Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

Ferroptosis contributes to the progression of female-specific neoplasms, from breast cancer to gynecological malignancies in a manner regulated by non-coding RNAs: Mechanistic implications

Ferroptosis, a recently identified type of non-apoptotic cell death, triggers the elimination of cells in the presence of lipid peroxidation and in an iron-dependent manner. Indeed, ferroptosis-stimulating factors have the ability of suppressing antioxidant capacity, leading to the accumulation of reactive oxygen species (ROS) and the subsequent oxidative death of the cells. Ferroptosis is involved in the pathophysiological basis of different maladies, such as multiple cancers, among which female-oriented malignancies have attracted much attention in recent years. In this context, it has also been unveiled that non-coding RNA transcripts, including microRNAs, long non-coding RNAs, and circular RNAs have regulatory interconnections with the ferroptotic flux, which controls the pathogenic development of diseases. Furthermore, the potential of employing these RNA transcripts as therapeutic targets during the onset of female-specific neoplasms to modulate ferroptosis has become a research hotspot; however, the molecular mechanisms and functional alterations of ferroptosis still require further investigation. The current review comprehensively highlights ferroptosis and its association with non-coding RNAs with a focus on how this crosstalk affects the pathogenesis of female-oriented malignancies, from breast cancer to ovarian, cervical, and endometrial neoplasms, suggesting novel therapeutic targets to decelerate and even block the expansion and development of these tumors.

A1BG-AS1 promotes adriamycin resistance of breast cancer by recruiting IGF2BP2 to upregulate ABCB1 in an m6A-dependent manner

Adriamycin (ADR) resistance is an obstacle for chemotherapy of breast cancer (BC). ATP binding cassette subfamily B member 1 (ABCB1) expression is indicated to be closely related to the drug resistance of cancer cells. The current work intended to explore the molecular mechanisms to regulate ABCB1 in BC cells with ADR resistance. We found that long noncoding RNA (lncRNA) A1BG antisense RNA 1 (A1BG-AS1) is upregulated in ADR resistant BC cell lines (MCF-7/ADR, MDA-MB-231/ADR). A1BG-AS1 knockdown enhanced the ADR sensitivity by suppressing the viability, proliferation potential and migration ability, and facilitating cell apoptosis in BC. Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) is known to be an m6A reader to modulate the stability of mRNA transcripts in an m6A-dependent manner, which was a shared RNA binding protein (RBP) for A1BG-AS1 and ABCB1. The interaction of IGF2BP2 with A1BG-AS1 or ABCB1 was explored and verified using RNA pulldown and RNA immunoprecipitation (RIP) assays. ABCB1 mRNA and protein expression was positively regulated by A1BG-AS1 and IGF2BP2 in BC cells. ABCB1 mRNA expression was stabilized by A1BG-AS1 via recruiting IGF2BP2 in an m6A-dependent manner. Moreover, rescue assays demonstrated that A1BG-AS1 enhanced BC ADR resistance by positively modulating ABCB1. Xenograft mouse models were used to explore whether A1BG-AS1 affected the ADR resistance in BC in vivo. The findings indicated that A1BG-AS1 silencing inhibited tumor growth and alleviated ADR resistance in vivo. In conclusion, A1BG-AS1 enhances the ADR resistance of BC by recruiting IGF2BP2 to upregulate ABCB1 in an m6A-dependent manner.

The current advances of lncRNAs in breast cancer immunobiology research

Breast cancer is the most frequently diagnosed malignancy and the leading cause of cancer-related death in women worldwide. Breast cancer development and progression are mainly associated with tumor-intrinsic alterations in diverse genes and signaling pathways and with tumor-extrinsic dysregulations linked to the tumor immune microenvironment. Significantly, abnormal expression of lncRNAs affects the tumor immune microenvironment characteristics and modulates the behavior of different cancer types, including breast cancer. In this review, we provide the current advances about the role of lncRNAs as tumor-intrinsic and tumor-extrinsic modulators of the antitumoral immune response and the immune microenvironment in breast cancer, as well as lncRNAs which are potential biomarkers of tumor immune microenvironment and clinicopathological characteristics in patients, suggesting that lncRNAs are potential targets for immunotherapy in breast cancer.

The Clinical Utility of lncRNAs and Their Application as Molecular Biomarkers in Breast Cancer

Given their tumor-specific and stage-specific gene expression, long non-coding RNAs (lncRNAs) have demonstrated to be potential molecular biomarkers for diagnosis, prognosis, and treatment response. Particularly, the lncRNAs DSCAM-AS1 and GATA3-AS1 serve as examples of this because of their high subtype-specific expression profile in luminal B-like breast cancer. This makes them candidates to use as molecular biomarkers in clinical practice. However, lncRNA studies in breast cancer are limited in sample size and are restricted to the determination of their biological function, which represents an obstacle for its inclusion as molecular biomarkers of clinical utility. Nevertheless, due to their expression specificity among diseases, such as cancer, and their stability in body fluids, lncRNAs are promising molecular biomarkers that could improve the reliability, sensitivity, and specificity of molecular techniques used in clinical diagnosis. The development of lncRNA-based diagnostics and lncRNA-based therapeutics will be useful in routine medical practice to improve patient clinical management and quality of life.

Metformin and long non-coding RNAs in breast cancer

Breast cancer (BC) is the second most common cancer and cause of death in women. In recent years many studies investigated the association of long non-coding RNAs (lncRNAs), as novel genetic factors, on BC risk, survival, clinical and pathological features. Recent studies also investigated the roles of metformin treatment as the firstline treatment for type 2 diabetes (T2D) played in lncRNAs expression/regulation or BC incidence, outcome, mortality and survival, separately. This comprehensive study aimed to review lncRNAs associated with BC features and identify metformin-regulated lncRNAs and their mechanisms of action on BC or other types of cancers. Finally, metformin affects BC by regulating five BC-associated lncRNAs including GAS5, HOTAIR, MALAT1, and H19, by several molecular mechanisms have been described in this review. In addition, metformin action on other types of cancers by regulating ten lncRNAs including AC006160.1, Loc100506691, lncRNA-AF085935, SNHG7, HULC, UCA1, H19, MALAT1, AFAP1-AS1, AC026904.1 is described.

Development and Validation of Ferroptosis-Related lncRNAs as Prognosis and Diagnosis Biomarkers for Breast Cancer

Breast cancer (BC) is one of the most common malignancies affecting women. Ferroptosis is a novel cancer treatment option. The present study is aimed to identify suitable ferroptosis-related lncRNAs to predict and diagnose BC. Differential expression and Cox regression analyses were used to screen suitable prognostic biomarkers and construct a suitable risk model. We identified four ferroptosis-related differentially expressed lncRNAs (FR-DELs) (LINC01152, AC004585.1, MAPT-IT1, and AC026401.3), which were independently correlated with the overall survival of BC patients. The area under the curve value of the prognostic model using those four biomarkers was over 0.60 in all three groups. The sensitivity and specificity of the diagnostic model using those four biomarkers were 86.89% and 86.73%, respectively. Our present study indicated that these four FR-DELs (LINC01152, AC004585.1, MAPT-IT1, and AC026401.3) could be prognostic biomarkers for BC, although clinical validation studies are required.

A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer

Long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on N7-methylguanosine (m7G)-related lncRNA in breast cancer is lacking. Therefore, the present study explored the prognostic value, gene expression characteristics, and effects of m7G-related lncRNA on tumor immune cell infiltration and tumor mutational burden (TMB) in breast cancer. lncRNA expression matrices and clinical follow-up data of patients with breast cancer were obtained from The Cancer Genome Atlas, revealing eight significantly differentially expressed and prognostically relevant m7G-related lncRNAs in breast cancer tissues: BAIAP2-DT, COL4A2-AS1, FARP1-AS1, RERE-AS1, NDUFA6-DT, TFAP2A-AS1, LINC00115, and MIR302CHG. A breast cancer prognostic signature was created based on these m7G-related lncRNAs according to least absolute shrinkage and selection operator Cox regression. The prognostic signature combined with potential prognostic factors showed independent prognostic value, reliability, and specificity. Meanwhile, we constructed a risk score-based nomogram to assist clinical decision-making. Gene set enrichment analysis revealed that low- and high-risk group were associated with metabolism-related pathways. Our study demonstrated the association between tumor immune cell infiltration based on analyses with the CIBERSORT algorithm and prognostic signature. We also assessed the correlation between prognostic signature and TMB. Lastly, quantitative real-time polymerase chain reaction analysis was performed to validate differentially expressed lncRNAs. The effective prognostic signature based on m7G-related lncRNAs has the potential to predict the survival prognosis of patients with breast cancer. The eight m7G-related lncRNAs identified in this study might represent potential biomarkers and therapeutic targets of breast cancer.

Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Background

Advances in our understanding of the tumor microenvironment have radically changed the cancer field, highlighting the emerging need for biomarkers of an active, favorable tumor immune phenotype to aid treatment stratification and clinical prognostication. Numerous immune-related gene signatures have been defined; however, their prognostic value is often limited to one or few cancer types. Moreover, the area of non-coding RNA as biomarkers remains largely unexplored although their number and biological roles are rapidly expanding.

Methods

We developed a multi-step process to identify immune-related long non-coding RNA signatures with prognostic connotation in multiple TCGA solid cancer datasets.

Results

Using the breast cancer dataset as a discovery cohort we found 2988 differentially expressed lncRNAs between immune favorable and unfavorable tumors, as defined by the immunologic constant of rejection (ICR) gene signature. Mapping of the lncRNAs to a coding-non-coding network identified 127 proxy protein-coding genes that are enriched in immune-related diseases and functions. Next, we defined two distinct 20-lncRNA prognostic signatures that show a stronger effect on overall survival than the ICR signature in multiple solid cancers. Furthermore, we found a 3 lncRNA signature that demonstrated prognostic significance across 5 solid cancer types with a stronger association with clinical outcome than ICR. Moreover, this 3 lncRNA signature showed additional prognostic significance in uterine corpus endometrial carcinoma and cervical squamous cell carcinoma and endocervical adenocarcinoma as compared to ICR.

Conclusion

We identified an immune-related 3-lncRNA signature with prognostic connotation in multiple solid cancer types which performed equally well and in some cases better than the 20-gene ICR signature, indicating that it could be used as a minimal informative signature for clinical implementation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03654-7.

Downregulation of lncRNA USP2‑AS1 in the placentas of pregnant women with non‑diabetic fetal macrosomia promotes trophoblast cell proliferation

Macrosomia is a common perinatal complication, with a series of adverse effects on newborns and pregnant women. However, the effects of long non-coding RNAs (lncRNAs) on non-diabetic fetal macrosomia (NDFMS) remain unclear. The aim of the present study was to investigate whether aberrant lncRNA expression in the placenta is involved in the pathogenesis of NDFMS and to elucidate its biological mechanisms. The expression profile of lncRNAs in the placentas of pregnant women with NDFMS was investigated using an Agilent Human LncRNA Microarray. Differentially expressed lncRNAs were selected for validation using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Additionally, the function of lncRNA ubiquitin-specific peptidase 2 antisense RNA 1 (USP2-AS1) was investigated using a trophoblast cell line. The results revealed that 763 lncRNAs were upregulated and 129 lncRNAs were downregulated in the placentas of women in the NDFMS group (|FC| ≥2.0). A total of 10 lncRNAs (|FC| ≥4.0, signal value ≥50) were selected for validation using two-stage RT-qPCR, indicating that the expression trends of the 10 differentially expressed lncRNAs in the NDFMS group (n=8 vs. 8 and 48 vs. 48) were consistent with the microarray data. In addition, a significant downregulation in the levels of lncRNA USP2-AS1 was observed in both the microarray data and second-stage verification. The overexpression of lncRNA USP2-AS1 induced G1 phase cell cycle arrest and the number of cells entering S phase was reduced. In addition, the viability of HTR-8/SVneo cells was significantly inhibited when lncRNA USP2-AS1 was overexpressed. Therefore, these findings demonstrated that lncRNAs were significantly differentially expressed in the placentas of pregnant women with NDFMS and that the downregulation of lncRNA USP2-AS1 may be involved in the pathogenesis of NDFMS, by promoting trophoblast cell viability.

A Novel lncRNA Panel for Risk Stratification and Immune Landscape in Breast Cancer Patients

Purpose

In recent years, breast cancer (BC) has been a primary cause of mortality in women. However, the underlying mechanisms remain to be elucidated. Accumulating evidence has supported the hypothesis that long noncoding RNAs (lncRNAs) play central roles in the progression of cancer. We aimed to construct an immune-related lncRNA panel to predict the prognosis of patients with BC and evaluate the immune features.

Methods

The expression profiles of patients with BC were obtained from The Cancer Genome Atlas (TCGA) database to screen the differentially expressed lncRNAs (DELs). Pearson’s correlation analysis was employed to filter the DELs related to the immune-associated genes. Univariate Cox regression, the LASSO algorithm, and multivariate Cox regression analyses were conducted to establish the model. Functional enrichment analyses and biological experiments were performed to explore the immune activity of the lncRNA panel.

Results

A four-immune-related lncRNA panel (IRLP) composed of AC022196.1, ARHGAP26-AS1, DPYD-AS1 and PURPL was established in TCGA training cohort. The prognostic accuracy of the predictive model was confirmed in TCGA internal validation cohort, TCGA entire cohort and Qilu external validation cohort. Bioinformatics analyses indicated that the IRLP had a close relationship with tumour infiltrating immune cells and immunomodulatory biomarkers. The biological functions of the four immune-related lncRNAs in BC were first investigated in vitro and in vivo. PURPL was indicated to play a central role in the regulation of macrophage recruitment and polarization via CCL2.

Conclusion

Our study identified IRLP as a reliable prognostic indicator with great potential for clinical application in personalized immunotherapy.

The Mechanisms of lncRNA-Mediated Multidrug Resistance and the Clinical Application Prospects of lncRNAs in Breast Cancer

Simple Summary

Multidrug resistance (MDR) is a major cause of breast cancer (BC) chemotherapy failure. Long noncoding RNAs (lncRNAs) have been shown closely related to the chemoresistance of BC. In this work, the mechanisms of lncRNA-mediated MDR in BC were elaborated from eight sections, including apoptosis, autophagy, DNA repair, cell cycle, drug efflux, epithelial-mesenchymal transition, epigenetic modification and the tumor microenvironment. Additionally, we also discuss the clinical significance of lncRNAs, which may be biomarkers for diagnosis, therapy and prognosis.

Abstract

Breast cancer (BC) is a highly heterogeneous disease and presents a great threat to female health worldwide. Chemotherapy is one of the predominant strategies for the treatment of BC; however, multidrug resistance (MDR) has seriously affected or hindered the effect of chemotherapy. Recently, a growing number of studies have indicated that lncRNAs play vital and varied roles in BC chemoresistance, including apoptosis, autophagy, DNA repair, cell cycle, drug efflux, epithelial-mesenchymal transition (EMT), epigenetic modification and the tumor microenvironment (TME). Although thousands of lncRNAs have been implicated in the chemoresistance of BC, a systematic review of their regulatory mechanisms remains to be performed. In this review, we systematically summarized the mechanisms of MDR and the functions of lncRNAs mediated in the chemoresistance of BC from the latest literature. These findings significantly enhance the current understanding of lncRNAs and suggest that they may be promising prognostic biomarkers for BC patients receiving chemotherapy, as well as therapeutic targets to prevent or reverse chemoresistance.

Pyroptosis-related molecular classification and immune microenvironment infiltration in breast cancer: A novel therapeutic target

The underlying role of pyroptosis in breast cancer (BC) remains unknown. Herein, we investigated the correlations of 33 pyroptosis‐related genes (PRGs) with immune checkpoints and immune cell infiltrations in BC patients based on The Cancer Genome Atlas cohort (n = 996) and Gene Expression Omnibus cohort (n = 3,262). Enrichment analysis revealed that these PRGs mainly functioned in pyroptosis, inflammasomes and regulation of autophagy pathway. Four prognostic independent PRGs (CASP9, TIRAP, GSDMC and IL18) were identified. Then, cluster 1/2 was recognized using consensus clustering for these four PRGs. Patients from cluster 1 had a favourable prognosis and diverse immune cell infiltrations. A nomogram was developed based on age, TNM stage, tumour subtype and pyroptosis score. Patients with the high‐risk group exhibited worse 5‐year OS, and the result was consistent in the external cohort. Additionally, high‐risk group patients were associated with downregulated immune checkpoint expression. Further analysis suggested that the high‐risk group patients were associated with a higher IC50 of paclitaxel, doxorubicin, cisplatin, methotrexate and vinorelbine. In summarizing, the pyroptosis score‐based nomogram might serve as an independent prognostic predictor and could guide medication for chemotherapy. Additionally, it may bring novel insight into the regulation of tumour immune microenvironment in BC and help to achieve precision immunotherapy.

A Novel Prognostic Signature of Immune-Related Long Noncoding RNA Pairs for Tumor-Infiltrating Immune Cells and Drug Susceptibility in Breast Cancer

Prognostic signatures of specific immune-related long noncoding RNAs (irlncRNAs) have been elucidated with the development of immunotherapy for breast cancer, but the heterogeneity of gene expression in different patients still limits their effectiveness. We constructed a new prognostic signature based on the relative expression of differentially expressed irlncRNA (DEirlncRNA) pairs and analyzed its clinical application in 1069 patients from The Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) containing 745 White patients, 180 Black and African American patients, 58 Asian patients, 181 stage I patients, 606 stage II patients, 240 stage III patients, and 20 stage IV patients. Data from TCGA-BRCA and ImmPort were used to screen DEirlncRNAs, and the DEirlncRNA pairs were established by cyclical single comparison of each DEirlncRNA. After the data optimization, we constructed a signature containing 24 DEirlncRNA pairs. Risk groups of this signature were defined using the cutoff value from the 10-year survival receiver operating characteristic curve, and Kaplan-Meier analysis verified its prognostic effectiveness. Furthermore, we confirmed this signature as an independent prognostic factor and confirmed its close association with traditional clinicopathological factors. Moreover, this risk signature was closely related to tumor-infiltrating immune cells and drug susceptibility. In short, we successfully constructed a risk signature of DEirlncRNA pairs, which might provide new insights for breast cancer precision therapy.

LncRNA-SNX17 Promotes HTR-8/SVneo Proliferation and Invasion Through miR-517a/IGF-1 in the Placenta of Diabetic Macrosomia

Gestational diabetes mellitus (GDM) has become a worldwide problem in recent years. Macrosomia, a primary consequence of GDM, has short-term and life-long consequences in the offspring of mothers with GDM. Our previous study showed that miR-517a was dysregulated in placenta and plasma of fetal growth restriction through inhibiting invasion of trophoblast and might be closely related with the regulation of birth weight by the placenta. To further investigate the mechanism of miR-517a, we conducted genome-wide microarray profile of lncRNAs. lncRNA-SNX17 was found to be significantly upregulated in the placenta of diabetic macrosomia by qRT-PCR, and the expression of miR-517a and IGF-1 were measured by qRT-PCR and Western blot. Interestingly, significant inverse correlations of the miR-517a with both lncRNA-SNX17 and IGF-1 expression were revealed in the placenta of diabetic macrosomia. Bioinformatic prediction also revealed that both lncRNA-SNX17 and IGF-1 possessed binding sites for miR-517a, which were then confirmed by luciferase report assay. LncRNA-SNX17 overexpression reduced the expression of miR-517a and increased the IGF-1 expression in HTR-8/SVneo human trophoblast cell line and thus enhanced the proliferation of HTR-8/SVneo. The enhancement of HTR-8/SVneo proliferation by lncRNA-SXN17 could be nullified by co-transfection of miR-517a mimics. The data suggested that lncRNA-SNX17 might promote the trophoblast proliferation through miR-517a/IGF-1 pathway and might play a role in the placentation of diabetic macrosomia.

Nine Pyroptosis-Related lncRNAs are Identified as Biomarkers for Predicting the Prognosis and Immunotherapy of Endometrial Carcinoma

Background

Endometrial carcinoma (EC) is one of the most common malignancies. Immunotherapy has shown promising effects in the treatment against specific subtypes of EC.

Methods

The RNA and clinical information of patients with EC were acquired from The Cancer Gene Atlas (TCGA) database. Firstly, the differentially expressed pyroptosis-related lncRNAs (PRLs) were screened between the tumor and normal control tissue. Secondly, the PRLs closely related to survival were identified by univariate and multivariate regression analysis, based on which, we evaluated the risk score for each EC patient to construct a risk signature. Moreover, we assessed the prognostic value, clinical relevance immunity, and immunotherapy based on this signature.

Results

We screened out 9 individual PRLs (AC087491.1, AL353622.1, AL035530.2, LINC02036, AL021578.1, AL390195.2, AC009097.2, AC004585.1, and AC244517.7) closely related to the prognosis of EC. Kaplan–Meier analyses showed a poorer prognosis for the patients in the high-risk FRLs signature (P < 0.001). The area under the curve (AUC) for 1 year, 2 years, 3 years was 0.693, 0.694, 0.750, respectively. Our risk model could be considered as an independent prognostic marker for EC (P < 0.001, HR:2.172, 95% CI:1.532–3.079). Moreover, immune functions and checkpoints were generally different in the 2 groups. Simulation analysis by termed immunophenoscores hinted that immunotherapy might bring optimal therapeutic effect in the low-risk group.

Conclusion

We successfully developed a novel signature with 9 lncRNAs related to pyroptosis, which may be used as biomarkers to evaluate the prognosis and immune treatment of EC.

Construction of Prognostic Risk Model of 5-Methylcytosine-Related Long Non-Coding RNAs and Evaluation of the Characteristics of Tumor-Infiltrating Immune Cells in Breast Cancer

Purpose: The role of 5-methylcytosine-related long non-coding RNAs (m5C-lncRNAs) in breast cancer (BC) remains unclear. Here, we aimed to investigate the prognostic value, gene expression characteristics, and correlation between m5C-lncRNA risk model and tumor immune cell infiltration in BC. Methods: The expression matrix of m5C-lncRNAs in BC was obtained from The Cancer Genome Atlas database, and the lncRNAs were analyzed using differential expression analysis as well as univariate and multivariate Cox regression analysis to eventually obtain BC-specific m5C-lncRNAs. A risk model was developed based on three lncRNAs using multivariate Cox regression and the prognostic value, accuracy, as well as reliability were verified. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment of the risk model. CIBERSORT algorithm and correlation analysis were used to explore the characteristics of the BC tumor-infiltrating immune cells. Finally, reverse transcription-quantitative polymerase chain reaction was performed to detect the expression level of three lncRNA in clinical samples. Results: A total of 334 differential m5C-lncRNAs were identified, and three BC-specific m5C-lncRNAs were selected, namely AP005131.2, AL121832.2, and LINC01152. Based on these three lncRNAs, a highly reliable and specific risk model was constructed, which was proven to be closely related to the prognosis of patients with BC. Therefore, a nomogram based on the risk score was built to assist clinical decisions. GSEA revealed that the risk model was significantly enriched in metabolism-related pathways and was associated with tumor immune cell infiltration based on the analysis with the CIBERSORT algorithm. Conclusion: The efficient risk model based on m5C-lncRNAs associated with cancer metabolism and tumor immune cell infiltration could predict the survival prognosis of patients, and AP005131.2, AL121832.2, and LINC01152 could be novel biomarkers and therapeutic targets for BC.

Dissecting the Role of N6-Methylandenosine-Related Long Non-coding RNAs Signature in Prognosis and Immune Microenvironment of Breast Cancer

Breast cancer (BC) represents a molecularly and clinically heterogeneous disease. Recent progress in immunotherapy has provided a glimmer of hope for several BC subtypes. The relationship between N6-methyladenosine (m6A) modification and long non-coding RNAs (LncRNAs) is still largely unexplored in BC. Here, with the intention to dissect the landscape of m6A-related lncRNAs and explore the immunotherapeutic value of the m6A-related lncRNA signature, we identified m6A-related lncRNAs by co-expression analysis from The Cancer Genome Atlas (TCGA) and stratified BC patients into different subgroups. Furthermore, we generated an m6A-related lncRNA prognostic signature. Four molecular subtypes were identified by consensus clustering. Cluster 3 preferentially had favorable prognosis, upregulated immune checkpoint expression, and high level of immune cell infiltration. Twenty-one m6A-related lncRNAs were applied to construct the m6A-related lncRNA model (m6A-LncRM). Survival analysis and receiver operating characteristic (ROC) curves further confirmed the prognostic value and prediction performance of m6A-LncRM. Finally, high- and low-risk BC subgroups displayed significantly different clinical features and immune cell infiltration status. Overall, our study systematically explored the prognostic value of the m6A-related LncRNAs and identified a high immunogenicity BC subtype. The proposed m6A-related LncRNA model might serve as a robust prognostic signature and attractive immunotherapeutic targets for BC treatment.

CDKN1C as a prognostic biomarker correlated with immune infiltrates and therapeutic responses in breast cancer patients

Breast cancer (BC) prognosis and therapeutic sensitivity could not be predicted efficiently. Previous evidence have shown the vital roles of CDKN1C in BC. Therefore, we aimed to construct a CDKN1C‐based model to accurately predicting overall survival (OS) and treatment responses in BC patients. In this study, 995 BC patients from The Cancer Genome Atlas database were selected. Kaplan‐Meier curve, Gene set enrichment and immune infiltrates analyses were executed. We developed a novel CDKN1C‐based nomogram to predict the OS, verified by the time‐dependent receiver operating characteristic curve, calibration curve and decision curve. Therapeutic response prediction was followed based on the low‐ and high‐nomogram score groups. Our results indicated that low‐CDKN1C expression was associated with shorter OS and lower proportion of naïve B cells, CD8 T cells, activated NK cells. The predictive accuracy of the nomogram for 5‐year OS was superior to the tumour‐node‐metastasis stage (area under the curve: 0.746 vs. 0.634, p < 0.001). The nomogram exhibited excellent predictive performance, calibration ability and clinical utility. Moreover, low‐risk patients were identified with stronger sensitivity to therapeutic agents. This tool can improve BC prognosis and therapeutic responses prediction, thus guiding individualized treatment decisions.

Construction and Verification of a Hypoxia-Related 4-lncRNA Model for Prediction of Breast Cancer

Introduction

Breast cancer is the most common form of cancer worldwide and a serious threat to women. Hypoxia is thought to be associated with poor prognosis of patients with cancer. Long non-coding RNAs are differentially expressed during tumorigenesis and can serve as unambiguous molecular biomarkers for the prognosis of breast cancer.

Methods

Here, we accessed the data from The Cancer Genome Atlas for model construction and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to identify biological functions. Four prognostic hypoxia-related lncRNAs identified by univariate, LASSO, and multivariate Cox regression analyses were used to develop a prognostic risk-related signature. Kaplan–Meier and receiver operating characteristic curve analyses were performed, and independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate the specificity and sensitivity of the signature. Survival analysis and receiver operating characteristic curve analyses of the validation cohort were operated to corroborate the robustness of the model.

Results

Our results demonstrate the development of a reliable prognostic gene signature comprising four long non-coding RNAs (AL031316.1, AC004585.1, LINC01235, and ACTA2-AS1). The signature displayed irreplaceable prognostic power for overall survival in patients with breast cancer in both the training and validation cohorts. Furthermore, immune cell infiltration analysis revealed that B cells, CD4 T cells, CD8 T cells, neutrophils, and dendritic cells were significantly different between the high-risk and low-risk groups. The high-risk and low-risk groups could be precisely distinguished using the risk signature to predict patient outcomes.

Discussion

In summary, our study proves that hypoxia-related long non-coding RNAs serve as accurate indicators of poor prognosis and short overall survival, and are likely to act as potential targets for future cancer therapy.

Metabolism-related long non-coding RNAs (lncRNAs) as potential biomarkers for predicting risk of recurrence in breast cancer patients

Metabolism affects the development, progression, and prognosis of various cancers, including breast cancer (BC). Our aim was to develop a metabolism-related long non-coding RNA (lncRNA) signature to assess the prognosis of BC patients in order to optimize treatment. Metabolism-related genes between breast tumors and normal tissues were screened out, and Pearson correlation analysis was used to investigate metabolism-related lncRNAs. In total, five metabolism-related lncRNAs were enrolled to establish prognostic signatures. Kaplan-Meier plots and the receiver operating characteristic (ROC) curves demonstrated good performance in both training and validation groups. Further analysis demonstrated that the signature was an independent prognostic factor for BC. A nomogram incorporating risk score and tumor stage was then constructed to evaluate the 3 - and 5-year recurrence-free survival (RFS) in patients with BC. In conclusion, this study identified a metabolism-related lncRNA signature that can predict RFS of BC patients and established a prognostic nomogram that helps guide the individualized treatment of patients at different risks.

Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

Background

Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning.

Methods

We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA.

Results

We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (ABCC3), collagen type IV alpha 1 chain (COL4A1), podoplanin (PDPN), annexin A1 (ANXA1), COL4A2, insulin-like growth factor binding protein 2 (IGFBP2), serpin family A member 3 (SERPINA3), CXXC-type zinc finger protein 11 (CXXC11), junctophilin 3 (JPH3), secretogranin III (SCG3), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (SMOC1), Cluster of Differentiation 14 (CD14), COL1A1, S100 calcium-binding protein A4 (S100A4), and transforming growth factor beta 1 (TGF-β1). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling, interferon gamma (IFN-γ) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group.

Conclusion

We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.

Identification of Five Immune-Related lncRNAs Predicting Survival and Tumor Microenvironment Characteristics in Breast Cancer

A common cancer in females, breast cancer (BRCA) mortality has been recently reduced; however, the prognosis of BRCA patients remains poor. This study attempted to develop prognostic immune-related long noncoding RNAs (lncRNAs) for BRCA and identify the effects of these lncRNAs on the tumor microenvironment (TME). Gene expression data from The Cancer Genome Atlas (TCGA) database were collected in order to select differentially expressed lncRNAs. Immune-related lncRNAs were downloaded from the ImmLnc database, where 316 immune-related lncRNAs were identified, 12 of which were found to be significantly related to the prognosis of BRCA patients. Multivariate cox regression analysis was then applied to construct prognostic immune-related lncRNAs as the risk model, including C6orf99, LINC00987, SIAH2-AS1, LINC01010, and ELOVL2-AS1. High-risk and low-risk groups were distinguished according to the median of immune-related risk scores. Accordingly, the overall survival (OS) in the high-risk group was observed to be shorter than that in the low-risk group. qRT-PCR analysis demonstrated that lncRNA expression levels in BRCA cell lines were in basic agreement with predictions except for LINC00987. By validating numerous clinical samples, lncRNA C6orf99 was shown to be highly expressed in the advanced stage, while LINC01010 and SIAH2-AS1 decreased in the advanced T-stage and M-stage. Moreover, the expression of LINC0098 was found to be significantly decreased among the groups (>50 years old). Gene set enrichment analysis (GSEA) was applied to analyze the cancer hallmarks and immunological characteristics of the high-risk and low-risk groups. Importantly, the TIMER database demonstrated that this immune-related lncRNA risk model for breast cancer is related to the infiltration of immune cells. In conclusion, the results indicated that five immune-related lncRNAs could be used as a prognostic model and may even accelerate immunotherapy for BRCA patients.