Four-lncRNA immune prognostic signature for triple-negative breast cancer Running title: Immune lncRNAs predict prognosis of TNBC

The Role of Long Non-Coding RNAs in Modulating the Immune Microenvironment of Triple-Negative Breast Cancer: Mechanistic Insights and Therapeutic Potential

Triple-negative breast cancer (TNBC) is a highly heterogeneous and aggressive subtype of breast cancer that faces therapeutic challenges due to a shortage of effective targeted therapies. The complex biology of TNBC renders its clinical management fraught with difficulties, especially regarding the immune microenvironment of the tumor. In recent years, long non-coding RNAs (lncRNAs) have been recognized as important gene regulators with key roles in tumor development and microenvironmental regulation. Previous studies have shown that lncRNAs play important roles in the immune microenvironment of TNBC, including the regulation of tumor immune escape and the function of tumor-infiltrating immune cells. However, despite the increasing research on lncRNAs, there are still many unanswered questions, such as their specific mechanism of action and how to effectively utilize them as therapeutic targets. Therefore, the aim of this study was to review the mechanisms of lncRNAs in the TNBC immune microenvironment, explore their regulatory roles in tumor immune escape and immune cell infiltration, and explore their prospects as potential therapeutic targets. By integrating the latest research results, this study aims to provide new ideas and directions for future TNBC treatment.

Unfurling the functional association between long intergenic noncoding RNAs (lincRNAs) and HPV16-related cervical cancer pathogenesis through weighted gene co-expression network analysis of differentially expressed lincRNAs and coding genes

Long intergenic noncoding RNAs (lincRNAs) do not overlap annotated coding genes and are located in intergenic regions, as opposed to antisense and sense-intronic lncRNAs, located in genic regions. LincRNAs influence gene expression profiles and are thereby key to disease pathogenesis. In this study, we assessed the association between lincRNAs and HPV16-positive cervical cancer (CaCx) pathogenesis using weighted gene co-expression network analysis (WGCNA) with coding genes, comparing differentially expressed lincRNA and coding genes (DElincGs and DEcGs, respectively) in HPV16-positive patients with CaCx (n = 44) with those in HPV-negative healthy individuals (n = 34). Our analysis revealed five DElincG modules, co-expressing and correlating with DEcGs. We validated a substantial number of such module-specific correlations in the HPV16-positive cancer TCGA-CESC dataset. Four such modules, displayed significant correlations with patient traits, such as HPV16 physical status, lymph node involvement and overall survival (OS), highlighting a collaborative effect of all genes within specific modules on traits. Using the DAVID bioinformatics knowledgebase, we identified the underlying biological processes associated with these modules as cancer development and progression-associated pathways. Next, we identified the top 10 DElincGs with the highest connectivity within each functional module. Focusing on the prognostic module hub genes, downregulated CTD-2619J13.13 expression was associated with poor patient OS. This lincRNA gene interacted with 25 coding genes of its module and was associated with such biological processes as keratinization loss and keratinocyte differentiation, reflecting severe disease phenotypes. This study has translational relevance in fighting various cancers with high mortality rates in underdeveloped countries.

Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer

Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key roles in its prognosis. In particular, immune-related lncRNAs (IRL) have attracted enormous interest in diagnosis and treatment, but less is known about their potential functions. We aimed to investigate dysfunctional IRL and construct a risk model for improving the outcomes of patients. Nineteen immune cell types were collected for identifying house-keeping lncRNAs (HKLncRNA). GSE39582 and TCGA-COAD were treated as the discovery and validation datasets, respectively. Four machine learning algorithms (LASSO, Random Forest, Boruta, and Xgboost) and a Gaussian mixture model were utilized to mine the optimal combination of lncRNAs. Univariate and multivariate Cox regression was utilized to construct the risk score model. We distinguished the functional difference in an immune perspective between low- and high-risk cohorts calculated by this scoring system. Finally, we provided a nomogram. By leveraging the microarray, sequencing, and clinical data for immune cells and colon cancer patients, we identified the 221 HKLncRNAs with a low cell type-specificity index. Eighty-seven lncRNAs were up-regulated in the immune compared to cancer cells. Twelve lncRNAs were beneficial in improving performance. A risk score model with three lncRNAs (CYB561D2, LINC00638, and DANCR) was proposed with robust ROC performance on an independent dataset. According to immune-related analysis, the risk score is strongly associated with the tumor immune microenvironment. Our results emphasized IRL has the potential to be a powerful and effective therapy for enhancing the prognostic of colon cancer.

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.

Genetic Heterogeneity, Tumor Microenvironment and Immunotherapy in Triple-Negative Breast Cancer

Heterogeneity of triple-negative breast cancer is well known at clinical, histopathological, and molecular levels. Genomic instability and greater mutation rates, which may result in the creation of neoantigens and enhanced immunogenicity, are additional characteristics of this breast cancer type. Clinical outcome is poor due to early age of onset, high metastatic potential, and increased likelihood of distant recurrence. Consequently, efforts to elucidate molecular mechanisms of breast cancer development, progression, and metastatic spread have been initiated to improve treatment options and improve outcomes for these patients. The extremely complex and heterogeneous tumor immune microenvironment is made up of several cell types and commonly possesses disorganized gene expression. Altered signaling pathways are mainly associated with mutated genes including p53, PIK3CA, and MAPK, and which are positively correlated with genes regulating immune response. Of note, particular immunity-associated genes could be used in prognostic indexes to assess the most effective management. Recent findings highlight the fact that long non-coding RNAs also play an important role in shaping tumor microenvironment formation, and can mediate tumor immune evasion. Identification of molecular signatures, through the use of multi-omics approaches, and effector pathways that drive early stages of the carcinogenic process are important steps in developing new strategies for targeted cancer treatment and prevention. Advances in immunotherapy by remodeling the host immune system to eradicate tumor cells have great promise to lead to novel therapeutic strategies. Current research is focused on combining immune checkpoint inhibition with chemotherapy, PARP inhibitors, cancer vaccines, or natural killer cell therapy. Targeted therapies may improve therapeutic response, eliminate therapeutic resistance, and improve overall patient survival. In the future, these evolving advancements should be implemented for personalized medicine and state-of-art management of cancer patients.

Non-coding RNAs, another side of immune regulation during triple-negative breast cancer

Triple-negative breast cancer (TNBC) is considered about 12-24 % of all breast cancer cases. Patients experience poor overall survival, high recurrence rate, and distant metastasis compared to other breast cancer subtypes. Numerous studies have highlighted the crucial roles of non-coding RNAs (ncRNAs) in carcinogenesis and proliferation, migration, and metastasis of tumor cells in TNBC. Recent research has demonstrated that long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play a role in the regulation of the immune system by affecting the tumor microenvironment, the epithelial-mesenchymal transition, the regulation of dendritic cells and myeloid-derived stem cells, and T and B cell activation and differentiation. Immune-related miRNAs and lncRNAs, which have been established as predictive markers for various cancers, are strongly linked to immune cell infiltration and could be a viable therapeutic target for TNBC. In the current review, we discuss the recent updates of ncRNAs, including miRNAs and lncRNAs in TNBC, including their biogenesis, target genes, and biological function of their targets, which are mostly involved in the immune response.

Identification and validation of a novel prognostic signature based on transcription factors in breast cancer by bioinformatics analysis

BACKGROUND

Breast cancer (BRCA) is the leading cause of cancer mortality among women, and it is associated with many tumor suppressors and oncogenes. There is increasing evidence that transcription factors (TFs) play vital roles in human malignancies, but TFs-based biomarkers for BRCA prognosis were still rare and necessary. This study sought to develop and validate a prognostic model based on TFs for BRCA patients.

METHODS

Differentially expressed TFs were screened from 1,109 BRCA and 113 non-tumor samples downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis was used to identify TFs associated with overall survival (OS) of BRCA, and multivariate Cox regression analysis was performed to establish the optimal risk model. The predictive value of the TF model was established using TCGA database and validated using a Gene Expression Omnibus (GEO) data set (GSE20685). A gene set enrichment analysis was conducted to identify the enriched signaling pathways in high-risk and low-risk BRCA patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the TF target genes were also conducted separately.

RESULTS

A total of 394 differentially expressed TFs were screened. A 9-TF prognostic model, comprising PAX7, POU3F2, ZIC2, WT1, ALX4, FOXJ1, SPIB, LEF1 and NFE2, was constructed and validated. Compared to those in the low-risk group, patients in the high-risk group had worse clinical outcomes (P<0.001). The areas under the curve of the prognostic model for 5-year OS were 0.722 in the training cohort and 0.651 in the testing cohort. Additionally, the risk score was an independent prediction indicator for BRCA patients both in the training cohort (HR =1.757, P<0.001) and testing cohort (HR =1.401, P=0.001). It was associated with various cancer signaling pathways. Ultimately, 9 overlapping target genes were predicted by 3 prediction nomograms. The GO and KEGG enrichment analyses of these target genes suggested that the TFs in the model may regulate the activation of some classical tumor signaling pathways to control the progression of BRCA through these target genes.

CONCLUSIONS

Our study developed and validated a novel prognostic TF model that can effectively predict 5-year OS for BRCA patients.