-step algorithm runs very slowly, especially when dealing with high-dimensional omics data. The AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm is proposed. In simulation experiments, the AR-Cstep algorithm converged faster (the average computation time was only 2% of that of the C-step algorithm) and was more accurate in terms of variable selection and outlier identification than the C-step algorithm. The two algorithms were further compared on triple negative breast cancer (TNBC) RNA-seq data. AR-Cstep can solve the problem of the C-step not converging and ensures that the iterative process is in the direction that improves criterion function. As an improvement of the C-step algorithm, the AR-Cstep algorithm can be extended to other robust models with regularized parameters.

Identification of Atrial Fibrillation-Associated Genes ERBB2 and MYPN Using Genome-Wide Association and Transcriptome Expression Profile Data on Left-Right Atrial Appendages

More reliable methods are needed to uncover novel biomarkers associated with atrial fibrillation (AF). Our objective is to identify significant network modules and newly AF-associated genes by integrative genetic analysis approaches. The single nucleotide polymorphisms with nominal relevance significance from the AF-associated genome-wide association study (GWAS) data were converted into the GWAS discovery set using ProxyGeneLD, followed by merging with significant network modules constructed by weighted gene coexpression network analysis (WGCNA) from one expression profile data set, composed of left and right atrial appendages (LAA and RAA). In LAA, two distinct network modules were identified (blue: p = 0.0076; yellow: p = 0.023). Five AF-associated biomarkers were identified (ERBB2, HERC4, MYH7, MYPN, and PBXIP1), combined with the GWAS test set. In RAA, three distinct network modules were identified and only one AF-associated gene LOXL1 was determined. Using human LAA tissues by real-time quantitative polymerase chain reaction, the differentially expressive results of ERBB2, MYH7, and MYPN were observed (p < 0.05). This study first demonstrated the feasibility of fusing GWAS with expression profile data by ProxyGeneLD and WGCNA to explore AF-associated genes. In particular, two newly identified genes ERBB2 and MYPN via this approach contribute to further understanding the occurrence and development of AF, thereby offering preliminary data for subsequent studies.

Construction of a circRNA-miRNA-mRNA Regulatory Network Reveals Potential Mechanism and Treatment Options for Osteosarcoma

Background

Osteosarcoma is a common malignant primary bone tumor in adolescents and children. Numerous studies have shown that circRNAs were involved in the proliferation and invasion of various tumors. However, the role of circRNAs in osteosarcoma remains unclear. Here, we aimed to explore the regulatory network among circRNA-miRNA-mRNA in osteosarcoma.

Methods

The circRNA (GSE140256), microRNA (GSE28423), and mRNA (GSE99671) expression profiles of osteosarcoma were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs, miRNAs and mRNAs were identified. CircRNA-miRNA interactions and miRNA-mRNA interactions were determined by Circular RNA Interactome (CircInteractome) database and microRNA Data Integration Portal (mirDIP) database, respectively. Then, we constructed a regulatory network. Function enrichment analysis of miRNA and mRNA was performed by DIANA-miRPath v3.0 and Metascape database, respectively. mRNAs with significant prognostic value were identified based on expression profiles from The Cancer Genome Atlas (TCGA) database, and we constructed a subnetwork for them. To make the most of the network, we used the CLUE database to predict potential drugs for the treatment of osteosarcoma based on mRNA expression in the network. And we used the STITCH database to analyze and validate the interactions among these drugs and mRNAs, and to further screen for potential drugs.

Results

A total of 9 circRNAs, 19 miRNAs, 67 mRNAs, 54 pairs of circRNA-miRNA interactions and 110 pairs of miRNA-mRNA interactions were identified. A circRNA-miRNA-mRNA network was constructed. Function enrichment analysis indicated that these miRNAs and mRNAs in the network were involved in the process of tumorigenesis and immune response. Among these mRNAs, STC2 and RASGRP2 with significantly prognostic value were identified, and we constructed a subnetwork for them. Based on mRNA expression in the network, three potential drugs, quinacridine, thalidomide and zonisamide, were screened for the treatment of osteosarcoma. Among them, quinacridine and thalidomide have been proved to have anti-tumor effects in previous studies, while zonisamide has not been reported. And a corresponding drug-protein interaction network was constructed.

Conclusion

Overall, we constructed a circRNA-miRNA-mRNA regulatory network to investigate the possible mechanism in osteosarcoma, and predicted that quinacridine, thalidomide and zonisamide could be potential drugs for the treatment of osteosarcoma.

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

Background

Previous studies have reported that labeling errors are not uncommon in omics data. Potential outliers may severely undermine the correct classification of patients and the identification of reliable biomarkers for a particular disease. Three methods have been proposed to address the problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based on the least trimmed square (enetLTS), and Ensemble. Ensemble is an ensembled classification based on distinct feature selection and modeling strategies. The accuracy of biomarker selection and outlier detection of these methods needs to be evaluated and compared so that the appropriate method can be chosen.

Results

The accuracy of variable selection, outlier identification, and prediction of three methods (Ensemble, enetLTS, Rlogreg) were compared for simulated and an RNA-seq dataset. On simulated datasets, Ensemble had the highest variable selection accuracy, as measured by a comprehensive index, and lowest false discovery rate among the three methods. When the sample size was large and the proportion of outliers was ≤5%, the positive selection rate of Ensemble was similar to that of enetLTS. However, when the proportion of outliers was 10% or 15%, Ensemble missed some variables that affected the response variables.

Overall, enetLTS had the best outlier detection accuracy with false positive rates < 0.05 and high sensitivity, and enetLTS still performed well when the proportion of outliers was relatively large. With 1% or 2% outliers, Ensemble showed high outlier detection accuracy, but with higher proportions of outliers Ensemble missed many mislabeled samples. Rlogreg and Ensemble were less accurate in identifying outliers than enetLTS. The prediction accuracy of enetLTS was better than that of Rlogreg. Running Ensemble on a subset of data after removing the outliers identified by enetLTS improved the variable selection accuracy of Ensemble.

Conclusions

When the proportion of outliers is ≤5%, Ensemble can be used for variable selection. When the proportion of outliers is > 5%, Ensemble can be used for variable selection on a subset after removing outliers identified by enetLTS. For outlier identification, enetLTS is the recommended method. In practice, the proportion of outliers can be estimated according to the inaccuracy of the diagnostic methods used.

Bioinformatic Identification of a Breast-Specific Transcript Profile

PURPOSE

To identify a breast-specific transcript profile for the first time, and present an updated bioinformatics strategy for searching tissue-specific transcripts and predicting their significance in cancer.

EXPERIMENTAL DESIGN

The RNA-seq data of 49 311 transcripts in 88 human tissues from the GTEx, the Illumina Body Map, and the RIKEN FANTOM5 project are integrated to screen breast-specific transcripts. Gene Expression Profiling Interactive Analysis, TGCA, and Kaplan-Meier Plotter are used to examine their expression in cancer tissues and values for prognosis prediction.

RESULTS

Only 96 transcripts in human genome are breast-specific for women. Among them, ankyrin repeat domain 30A (ANKRD30A) and long intergenic non-protein coding RNA 993 (LINC00993) are further analyzed. The two transcripts are also breast-specific in 33 types of common female cancer and are often dysregulated in breast cancer tissues. Their expression is higher in the luminal breast cancer while significantly downregulated in triple-negative breast cancer. Moreover, the high expression levels of ANKRD30A and LINC0993 in breast cancer tissues indicate a better prognosis of patients with breast cancer.

CONCLUSIONS AND CLINICAL RELEVANCE

Breast-specific transcripts in human genome are rare and poorly understood currently. The data indicate that these breast-specific biomarkers are promising candidates for screening early cancer, assessing treatment response, monitoring recurrence, identifying metastatic tumor origin, and serving as potential targets for immunotherapy.

BRD4 inhibition suppresses PD-L1 expression in triple-negative breast cancer

Programmed death-ligand 1 (PD-L1) expression on the surface of tumour cells can cause tumour immune evasion. Benefits of combining anti-PD-L1 therapy with nab-paclitaxel in patients with advanced triple-negative breast cancer (TNBC) have been reported. However, some patients cannot tolerate the immune-related adverse effects (irAEs) caused by antibody-based immunotherapy. BRD4 is a member of the bromodomain and extra-terminal domain (BET) family. BRD4 inhibition has shown antitumour effects in many tumours, but its role in TNBC has not been definitively concluded. In particular, the immune regulation of BRD4 in TNBC has been rarely studied. In this study, we used JQ1, a BET inhibitor, and small interfering RNAs (siRNAs) targeting BRD4 to explore the influence of BRD4 on PD-L1 expression in TNBC. The results indicated that BRD4 inhibition suppressed PD-L1 expression and the PD-L1 upregulation induced by interferon-γ (IFN-γ). In the in vivo experiments, we found that JQ1 not only reduced the PD-L1 expression level but also changed the proportions of T lymphocyte subsets in the spleens of tumour-bearing mice, which helped to relieve immunosuppression. Briefly, our study reveals that BRD4 regulates PD-L1 expression and may provide a potential method for blocking the programmed death 1 (PD-1)/PD-L1 immune checkpoint in TNBC.