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

A Novel Cellular Senescence-related lncRNA Signature for Predicting the Prognosis of Breast Cancer Patients

Background: Long non-coding RNA (lncRNA), a crucial regulator in breast cancer (BC) development, is intricately linked with cellular senescence. However, there is a lack of cellular senescence-related lncRNAs (CSRLs) signature to evaluate the prognosis of BC patients. Methods: Correlation analysis was conducted to identify lncRNAs associated with cellular senescence. Subsequently, a CSRL signature was crafted in the training cohort. The model's accuracy was evaluated through survival analysis and receiver operating characteristic curves. Furthermore, prognostic nomograms amalgamating cellular senescence and clinical characteristics were devised. Tumor microenvironment and checkpoint disparities were compared between low-risk and high-risk groups. The correlation between these signatures and treatment response in BC patients was also investigated. Finally, functional experiments were conducted for validation. Results: A signature comprising nine CSRLs was devised, which demonstrated adept prognostic capability in BC patients. Functional enrichment analysis revealed that tumor and immune-related pathways were predominantly enriched. Compared to the low-risk group, the high-risk group could benefit more from immunotherapy and certain chemotherapeutic agents. The expression of the 9 CSRLs was validated through in vitro experiments in different subtypes of BC cell lines and tissues. AC098484.1 was specifically verified for its association with senescence-associated secretory phenotypes. Conclusion: The CSRLs signature emerges as a promising prognostic biomarker for BC, with implications for immunological studies and treatment strategies. AC098484.1 has potential relevance in the treatment of BC cell senescence, and these findings improve the clinical treatment levels for BC patients.

Silencing LINC00987 ameliorates adriamycin resistance of acute myeloid leukemia via miR-4458/HMGA2 axis

BACKGROUND

Most patients with acute myeloid leukemia (AML) eventually develop drug resistance, leading to a poor prognosis. Dysregulated long gene non coding RNAs (lincRNAs) have been implicated in chemoresistance in AML. Unfortunately, the effects of lincRNAs which participate in regulating the Adriamycin (ADR) resistance in AML cells remain unclear. Thus, the purpose of this study is to determine LINC00987 function in ADR-resistant AML.

METHODS

In this study, ADR-resistant cells were constructed. LINC00987, miRNAs, and HMGA2 mRNA expression were measured by qRT-PCR. P-GP, BCRP, and HMGA2 protein were measured by Western blot. The proliferation was analyzed by MTS and calculated IC50. Soft agar colony formation assay and TUNEL staining were used to analyze cell colony formation and apoptosis. Xenograft tumor experiment was used to analyze the xenograft tumor growth of ADR-resistant AML.

RESULTS

We found that higher expression of LINC00987 was observed in AML patients and associated with poor overall survival in AML patients. LINC00987 expression was increased in ADR-resistant AML cells, including ADR/MOLM13 and ADR/HL-60 cells. LINC00987 downregulation reduces ADR resistance in ADR/MOLM13 and ADR/HL-60 cells in vitro and in vivo, while LINC00987 overexpression enhanced ADR resistance in MOLM13 and HL-60 cells. Additionally, LINC00987 functions as a competing endogenous RNA for miR-4458 to affect ADR resistance in ADR/MOLM13 and ADR/HL-60 cells. HMGA2 is a target of miR-4458. LINC00987 knockdown and miR-4458 overexpression reduced HMGA2 expression. HMGA2 overexpression enhanced ADR resistance, which reversed the function of LINC00987 silencing in suppressing ADR resistance of ADR/MOLM13 and ADR/HL-60 cells.

CONCLUSIONS

Downregulation of LINC00987 weakens ADR resistance by releasing miR-4458 to deplete HMGA2 in ADR/MOLM13 and ADR/HL-60. Therefore, LINC00987 may act as the therapeutic target for treating chemoresistant AML.

Long non-coding RNA and the tumor microenvironment: Prospects for clinical applications in breast cancer

Breast cancer has surpassed lung cancer as the number one cancer worldwide, and invasion and metastasis are still the main causes of death in breast cancer patients. The tumor microenvironment (TME) is an important site for the growth of tumor cells nourished by vascular networks, and various components of the TME interact strongly with cancer cells and are one of the important mechanisms of tumor progression and metastasis. In recent years, many studies have reported that long non-coding RNAs (LncRNAs) are involved in the formation of TME and influence the process of tumorigenesis and metastasis. This paper reviews the basic characteristics and functional roles of LncRNA in breast cancer TME and introduces the various mechanisms of LncRNA in breast cancer microenvironment that induce breast cancer development and metastasis in three directions: immune cells, non-immune cells, and extracellular matrix in TME, providing potential biomarkers or therapeutic targets for clinical practice.

The role of long non-coding RNAs in breast cancer microenvironment

The tumor microenvironment (TME), which includes tumor cells, fibroblasts, endothelial cells, immune cells, and blood vessels, can affect tumor growth and metastasis. Studies have shown that tumor cells, fibroblasts, and macrophages can promote the development of tumors, while T and B cells can inhibit tumor progression. The crosstalk among different cells within the TME needs further study. Long non-coding RNAs (lncRNAs) are involved in biological processes, including cell proliferation, migration, and differentiation. The abnormal expression of certain lncRNAs is correlated with the progression of breast cancer and has been proven as diagnostic markers in various cancers, including breast cancer. In breast cancer, recent studies have shown that tumor cell- and non-tumor cell-derived lncRNAs can affect various facets of tumor progression, including growth, proliferation, and migration of tumor cells. Interestingly, in addition to being regulated by lncRNAs derived from tumor and non-tumor cells, the TME can regulate the expression of lncRNAs in tumor cells, fibroblasts, and macrophages, influencing their phenotype and function. However, the detailed molecular mechanisms of these phenomena remain unclear in the breast cancer microenvironment. Currently, many studies have shown that TME-associated lncRNAs are potential diagnostic and therapeutic targets for breast cancer. Considering that TME and lncRNAs can regulate each other, we summarize the role of lncRNAs in the breast cancer microenvironment and the potential of lncRNAs as valuable diagnostic markers.

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.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia

Background

Cuproptosis is a type of programmed cell death that is involved in multiple physiological and pathological processes, including cancer. We constructed a prognostic cuproptosis-related long non-coding RNA (lncRNA) signature for acute myeloid leukemia (AML).

Methods

RNA-seq and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) database. The cuproptosis-related prognostic lncRNAs were identified by co-expression and univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was performed to construct a cuproptosis-related lncRNA signature, after which the AML patients were classified into two risk groups based on the risk model. Kaplan-Meier, ROC, univariate and multivariate Cox regression, nomogram, and calibration curves analyses were used to evaluate the prognostic value of the model. Then, expression levels of the lncRNAs in the signature were investigated in AML samples by quantitative polymerase chain reaction (qPCR). KEGG functional analysis, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. The sensitivities for potential therapeutic drugs for AML were also investigated.

Results

Five hundred and three lncRNAs related to 19 CRGs in AML samples from the TCGA database were obtained, and 21 differentially expressed lncRNAs were identified based on the 2-year overall survival (OS) outcomes of AML patients. A 4-cuproptosis-related lncRNA signature for survival was constructed by LASSO Cox regression. High-risk AML patients exhibited worse outcomes. Univariate and multivariate Cox regression analyses demonstrated the independent prognostic value of the model. ROC, nomogram, and calibration curves analyses revealed the predictive power of the signature. KEGG pathway and ssGSEA analyses showed that the high-risk group had higher immune activities. Lastly, AML patients from different risk groups showed differential responses to various agents.

Conclusion

A cuproptosis-related lncRNA signature was established to predict the prognosis and inform on potential therapeutic strategies for AML patients.

Machine Learning-Devised Immune-Related lncRNA Signature Panel Predicts the Prognosis and Immune Landscape in Breast Cancer Novel IRLP Signature in BRCA

Long noncoding RNAs (lncRNAs) actively participate in breast cancer (BRCA) tumorigenesis via epigenetic mechanisms. Our study identified immune-related lncRNA (irlncRNA) pairs and compiled them into a set of noncoding gene signatures able to stratify subtypes of BRCA associated with variable degrees of survival and immune cell infiltration. A 40 immune-related lncRNA pair (IRLP) signature including 43 irlncRNAs was built, with high sensitivity and specificity for the prediction of survival in different molecular subtypes of BRCA. Results demonstrated that the low-risk group showed a significantly longer survival rate, and this novel IRLP signature was highly associated with survival status, T stage, metastatic disease, and overall stage in BRCA. Immune infiltrating analyses found that the low-risk group has a lower expression level of macrophage M2 and a higher expression level of immunosuppressed biomarkers than the high-risk group. DEirlncRNAs were further proven to be significantly related to the MAPK signaling, Jak-STAT signaling, and ErbB signaling pathways in BRCA. In conclusion, the 40 IRLP signature showed a promising clinical prediction value in the prognosis of different molecular subtypes and immunotherapy response in BRCA, and the underlying mechanism for these IRLPs warrants further investigations.

LncRNAs in tumor metabolic reprogramming and immune microenvironment remodeling

Evidence accumulated over the past decade has verified that long non-coding RNAs (lncRNAs) exert important functions in multiple cell programs. As a novel class of cellular regulatory molecules, lncRNAs interact with different molecules, such as DNA, RNA or proteins, depending on their subcellular distribution, to modulate gene transcription and kinase cascades. It has been widely clarified that lncRNAs play important roles in modulating metabolic reprogramming and reshaping the immune landscape and serve as hinges bridging tumor metabolism and anti-tumor immunity. Given these facts, lncRNAs, as putative regulators of tumor initiation and progression, have attracted extensive attention in recent years. In this review, we summarized the current research progress on the role of lncRNAs in tumor metabolic reprogramming and tumor-immune microenvironment remodeling, and conclude with our laboratory's contributions in advancing the clinical applications of lncRNAs.

Identification of CD4+ Conventional T Cells-Related lncRNA Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Breast Cancer

Background

Breast cancer (BC) is one of the most common malignancies in women, and long non-coding RNAs (lncRNAs) are key regulators of its development. T cells can recognize and kill cancer cells, and CD4⁺ T conventional (Tconv) cells are the main orchestrators of cancer immune function. However, research on CD4⁺ Tconv-related lncRNAs (CD4TLAs) prognostic signature in patients with BC is still lacking.

Method

A TCGA database and a GEO database were used to collect the BC patients. Through LASSO Cox regression analysis CD4TLAs-related prognostic models were further constructed, and risk scores (RS) were generated and developed a nomogram based on CD4TLAs. The accuracy of this model was validated in randomized cohorts and different clinical subgroups. Gene set enrichment analysis (GSEA) was used to explore potential signature-based functions. The role of RS has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy.

Result

A prognostic model based on 16 CD4TLAs was identified. High-RS was significantly associated with a poorer prognosis. RS was shown to be an independent prognostic indicator in BC patients. The low-RS group had a significant expression of immune infiltrating cells and significantly enriched immune-related functional pathways. In addition, the results of immunotherapy prediction indicated that patients with low-RS were more sensitive to immunotherapy.

Conclusions

Our signature has potential predictive value for BC prognosis and immunotherapy response. The findings of this work have greatly increased our understanding of CD4TLA in BC.

ELOVL2-AS1 inhibits migration of triple negative breast cancer

In this study, we identified a key enhancer RNA (eRNA) region in breast cancer (BRCA) by applying an integrated analysis method. Reported eRNA region and genes affected by them were selected as presumed target pairs. Kaplan-Meier (KM) survival and correlation analyses were performed to screen valuable eRNA region. Based on the KM value and its correlation with the paired target genes, we carefully selected ELOVL2-AS1 as a potential key eRNA region in BRCA. Subsequently, we analyzed the expression of ELOVL2-AS1 and ELOVL2 in four BRCA subtypes and in different BRCA cell lines. The expression of ELOVL2-AS1 and ELOVL2 in triple negative breast cancer (TNBC) was significantly lower than those in Luminal A. After that, we analyzed the function of genes that are positively correlated with ELOVL2-AS1. We found that the co-expression gene mainly related to cilia and cilia characteristics of TNBC is significantly weaker than that of Luminal A. Considering the stronger invasion and metastasis of TNBC (compared with Luminal A) and the close relationship between decreased cilia and metastasis, we overexpressed ELOVL2-AS1 in TNBC and observed its effect on cell migration. The results show that it can inhibit the migration of TNBC. Finally, we analyzed the assay for transposase-accessible chromatin sequencing data, chromatin interaction analysis with paired-end tag sequencing data, and chromatin immunoprecipitation sequencing data and identified the chromatin interaction between ELOVL2-AS1 and ELOVL2, suggesting a direct regulatory interaction.

Identification of Driver Genes and Interaction Networks Related to Brain Metastasis in Breast Cancer Patients

Brain metastasis is a common complication of breast cancer (BC); however, the interaction networks and driver genes that lead to brain metastasis in BC patients are still unknown. In this study, we employed bioinformatics analyses to discover hub genes and long noncoding RNA- (lncRNA-) protein-coding gene (PCG) networks related to BC brain metastasis (BCBM). Firstly, we screened differentially expressed PCGs and lncRNAs in normal and BCBM samples using the GSE52604 dataset. Subsequently, differentially expressed genes (DEGs) and overall interaction networks were constructed, and topological degrees were analyzed to identify potential driver genes. After identifying the hub pathogenic module by weighted gene coexpression network analysis (WGCNA), the genes in the hub module were evaluated for functional enrichment. Finally, we constructed multiple interaction networks associated with BCBM and identified seven potential driver genes, out of which MYBPC1 was the only overlapping gene in the adopted analytical methods. It is worth mentioning that we validated the prognostic value of the identified hub genes in TCGA database and evaluated the prediction ability of MYBPC1 in the GSE38057 dataset. In addition, the CIBERSORT algorithm revealed changes in the immune microenvironment. In conclusion, the driver PCGs and lncRNAs in the interaction networks can be utilized as a promising therapeutic strategy for the treatment of brain metastasis in BC patients.

Identification of Diagnostic Markers Correlated With HIV+ Immune Non-response Based on Bioinformatics Analysis

Background: HIV-infected immunological non-responders (INRs) are characterized by their inability to reconstitute CD4⁺ T cell pools after antiretroviral therapy. The risk of non-AIDS-related diseases in INRs is increased, and the outcome and prognosis of INRs are inferior to that of immunological responders (IRs). However, few markers can be used to define INRs precisely. In this study, we aim to identify further potential diagnostic markers associated with INRs through bioinformatic analyses of public datasets.

Methods: This study retrieved the microarray data sets of GSE106792 and GSE77939 from the Gene Expression Omnibus (GEO) database. After merging two microarray data and adjusting the batch effect, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) resource and Kyoto Encyclopedia of Genes and Genomes (KEGG) resource were conducted to analyze the biological process and functional enrichment. We performed receiver operating characteristic (ROC) curves to filtrate potential diagnostic markers for INRs. Gene Set Enrichment Analysis (GSEA) was conducted to perform the pathway enrichment analysis of individual genes. Single sample GSEA (ssGSEA) was performed to assess scores of immune cells within INRs and IRs. The correlations between the diagnostic markers and differential immune cells were examined by conducting Spearman’s rank correlation analysis. Subsequently, miRNA-mRNA-TF interaction networks in accordance with the potential diagnostic markers were built with Cytoscape. We finally verified the mRNA expression of the diagnostic markers in clinical samples of INRs and IRs by performing RT-qPCR.

Results: We identified 52 DEGs in the samples of peripheral blood mononuclear cells (PBMC) between INRs and IRs. A few inflammatory and immune-related pathways, including chronic inflammatory response, T cell receptor signaling pathway, were enriched. FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1 were considered as potential diagnostic markers. ssGSEA results showed that the IRs had significantly higher enrichment scores of seven immune cells compared with IRs. The miRNA-mRNA-TF network was constructed with 97 miRNAs, 6 diagnostic markers, and 26 TFs, which implied a possible regulatory relationship.

Conclusion: The six potential crucial genes, FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1, may be associated with clinical diagnosis in INRs. Our study provided new insights into diagnostic and therapeutic targets.

Identification of mRNA vaccines and conserved ferroptosis related immune landscape for individual precision treatment in bladder cancer

BACKGROUND

The aim of this study was to identify the ferroptosis induced tumor microenvironment (FeME) landscape in bladder cancer (BCa) for mRNA vaccine development and selecting suitable patients for precision treatment.

METHODS

Gene expression profiles and clinical information of 1216 BCa patients were extracted from TCGA-BLCA, three GEO databases and IMvigor210 cohort. We comprehensively established the FeME landscape of 1216 BCa samples based on 290 ferroptosis related genes (FRGs), and systematically correlated these regulation patterns with TME cell-infiltrating characteristics. Besides, we identified the patients' ferroptosis risk index (FRI) to predict the prognosis of BCa for precise treatment.

RESULTS

Six over-expressed and mutated tumor antigens associated with poor prognosis and infiltration of antigen presenting cells were identified in BCa. Furthermore, we demonstrated the evaluation of FeME within individual tumors could predict stages of tumor inflammation, subtypes, genetic variation, and patient prognosis. Then, 5-lncRNA signature was mined to produce the FRI. Low FRI was also linked to increased mutation load, better prognosis and enhanced response to anti-PD-L1 immunotherapy. Besides, an immunotherapy cohort confirmed patients with lower FRI demonstrated significant therapeutic advantages and clinical benefits.

CONCLUSIONS

TFRC, SCD, G6PD, FADS2, SQLE, and SLC3A2 are potent antigens for developing anti-BCa mRNA vaccine. Establishment of FRI will contribute to enhancing our cognition of TME infiltration characterization and guiding more effective immunotherapy strategies and selecting appropriate patients for tumor vaccine therapy.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40537-022-00641-z.

Hypoxia-related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD-L1 squamous and adenocarcinoma NSCLC

Background

Recently, the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) have achieved remarkable survival benefits in non‐small cell lung cancer (NSCLC) treatment. However, epidermal growth factor receptor (EGFR) wild type and low expression of programmed death‐ligand 1 (PD‐L1) NSCLC remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a hypoxia‐related lncRNAs (hypolncRNAs) classifier that could successfully identify the high‐risk patients and reveal its underlying molecular immunology characteristics.

Methods

By identifying the bottom 25% PD‐L1 expression level as low expression of PD‐L1 and removing EGFR mutant samples, a total of 222 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) samples and 93 adjacent non‐tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). A 0 or 1 matrix was constructed by cyclically pairing hypoxia‐related long non‐coding RNAs (hypolncRNAs) and divided into the train set and test set. The univariate Cox regression analysis determined the prognostic hypolncRNAs pairs. Then, the prognostic classifier contained nine hypolncRNAs pairs which were generated by Lasso regression and multivariate Cox analysis. It successfully stratified EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC (double‐negative LUAD and LUSC) patients into the high‐ and low‐risk groups, whose accuracy was proved by the time‐dependent receiver operating characteristic (ROC) curve. Furthermore, diverse acknowledged immunology methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT‐ABS, CIBERSORT, and the single‐sample gene set enrichment analysis (ssGSEA) revealed its underlying antitumor immunosuppressive status in the high‐risk patients.

Conclusions

It is noteworthy that hypolncRNAs are associated with the survival of double‐negative LUAD and LUSC patients, for which the possible mechanism is inhibiting the antitumor immune process.