Overexpression of MAPT-AS1 is associated with better patient survival in breast cancer

HAS2 facilitates glioma cell malignancy and suppresses ferroptosis in an FZD7-dependent manner

Glioma is the most common malignant tumor in the central nervous system, and it is crucial to uncover the factors that influence prognosis. In this study, we utilized Mfuzz to identify a gene set that showed a negative correlation with overall survival in patients with glioma. Gene Ontology (GO) enrichment analyses were then undertaken to gain insights into the functional characteristics and pathways associated with these genes. The expression distribution of Hyaluronan Synthase 2 (HAS2) was explored across multiple datasets, revealing its expression patterns. In vitro and in vivo experiments were carried out through gene knockdown and overexpression to validate the functionality of HAS2. Potential upstream transcription factors of HAS2 were predicted using transcriptional regulatory databases, and these predictions were experimentally validated using ChIP-PCR and dual-luciferase reporter gene assays. The results showed that elevated expression of HAS2 in glioma indicates poor prognosis. HAS2 was found to play a role in activating an antiferroptosis pathway in glioma cells. Inhibiting HAS2 significantly increased cellular sensitivity to ferroptosis-inducing agents. Finally, we determined that the oncogenic effect of HAS2 is mediated by the key receptor of the WNT pathway, FZD7.

Identification and validation of a copper homeostasis-related gene signature for the predicting prognosis of breast cancer patients via integrated bioinformatics analysis

The prognostic value of copper homeostasis-related genes in breast cancer (BC) remains largely unexplored. We analyzed copper homeostasis-related gene profiles within The Cancer Genome Atlas Program breast cancer cohorts and performed correlation analysis to explore the relationship between copper homeostasis-related mRNAs (chrmRNA) and lncRNAs. Based on these results, we developed a gene signature-based risk assessment model to predict BC patient outcomes using Cox regression analysis and a nomogram, which was further validated in a cohort of 72 BC patients. Using the gene set enrichment analysis, we identified 139 chrmRNAs and 16 core mRNAs via the Protein-Protein Interaction network. Additionally, our copper homeostasis-related lncRNAs (chrlncRNAs) (PINK1.AS, OIP5.AS1, HID.AS1, and MAPT.AS1) were evaluated as gene signatures of the predictive model. Kaplan-Meier survival analysis revealed that patients with a high-risk gene signature had significantly poorer clinical outcomes. Receiver operating characteristic curves showed that the prognostic value of the chrlncRNAs model reached 0.795 after ten years. Principal component analysis demonstrated the capability of the model to distinguish between low- and high-risk BC patients based on the gene signature. Using the pRRophetic package, we screened out 24 anticancer drugs that exhibited a significant relationship with the predictive model. Notably, we observed higher expression levels of the four chrlncRNAs in tumor tissues than in the adjacent normal tissues. The correlation between our model and the clinical characteristics of patients with BC highlights the potential of chrlncRNAs for predicting tumor progression. This novel gene signature not only predicts the prognosis of patients with BC but also suggests that targeting copper homeostasis may be a viable treatment strategy.

Identification of prognostic melatonin-related lncRNA signature in tumor immune microenvironment and drug resistance for breast cancer

BACKGROUND

Melatonin is a neurohormone involved in diverse physiological processes, including regulation of circadian rhythm, oncogenesis and immune function. More attention are focused on the molecular events surrounding the occurrence of abnormally expressed lncRNAs leading to breast cancer. The purpose of this study was to evaluate the role of melatonin-related lncRNAs in the clinical management of BRCA patients and their immune responses.

METHODS

The transcriptome data and clinical data of BRCA patients were acquired from TCGA database. A total of 1103 patients were randomly assigned to either training set or validation set. A melatonin-related lncRNA signature was constructed in the training set and verified in the validation set. Functional analysis, immune microenvironment and drug resistance analysis associated to melatonin-related lncRNAs were performed by utilizing GO&KEGG, ESTIMATE and TIDE analysis. A nomogram based on the signature score and clinical characteristics was established, which was calibrated to increase prediction probability of 1-year, 3-year and 5-year survival for BRCA patients.

RESULTS

BRCA patients were divided into two signature groups based on a 17-melatonin-related lncRNA signature. High-signature patients had worse prognosis than low-signature patients (p < 0.001). Univariate and multivariate Cox regression analysis proved that the signature score was an independent prognostic factor for BRCA patients. Functional analysis indicated that high-signature BRCA involved in regulation of processing and maturation of mRNA and misfolded protein response. Remarkably, immune microenvironment analysis showed that the proportion of tumor-infiltrating M2 macrophage and the expression of CTLA4 were significantly higher in high-signature BRCA. The calibration curves for the probability of invasive BRCA showed optimal agreement between the probability as predicted by the nomogram and the actual probability.

CONCLUSIONS

A novel melatonin-related lncRNA signature was considered as an independent prognostic indicator for BRCA patients. Melatonin-related lncRNAs were potentially associated with tumor immune microenvironment and might be therapeutic targets for BRCA patients.

A Novel Aging-Related Prognostic lncRNA Signature Correlated with Immune Cell Infiltration and Response to Immunotherapy in Breast Cancer

Breast cancer (BC) is among the most universal malignant tumors in women worldwide. Aging is a complex phenomenon, caused by a variety of factors, that plays a significant role in tumor development. Consequently, it is crucial to screen for prognostic aging-related long non-coding RNAs (lncRNAs) in BC. The BC samples from the breast-invasive carcinoma cohort were downloaded from The Cancer Genome Atlas (TCGA) database. The differential expression of aging-related lncRNAs (DEarlncRNAs) was screened by Pearson correlation analysis. Univariate Cox regression, LASSO-Cox analysis, and multivariate Cox analysis were performed to construct an aging-related lncRNA signature. The signature was validated in the GSE20685 dataset from the Gene Expression Omnibus (GEO) database. Subsequently, a nomogram was constructed to predict survival in BC patients. The accuracy of prediction performance was assessed through the time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, principal component analyses, decision curve analysis, calibration curve, and concordance index. Finally, differences in tumor mutational burden, tumor-infiltrating immune cells, and patients' response to chemotherapy and immunotherapy between the high- and low-risk score groups were explored. Analysis of the TCGA cohort revealed a six aging-related lncRNA signature consisting of MCF2L-AS1, USP30-AS1, OTUD6B-AS1, MAPT-AS1, PRR34-AS1, and DLGAP1-AS1. The time-dependent ROC curve proved the optimal predictability for prognosis in BC patients with areas under curves (AUCs) of 0.753, 0.772, and 0.722 in 1, 3, and 5 years, respectively. Patients in the low-risk group had better overall survival and significantly lower total tumor mutational burden. Meanwhile, the high-risk group had a lower proportion of tumor-killing immune cells. The low-risk group could benefit more from immunotherapy and some chemotherapeutics than the high-risk group. The aging-related lncRNA signature can provide new perspectives and methods for early BC diagnosis and therapeutic targets, especially tumor immunotherapy.

A Novel Oxidative Stress-Related lncRNA Signature That Predicts the Prognosis and Tumor Immune Microenvironment of Breast Cancer

Background

The association between oxidative stress and lncRNAs within the cancer-related researching field has been a controversial subject. At present, the exact function of oxidative stress as well as lncRNAs exert in breast cancer (BC) are still unclear. Therefore, the present study examined the lncRNAs oxidative stress-related in BC.

Methods

Transcriptome data of BC obtained from TCGA (The Cancer Genome Atlas) database were used to generate synthetic matrices. Patients with breast cancer were randomly assigned to training, testing, or combined groups. The prognostic signature of oxidative stress was created using the selection operator Cox regression method, and the difference in prognosis between groups was examined using Kaplan-Meier curves, the accuracy of which was calculated using a receiver-operating characteristic-area through the curve (ROC-AUC) analysis with internal validation. Also, the Gene Set Enrichment Analyses (GSEA) was applied for the analysis of the risk groups. To conclude, the half-maximal inhibitory concentration (IC50) of these groups were investigated by immunoassay assay.

Results

A model based on 7 lncRNAs related to oxidative stress was proposed, and the calibration plots and projected prognosis matched well. For prognosis at 5, 3, and 1 year, the area under the ROC curve (AUC) values were 0.777, 0.777, and 0.759. The functions of target genes identified by GSEA appear to be mainly expressed in metabolism, signal transduction, tumorigenesis, and also the progression. The remarkable differences in IC50 and gene expression between risk groups in this study provide a deep insight for further systemic treatment. Higher macrophage scores were acquired in the high-risk group, of which patients showed more response to conventional chemotherapy drugs, such as AKT inhibitor VIII and Lapatinib, as well as immunotherapy strategies including anti-CD80, TNF SF4, CD276, and NRP1.

Conclusion

The prognosis of breast cancer can be independently predicted by the markers, which sheds light on further research of the specific role of lncRNAs which are oxidative stress-related and clinical treatment of breast cancer.

Landscape of prognosis and immunotherapy responsiveness under tumor glycosylation-related lncRNA patterns in breast cancer

Aberrant glycosylation, a post-translational modification of proteins, is regarded to engage in tumorigenesis and malignant progression of breast cancer (BC). The altered expression of glycosyltransferases causes abnormal glycan biosynthesis changes, which can serve as diagnostic hallmarks in BC. This study attempts to establish a predictive signature based on glycosyltransferase-related lncRNAs (GT-lncRNAs) in BC prognosis and response to immune checkpoint inhibitors (ICIs) treatment. We firstly screened out characterized glycosyltransferase-related genes (GTGs) through NMF and WGCNA analysis and identified GT-lncRNAs through co-expression analysis. By using the coefficients of 8 GT-lncRNAs, a risk score was calculated and its median value divided BC patients into high- and low-risk groups. The analyses unraveled that patients in the high-risk group had shorter survival and the risk score was an independent predictor of BC prognosis. Besides, the predictive efficacy of our risk score was higher than other published models. Moreover, ESTIMATE analysis, immunophenoscore (IPS), and SubMAP analysis showed that the risk score could stratify patients with distinct immune infiltration, and patients in the high-risk group might benefit more from ICIs treatment. Finally, the vitro assay showed that MIR4435-2HG might promote the proliferation and migration of BC cells, facilitate the polarization of M1 into M2 macrophages, enhance the migration of macrophages and increase the PD-1/PD-L1/CTLA4 expression. Collectively, our well-constructed prognostic signature with GT-lncRNAs had the ability to identify two subtypes with different survival state and responses to immune therapy, which will provide reliable tools for predicting BC outcomes and making rational follow-up strategies.

Genetic instability-related lncRNAs predict prognosis and influence the immune microenvironment in breast cancer

Genome instability is a hallmark of cancer, and the function of lncRNAs in regulating genomic stability has been gradually characterized. However, the prognostic value of lncRNAs related to genetic instability has not been found in breast cancer. Here we constructed a genetic instability-related lncRNA model including U62317.4, SEMA3B-AS1, MAPT-AS1, AC115837.2, LINC01269, AL645608.7, and GACAT2. This model can evaluate the risk and predict the survival outcomes of patients. Further analysis showed that the differentially expressed genes between the high- and low-risk groups were enriched in immunity and cornified envelope formation pathways. In addition, M2 macrophages infiltrated more obviously in the high-risk group. In summary, lncRNAs related to genetic instability may influence the development of breast cancer through immune infiltration and keratinization. This study provides a wider insight into breast cancer development and treatment.

Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection

Simple Summary

Patient stratification is clinically important because it allows us to understand the characteristics and establish treatment strategies for a group. Transcriptomic data play an important role in determining molecular subtypes and predicting survival. In the case of breast cancer, although the order of prognosis according to molecular subtypes is well known, there is heterogeneity even within a subtype. Therefore, patient stratification considering both molecular subtypes and survival outcomes is required. In this study, a methodology to handle this problem is presented. A genetic algorithm is used to select a set of genes, and a risk score is assigned to each patient using their expression level. According to the risk score, patients are ordered and stratified considering molecular subtypes and survival outcomes. Consequently, informative genes for patient stratification with respect to both aspects could be nominated, and the usefulness of the risk score was shown through comparison with other indicators.

Abstract

Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

Subtype and cell type specific expression of lncRNAs provide insight into breast cancer

Long non-coding RNAs (lncRNAs) are involved in breast cancer pathogenesis through chromatin remodeling, transcriptional and post-transcriptional gene regulation. We report robust associations between lncRNA expression and breast cancer clinicopathological features in two population-based cohorts: SCAN-B and TCGA. Using co-expression analysis of lncRNAs with protein coding genes, we discovered three distinct clusters of lncRNAs. In silico cell type deconvolution coupled with single-cell RNA-seq analyses revealed that these three clusters were driven by cell type specific expression of lncRNAs. In one cluster lncRNAs were expressed by cancer cells and were mostly associated with the estrogen signaling pathways. In the two other clusters, lncRNAs were expressed either by immune cells or fibroblasts of the tumor microenvironment. To further investigate the cis-regulatory regions driving lncRNA expression in breast cancer, we identified subtype-specific transcription factor (TF) occupancy at lncRNA promoters. We also integrated lncRNA expression with DNA methylation data to identify long-range regulatory regions for lncRNA which were validated using ChiA-Pet-Pol2 loops. lncRNAs play an important role in shaping the gene regulatory landscape in breast cancer. We provide a detailed subtype and cell type-specific expression of lncRNA, which improves the understanding of underlying transcriptional regulation in breast cancer.

Non-coding RNAs associated with autophagy and their regulatory role in cancer therapeutics

Cancer widely affects the world's health population and ranks second leading cause of death globally. Because of poor prognosis of various types of cancer such as sarcoma, lymphoma, adenomas etc., their high recurrence and metastasis rate and low early diagnosis rate have become concern lately. Role of autophagy in cancer progression is being studied since long. Autophagy is cell's self-degradative mechanism towards stress and has role in degradation of the cytoplasmic macromolecules which has potential to damage other cytosolic molecules. Autophagy can promote as well as inhibit tumorigenesis depending upon the associated protein combinations in cancer cells. Recent studies have shown that non-coding RNAs (ncRNAs) do not code for protein but play essential role in modulation of gene expression. At transcriptional level, different ncRNAs like lncRNAs, miRNAs and circRNAs directly or indirectly affect different stages of autophagy like autophagy-dependent and non-apoptotic cell death in cancer cells. This review focuses on the involvement of ncRNAs in autophagy and the modulation of several cancer signal transduction pathways in cancers such as lung, breast, prostate, pancreatic, thyroid, and kidney cancer.

Identification and validation of an epigenetically regulated long noncoding RNA model for breast cancer metabolism and prognosis

Background

Breast cancer (BC) is the leading cause of death among women, and epigenetic alterations that can dysregulate long noncoding RNAs (lncRNAs) are thought to be associated with cancer metabolism, development, and progression. This study investigated the epigenetic regulation of lncRNAs and its relationship with clinical outcomes and treatment responses in BC in order to identify novel and effective targets for BC treatment.

Methods

We comprehensively analysed DNA methylation and transcriptome data for BC and identified epigenetically regulated lncRNAs as potential prognostic biomarkers using machine learning and multivariate Cox regression analysis. Additionally, we applied multivariate Cox regression analysis adjusted for clinical characteristics and treatment responses to identify a set of survival-predictive lncRNAs, which were subsequently used for functional analysis of protein-encoding genes to identify downstream biological pathways.

Results

We identified a set of 1350 potential epigenetically regulated lncRNAs and generated a methylated lncRNA dataset for BC, MylnBrna, comprising 14 lncRNAs from a list of 20 epigenetically regulated lncRNAs significantly associated with tumour survival. MylnBrna stratifies patients into high-risk and low-risk groups with significantly different survival rates. These lncRNAs were found to be closely related to the biological pathways of amino acid metabolism and tumour metabolism, revealing a potential tumour-regulation function.

Conclusion

This study established a potential prognostic biomarker model (MylnBrna) for BC survival and offered an insight into the epigenetic regulatory mechanisms of lncRNAs in BC in the context of tumour metabolism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01256-2.

A Novel Necroptosis-Associated lncRNA Signature Can Impact the Immune Status and Predict the Outcome of Breast Cancer

Breast cancer (BRCA) is one of the leading causes of death among women worldwide, and drug resistance often leads to a poor prognosis. Necroptosis is a type of programmed cell death (PCD) and exhibits regulatory effects on tumor progression, but few studies have focused on the relationships between necroptosis-associated lncRNAs and BRCA. In this study, we established a signature basis of 7 necroptosis-related lncRNAs associated with prognosis and divided BRCA patients into high- and low-risk groups. Kaplan-Meier curves all showed an adverse prognosis for patients in the high-risk group. Cox assays confirmed that risk score was an independent prognostic factor for BRCA patients. The receiver operating characteristic (ROC) curve proved the predictive accuracy of the signature and the area under the curve (AUC) values of the risk score reached 0.722. The nomogram relatively accurately predicted the prognosis of the patients. GSEA analysis suggested that the related signaling pathways and biological processes enriched in the high- and low-risk groups may influence the tumor microenvironment (TME) of BRCA. ssGSEA showed the difference in immune cell infiltration, immune pathway activation, and immune checkpoint expression between the two risk groups, with the low-risk group more suitable for immunotherapy. According to the significant difference in IC50 between risk groups, patients can be guided for an individualized treatment plan. Overall, the authors established a prognostic signature consisting of 7 necroptosis-associated lncRNAs that can independently predict the clinical outcome of BRCA patients. The difference in the tumor immune microenvironment between the low- and high-risk populations may be the reason for the resistance to immunotherapy in some patients.

Roles of Long Non-coding RNAs in the Development of Aging-Related Neurodegenerative Diseases

Aging-related neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), are gradually becoming the primary burden of society and cause significant health-care concerns. Aging is a critical independent risk factor for neurodegenerative diseases. The pathological alterations of neurodegenerative diseases are tightly associated with mitochondrial dysfunction, inflammation, and oxidative stress, which in turn stimulates the further progression of neurodegenerative diseases. Given the potential research value, lncRNAs have attracted considerable attention. LncRNAs play complex and dynamic roles in multiple signal transduction axis of neurodegeneration. Emerging evidence indicates that lncRNAs exert crucial regulatory effects in the initiation and development of aging-related neurodegenerative diseases. This review compiles the underlying pathological mechanisms of aging and related neurodegenerative diseases. Besides, we discuss the roles of lncRNAs in aging. In addition, the crosstalk and network of lncRNAs in neurodegenerative diseases are also explored.

The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

Long non-coding RNA (lncRNA) is a prognostic biomarker for many types of cancer. Here, we aimed to study the prognostic value of lncRNA in Breast Invasive Carcinoma (BRCA). We downloaded expression profiles from The Cancer Genome Atlas (TCGA) datasets. Subsequently, we screened the differentially expressed genes between normal tissues and tumor tissues. Univariate Cox, LASSO regression, and multivariate Cox regression analysis were used to construct a lncRNA prognostic model. Finally, a nomogram based on the lncRNAs model was developed, and weighted gene co-expression network analysis (WGCNA) was used to predict mRNAs related to the model, and to perform function and pathway enrichment. We constructed a 6-lncRNA prognostic model. Univariate and multivariate Cox regression analysis showed that the 6-lncRNA model could be used as an independent prognostic factor for BRCA patients. We developed a nomogram based on the lncRNAs model and age, and showed good performance in predicting the survival rates of BRCA patients. Also, functional pathway enrichment analysis showed that genes related to the model were enriched in cell cycle-related pathways. Tumor immune infiltration analysis showed that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group. In general, the 6-lncRNA prognostic model and nomogram could be used as a practical and reliable prognostic tool for invasive breast cancer.

Identification of autophagy-related long non-coding RNA prognostic signature for breast cancer

Autophagy‐related long non‐coding RNAs (lncRNAs) disorders are related to the occurrence and development of breast cancer. The purpose of this study is to explore whether autophagy‐related lncRNA can predict the prognosis of breast cancer patients. The autophagy‐related lncRNAs prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We identified five autophagy‐related lncRNAs (MAPT‐AS1, LINC01871, AL122010.1, AC090912.1, AC061992.1) associated with prognostic value, and they were used to construct an autophagy‐related lncRNA prognostic signature (ALPS) model. ALPS model offered an independent prognostic value (HR = 1.664, 1.381‐2.006), where this risk score of the model was significantly related to the TNM stage, ER, PR and HER2 status in breast cancer patients. Nomogram could be utilized to predict survival for patients with breast cancer. Principal component analysis and Sankey Diagram results indicated that the distribution of five lncRNAs from the ALPS model tends to be low‐risk. Gene set enrichment analysis showed that the high‐risk group was enriched in autophagy and cancer‐related pathways, and the low‐risk group was enriched in regulatory immune‐related pathways. These results indicated that the ALPS model composed of five autophagy‐related lncRNAs could predict the prognosis of breast cancer patients.

Identification of KEY lncRNAs and mRNAs Associated with Oral Squamous Cell Carcinoma Progression

Background:

Oral squamous cell carcinoma (OSCC) has been the sixth most common cancer worldwide. Emerging studies showed long non-coding RNAs to play a key role in human cancers. However, the molecular mechanisms underlying the initiation and progression of OSCC remained to be further explored.

Objective:

The present study aimed to identify differentially expressed lncRNAs and mRNAs in OSCC.

Methods:

GSE30784 was analyzed to identify differentially expressed lncRNAs and mRNAs in OSCC. Protein-protein interaction network and co-expression network analyses were performed to reveal the potential roles of OSCC related mRNAs and lncRNAs.

Results:

In the present study, we identified 21 up-regulated lncRNAs and 54 down-regulated lncRNAs in OSCC progression. Next, we constructed a lncRNA related co-expression network in OSCC, which included 692 mRNAs and 2193 edges. Bioinformatics analysis showed that lncRNAs were widely co-expressed with regulating type I interferon signaling pathway, extracellular matrix organization, collagen catabolic process, immune response, ECM-receptor interaction, Focal adhesion, and PI3K-Akt signaling pathway. A key network, including lncRNA C5orf66-AS1, C21orf15, LOC100506098, PCBP1-AS1, LOC284825, OR7E14P, HCG22, and FLG-AS1, was found to be involved in the regulation of immune response to tumor cell, Golgi calcium ion transport, negative regulation of vitamin D receptor signaling pathway, and glycerol- 3-phosphate catabolic process. Moreover, we found higher expressions of CYP4F29P, PCBP1- AS1, HCG22, and C5orf66-AS1, which were associated with shorter overall survival time in OSCC samples.

Conclusions:

Our analysis can provide novel insights to explore the potential mechanisms underlying OSCC progression.

High-throughput proteomics of breast cancer interstitial fluid: identification of tumor subtype-specific serologically relevant biomarkers

Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor-specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple-negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High-grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low-grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype-specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of BC subtypes. Five of the most representative proteomics databases currently available were also used to estimate the potential for these selected proteins to serve as putative serological markers.

An Immune Gene-Related Five-lncRNA Signature for to Predict Glioma Prognosis

Background

The tumor immune microenvironment is closely related to the malignant progression and treatment resistance of glioma. Long non-coding RNA (lncRNA) plays a regulatory role in this process. We investigated the pathological mechanisms within the glioma microenvironment and potential immunotherapy resistance related to lncRNAs.

Method

We downloaded datasets derived from glioma patients and analyzed them by hierarchical clustering. Next, we analyzed the immune microenvironment of glioma, related gene expression, and patient survival. Coexpressed lncRNAs were analyzed to generate a model of lncRNAs and immune-related genes. We analyzed the model using survival and Cox regression. Then, univariate, multivariate, receiver operating characteristic (ROC), and principle component analysis (PCA) methods were used to verify the accuracy of the model. Finally, GSEA was used to evaluate which functions and pathways were associated with the differential genes.

Results

Normal brain tissue maintains a low-medium immune state, and gliomas are clearly divided into three groups (low to high immunity). The stromal, immune, and estimate scores increased along with immunity, while tumor purity decreased. Further, human leukocyte antigen (HLA), programmed cell death-1 (PDL1), T cell immunoglobulin and mucin domain 3 (TIM-3), B7-H3, and cytotoxic T lymphocyte-associated antigen-4 (CTLA4) expression increases concomitantly with immune state, and the patient prognosis worsens. Five immune gene-related lncRNAs (AP001007.1, LBX-AS1, MIR155HG, MAPT-AS1, and LINC00515) were screened to construct risk models. We found that risk scores are related to patient prognosis and clinical characteristics, and are positively correlated with PDL1, TIM-3, and B7-H3 expression. These lncRNAs may regulate the tumor immune microenvironment through cytokine-cytokine receptor interactions, complement, and coagulation cascades, and may promote CD8 + T cell, regulatory T cell, M1 macrophage, and infiltrating neutrophils activity in the high-immunity group. In vitro, the abnormal expression of immune-related lncRNAs and the relationship between risk scores and immune-related indicators (PDL1, CTLA4, CD3, CD8, iNOS) were verified by q-PCR and immunohistochemistry (IHC).

Conclusion

For the first time, we constructed immune gene-related lncRNA risk models. The risk score may be a new biomarker for tumor immune subtypes and provide molecular targets for glioma immunotherapy.

Prognostic value of long non-coding RNAs in triple negative breast cancer: A PRISMA-compliant meta-analysis

BACKGROUND

Triple-negative breast cancer (TNBC) is the most aggressive and lethal subtype of breast cancer. Accumulating evidence showed long non-coding RNAs (lncRNAs) are abnormally expressed in TNBC and could be valuable prognostic tools for TNBC patients. This study aims to research the prognostic value of lncRNAs in TNBC, using the meta-analysis method.

METHODS

We performed a detailed literature search on Pubmed, Scopus, and Web of Science for studies on the prognostic value of lncRNAs in TNBC. The meta-analysis method was used to determine the relationship between lncRNAs expression and survival of TNBC patients.

RESULTS

A total of 2803 TNBC patients and 24 lncRNAs from 27 different articles were included in the present study. Subgroup analysis demonstrated that overexpression of lncRNAs in a group that is upregulated in TBNC showed a significant association with poor overall survival (HR = 1.86, 95%CI = 1.45-2.27, I = 41.9%) and disease-free survival (HR = 1.85, 95%CI = 1.37-2.33, I = 0%). Conversely, overexpression of lncRNAs in a downregulation group was markedly related to good overall survival (HR = 0.60, 95%CI = 0.43-0.77, I = 28.6%). Moreover, expression of lncRNA SNHG12, MALAT1, HOTAIR, HIF1A-AS2, HULC, LINC00096, ZEB2-AS1, LUCAT1, and LINC000173 showed a marked correlation with positive lymph node metastasis (LNM), while lncRNA MIR503HG, GAS5, TCONS_l2_00002973 showed the opposite effect. High expression level of MALAT1, HIF1A-AS2, HULC, LINC00096, ADPGK-AS1, ZEB2-AS1, LUCAT1 were positively correlated with distant metastasis (DM), while lncRNA MIR503HG showed the opposite effect. In addition, the mechanisms of lncRNAs in TNBC were summarized.

CONCLUSIONS

This meta-analysis demonstrated that abnormally expressed lncRNA were significantly associated with the survival of TNBC patients and may serve as biomarkers and therapeutic targets for TNBC prognosis.

Identification of long non‑coding RNAs expressed during the osteogenic differentiation of human bone marrow‑derived mesenchymal stem cells obtained from patients with ONFH

Long non-coding RNAs (lncRNAs) are crucial for the occurrence and development of numerous diseases. Although lncRNAs are involved in the biological activities of stem cells and play crucial roles in stem cell differentiation, the expression of specific lncRNAs during human bone marrow-derived mesenchymal stem cell (hBMSC) osteogenic differentiation in osteonecrosis of the femoral head (ONFH) and their regulatory roles have not yet been fully elucidated. To the best of our knowledge, the present study is the first to characterize lncRNA expression profiles during hBMSC osteogenic differentiation in ONFH using microarray analysis and RT-qPCR to confirm the microarray data. A total of 24 downregulated and 24 upregulated lncRNAs were identified and the results of RT-qPCR were found to be consistent with those of microarray analysis. Bioinformatics analyses, using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, were conducted to explore the possible mechanisms and identify the signaling pathways that the lncRNAs are involved in. GO analysis revealed significant changes in the intracellular organelle, Ras protein signal transduction and transferase activity. KEGG pathway analysis revealed that the lncRNAs were closely associated with fatty acid metabolism, apoptosis and the TGF-β signaling pathway. The overexpression of MAPT antisense RNA 1 (MAPT-AS1) was found to promote osteogenesis and inhibit the adipogenesis of hBMSCs at the cellular and mRNA levels. On the whole, the findings of the present study identified the lncRNAs and their roles in hBMSCs undergoing osteogenic differentiation in ONFH and provide a new perspective for the pathogenesis of ONFH.

Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Long noncoding RNA (lncRNA) plays a critical role in the development of tumors. The aim of our study was construction of a lncRNA signature model to predict breast cancer (BRCA) patient survival. We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. Corresponding protein-coding genes were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. Finally, 521 differentially expression lncRNA were obtained. We constructed a ten-lncRNA signature model (LINC01208, RP5-1011O1.3, LINC01234, LINC00989, RP11-696F12.1, RP11-909N17.2, CTC-297N7.9, CTA-384D8.34, CTC-276P9.4, and MAPT-IT1) to predict BRCA patient survival using the multivariate Cox proportional hazard regression model. The C-index was 0.712, and AUC scores of training, test, and entire sets were 0.746, 0.717, and 0.732, respectively. Univariate Cox regression analysis indicated that age, tumor status, N status, M status, and risk score were significantly related to overall survival in patients with BRCA. Further, the multivariate analysis showed that risk score and M status had outstanding independent prognostic values, both with p < 0.001. The Gene Ontology (GO) function and KEEG pathway analysis was primarily enriched in immune response, receptor binding, external surface of plasma membrane, signal transduction, cytokine-cytokine receptor interaction, and cell adhesion molecules (CAMs). Finally, we constructed a ten-lncRNA signature model that can serve as an independent prognostic model to predict BRCA patient survival.

Microtubule-associated protein tau (MAPT) is a promising independent prognostic marker and tumor suppressive protein in clear cell renal cell carcinoma

INTRODUCTION

Microtubule-associated protein tau (MAPT) overexpression has been linked to poor prognosis in several cancers. MAPT-AS1 is a long noncoding RNA existing at the antisense strand of the MAPT promoter region. The clinical significance of MAPT and MAPT-AS-1 in clear cell renal cell carcinoma (ccRCC) is unknown. This study aimed to assess the expression and function of MAPT and MAPT-AS1 in ccRCC.

METHODS

The expression of MAPT was determined using immunohistochemistry in ccRCC. The effects of MAPT knockdown on cell growth and invasion were evaluated and the interaction between MAPT and microtubule-associated protein tau antisense (MAPT-AS1) were analyzed. The expression of MAPT-AS1 was determined using quantitative reverse transcription polymerase chain reaction in ccRCC tissues. We investigated the effect of MAPT-AS1 knockdown on cell growth and invasion. We analyzed the regulation of MAPT and MAPT-AS1.

RESULTS

Immunohistochemistry in 135 ccRCC cases showed that 61% of the cases were positive for MAPT. Kaplan-Meier analysis showed that the low expression of MAPT was associated with poor overall survival after nephrectomy. Knockdown of MAPT enhanced cell growth and invasion. quantitative reverse transcription polymerase chain reaction revealed a positive correlation between MAPT and MAPT-AS1. The expression of MAPT-AS1 was higher in ccRCC tissue than in nonneoplastic kidney tissue. Kaplan-Meier analysis showed that the low expression of MAPT-AS1 was associated with poor overall survival after nephrectomy by in silico analysis. MAPT-AS1 knockdown promoted cell growth and invasion activity. P53 knockout suppressed the expression of MAPT and MAPT-AS1.

CONCLUSION

These results suggest that MAPT and MAPT-AS1 may be promising predictive biomarkers for survival and play a tumor-suppressive role in ccRCC.

A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma

Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non-coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis-related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood-based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk-score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high-/low-risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1. Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer.