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

Landscape of lncRNAs expressed in Mexican patients with triple‑negative breast cancer

Long non‑coding RNAs (lncRNAs) are key regulators of gene expression, that can regulate a range of carcinogenic processes. Moreover, they exhibit stability in biological fluids, with some displaying tissue specificity. As their expression depends on specific conditions or is linked to the regulation of particular signaling pathways, lncRNAs are promising candidates for providing insights into the likely progression of the disease. This allows for the stratification of patients based on their risk of progression, making them potential prognostic biomarkers in various types of cancer. In addition, the tissue‑specific expression profile of lncRNAs renders them ideal candidates for detection, prognosis and monitoring of cancer progression. The present study aims to provide an overview of differentially expressed lncRNAs in Mexican patients with triple‑negative breast cancer (TNBC), a subtype of breast cancer. The aim was to identify potential prognostic biomarkers that can be applied to improve the clinical management of Mexican patients with TNBC. Human Transcriptome Array 2.0 microarrays were used to analyze the transcriptome of TNBC and luminal tumors, which are reported to have a good prognosis amongst aggressive tumor types. Subsequently, results from these microarrays were validated in a cohort from The Cancer Genome Atlas, an independent cohort of Mexican patients and in breast cancer cell lines (MCF7, ZR75, T47D, MDA‑MB‑231, MDA‑MB‑468 and BT20). A total of 746 differentially expressed transcripts were identified, including 102 lncRNAs in TNBC compared with luminal tumors. Among the lncRNAs with the most significant changes in expression levels, SOX9‑AS was highly expressed in TNBC, whereas the expression of Lnc‑peroxidasin‑3:1 (Lnc‑PXDN‑3:1), Lnc‑RNA Synapse Defective Rho GTPase Homolog (Lnc‑SYDE) and long intergenic non‑coding RNA (LINC)01087 were decreased. In addition, the low expression of lncRNA LINC01087, LINC02568, ACO22196, and lncRNA eosinophil granule ontogeny transcript (Lnc‑EGOT) was associated with poor overall survival (OS). Further analysis revealed that the high expression levels of Lnc‑PXDN‑3:1, Lnc RNA fibrous sheath interacting protein 1‑6:3 and (LINC)00182 were associated with reduced survival in patients with the luminal subtype of breast cancer. Similarly, low expression levels of lncRNAs such as GATA binding protein 3‑1 (Lnc‑GATA‑3‑1), LINC01087, and BX679671.1 in luminal subtypes of breast cancer, as well as LINC00504 and LncRNA rho guanine nucleotide exchange factor 38 intronic transcript 1 (Lnc‑ARHGEF38‑IT1) in basal subtypes have been linked to poorer survival. The interactions and functions of LINC01087 were then investigated, revealing the interaction of LINC01087 with RNAs and transcription factors, highlighting their potential involvement in the estrogen receptor pathway. The present study provided a detailed analysis of the expression of lncRNAs in TNBC, which highlights the role of lncRNAs as a biomarker in the survival outcomes of patients with breast cancer to improve the understanding of transcriptional regulation in TNBC.

Combined Effect of Conventional Chemotherapy with Epigenetic Modulators on Glioblastoma

BACKGROUND/OBJECTIVES

Glioblastoma is the most common malignant primary brain tumor, characterized by necrosis, uncontrolled proliferation, infiltration, angiogenesis, apoptosis resistance, and genomic instability. Epigenetic modifiers hold promise as adjuvant therapies for gliomas, with synergistic combinations being explored to enhance efficacy and reduce toxicity. This study aimed to evaluate the effects of single or combined treatments with various anticancer drugs (Carboplatin, Paclitaxel, Avastin), natural compounds (Quercetin), and epigenetic modulators (suberoylanilide hydroxamic acid and 5-Azacytidine) on the expression of some long noncoding RNAs and methylation drivers or some functional features in the U87-MG cell line.

METHODS

Treated and untreated U87-MG cells were used for the evaluation of drug-induced cytotoxicity, apoptotic events, and distribution in cell cycle phases, detection of cytokine release, and assessment of gene expression and global methylation.

RESULTS

Cytotoxicity assays led to the selection of drug concentrations to be used in further experiments. Expression analysis revealed distinct downregulation of nearly all investigated genes and long noncoding RNAs following treatments. All treatments resulted in a higher percentage of global methylation compared to untreated controls. All treatments effectively increased levels of apoptosis, while the epigenetic modulators exhibited a lower proliferation profile, with combined treatments showing elevated values of cell lysis.

CONCLUSIONS

The results indicate a link between Carboplatin and Avastin treatments and DNA methylation mechanisms involving EZH2, DNMT3A, and DNMT3B, with Avastin's direct impact on these enzymes warranting further study. This research underscores the promise of platinum-based therapies combined with epigenetic drugs to reactivate silenced tumor suppressor genes and optimize methylation profiles.

Long non-coding RNAs and their role in breast cancer pathogenesis and drug resistance: Navigating the non-coding landscape review

Despite the progress made in the development of targeted therapies, breast cancer (BC) continues to pose a significant threat to the health of women. Transcriptomics has emerged due to the advancements in high-throughput sequencing technology. This provides crucial information about the role of non-coding RNAs (ncRNAs) in human cells, particularly long ncRNAs (lncRNAs), in disease development and function. When the control of these ncRNAs is disrupted, various illnesses emerge, including cancer. Numerous studies have produced empirical data on the function of lncRNAs in tumorigenesis and disease development. However, the roles and mechanisms of numerous lncRNAs remain unidentified at the molecular level because their regulatory role and the functional implications of abnormalities in cancer biology have yet to be thoroughly defined. The review gives an itemized summary of the most current developments in the role of lncRNA in BC, focusing on three main pathways, PI3K, MAPK, NF-kB, and hypoxia, and their resistance mechanisms.

The implications for urological malignancies of non-coding RNAs in the the tumor microenvironment

Urological malignancies are a major global health issue because of their complexity and the wide range of ways they affect patients. There's a growing need for in-depth research into these cancers, especially at the molecular level. Recent studies have highlighted the importance of non-coding RNAs (ncRNAs) – these don't code for proteins but are crucial in controlling genes – and the tumor microenvironment (TME), which is no longer seen as just a background factor but as an active player in cancer progression. Understanding how ncRNAs and the TME interact is key for finding new ways to diagnose and predict outcomes in urological cancers, and for developing new treatments. This article reviews the basic features of ncRNAs and goes into detail about their various roles in the TME, focusing specifically on how different ncRNAs function and act in urological malignancies.

Decreased lncRNA HNF4A-AS1 facilitates resistance to sorafenib-induced ferroptosis of hepatocellular carcinoma by reprogramming lipid metabolism

Background: Resistance to sorafenib remains a major challenge in the systemic therapy of liver cancer. However, the involvement of lipid metabolism-related lncRNAs in this process remains unclear. Methods: Different expression levels of lipid metabolism-related lncRNAs in HCC were compared by analysis of Gene Expression Omnibus and The Cancer Genome Atlas databases. The influence of HNF4A-AS1 on sorafenib response was evaluated through analysis of public biobanks, cell cytotoxicity and colony formation assays. The effect of HNF4A-AS1 on sorafenib-induced ferroptosis was measured using lipid peroxidation, glutathione, malondialdehyde, and ROS levels. Furthermore, bioinformatic analyses and lipidomic profiling were conducted to study HNF4A-AS1 involvement in lipid metabolic reprogramming. Mechanistic experiments, including the luciferase reporter assay, RNA pulldown, RNA immunoprecipitation (RIP), methylated RNA immunoprecipitation (MeRIP), and RNA remaining assays, were employed to uncover the downstream targets and regulatory mechanisms of HNF4A-AS1 in sorafenib resistance in HCC. Xenograft and organoid experiments were carried out to assess the impact of HNF4A-AS1 on sorafenib response. Results: Bioinformatics analysis revealed that HNF4A-AS1, a lipid metabolism-related lncRNA, is specifically high-expressed in the normal liver and associated with sorafenib resistance in HCC. We further confirmed that HNF4A-AS1 was downregulated in HCC cells and organoids that resistant to sorafenib. Moreover, both in vitro and in vivo studies demonstrated that HNF4A-AS1 overexpression reversed sorafenib resistance in HCC cells, which was further enhanced by polyunsaturated fatty acids (PUFA) supplementation. Mechanistically, HNF4A-AS1 interacted with METTL3, leading to m6A modification of DECR1 mRNA, which subsequently decreased DECR1 expression via YTHDF3-dependent mRNA degradation. Consequently, decreased HNF4A-AS1 levels caused DECR1 overexpression, leading to decreased intracellular PUFA content and promoting resistance to sorafenib-induced ferroptosis in HCC. Conclusions: Our results indicated the pivotal role of lipid metabolism-related and liver-specific HNF4A-AS1 in inhibiting sorafenib resistance by promoting ferroptosis and suggesting that HNF4A-AS1 might be a potential target for HCC.

Long non-coding RNAs in ferroptosis, pyroptosis and necroptosis: from functions to clinical implications in cancer therapy

As global population ageing accelerates, cancer emerges as a predominant cause of mortality. Long non-coding RNAs (lncRNAs) play crucial roles in cancer cell growth and death, given their involvement in regulating downstream gene expression levels and numerous cellular processes. Cell death, especially non-apoptotic regulated cell death (RCD), such as ferroptosis, pyroptosis and necroptosis, significantly impacts cancer proliferation, invasion and metastasis. Understanding the interplay between lncRNAs and the diverse forms of cell death in cancer is imperative. Modulating lncRNA expression can regulate cancer onset and progression, offering promising therapeutic avenues. This review discusses the mechanisms by which lncRNAs modulate non-apoptotic RCDs in cancer, highlighting their potential as biomarkers for various cancer types. Elucidating the role of lncRNAs in cell death pathways provides valuable insights for personalised cancer interventions.

Targeting LRP6: A new strategy for cancer therapy

Targeting specific molecular drivers of tumor growth is a key approach in cancer therapy. Among these targets, the low-density lipoprotein receptor-related protein 6 (LRP6), a vital component of the Wnt signaling pathway, has emerged as an intriguing candidate. As a cell-surface receptor and vital co-receptor, LRP6 is frequently overexpressed in various cancer types, implicating its pivotal role in driving tumor progression. The pursuit of LRP6 as a target for cancer treatment has gained substantial traction, offering a promising avenue for therapeutic intervention. Here, this comprehensive review explores recent breakthroughs in our understanding of LRP6's functions and underlying molecular mechanisms, providing a profound discussion of its involvement in cancer pathogenesis and drug resistance. Importantly, we go beyond discussing LRP6's role in cancer by discussing diverse potential therapeutic approaches targeting this enigmatic protein. These approaches encompass a wide spectrum, including pharmacological agents, natural compounds, non-coding RNAs, epigenetic factors, proteins, and peptides that modulate LRP6 expression or disrupt its interactions. In addition, also discussed the challenges associated with developing LRP6 inhibitors and their advantages over Wnt inhibitors, as well as the drugs that have entered phase II clinical trials. By shedding light on these innovative strategies, we aim to underscore LRP6's significance as a valuable and multifaceted target for cancer treatment, igniting enthusiasm for further research and facilitating translation into clinical applications.

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server.

Exploring the translational landscape of the long noncoding RNA transcriptome in acute respiratory distress syndrome: it is a long way to the top

Acute respiratory distress syndrome (ARDS) poses a significant and widespread public health challenge. Extensive research conducted in recent decades has considerably improved our understanding of the disease pathophysiology. Nevertheless, ARDS continues to rank among the leading causes of mortality in intensive care units and its management remains a formidable task, primarily due to its remarkable heterogeneity. As a consequence, the syndrome is underdiagnosed, prognostication has important gaps and selection of the appropriate therapeutic approach is laborious. In recent years, the noncoding transcriptome has emerged as a new area of attention for researchers interested in biomarker development. Numerous studies have confirmed the potential of long noncoding RNAs (lncRNAs), transcripts with little or no coding information, as noninvasive tools for diagnosis, prognosis and prediction of the therapeutic response across a broad spectrum of ailments, including respiratory conditions. This article aims to provide a comprehensive overview of lncRNAs with specific emphasis on their role as biomarkers. We review current knowledge on the circulating lncRNAs as potential markers that can be used to enhance decision making in ARDS management. Additionally, we address the primary limitations and outline the steps that will be essential for integration of the use of lncRNAs in clinical laboratories. Our ultimate objective is to provide a framework for the implementation of lncRNAs in the management of ARDS.

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single-cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple-negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long non-coding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and -resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers an optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

HetFCM: functional co-module discovery by heterogeneous network co-clustering

Functional molecular module (i.e., gene-miRNA co-modules and gene-miRNA-lncRNA triple-layer modules) analysis can dissect complex regulations underlying etiology or phenotypes. However, current module detection methods lack an appropriate usage and effective model of multi-omics data and cross-layer regulations of heterogeneous molecules, causing the loss of critical genetic information and corrupting the detection performance. In this study, we propose a heterogeneous network co-clustering framework (HetFCM) to detect functional co-modules. HetFCM introduces an attributed heterogeneous network to jointly model interplays and multi-type attributes of different molecules, and applies multiple variational graph autoencoders on the network to generate cross-layer association matrices, then it performs adaptive weighted co-clustering on association matrices and attribute data to identify co-modules of heterogeneous molecules. Empirical study on Human and Maize datasets reveals that HetFCM can find out co-modules characterized with denser topology and more significant functions, which are associated with human breast cancer (subtypes) and maize phenotypes (i.e., lipid storage, drought tolerance and oil content). HetFCM is a useful tool to detect co-modules and can be applied to multi-layer functional modules, yielding novel insights for analyzing molecular mechanisms. We also developed a user-friendly module detection and analysis tool and shared it at http://www.sdu-idea.cn/FMDTool.

An estrogen-regulated long non-coding RNA NCALD promotes luminal breast cancer proliferation by activating GRHL2

PURPOSE

Luminal breast cancer (BC) is a prevalent subtype associated with an increased risk of late disease recurrence and mortality. Long noncoding RNAs (lncRNAs) likely play significant roles in regulating tissue-specific gene expression during tumorigenesis. However, the biological function and underlying mechanisms of specific dysregulated lncRNAs in luminal BC remain largely unknown, which has drawn our attention.

METHODS

The expression pattern of lncRNA NCALD in luminal BC was predicted and validated in collected tissue samples. Following cell transfection with knockdown of lncRNA NCALD and ESR1 and overexpression of GRHL2 and ESR1, we investigated the interactions among lncRNA NCALD, ESR1, and GRHL2. Additionally, their regulatory functions in luminal BC cell biological processes were studied. Subsequently, a xenograft tumor model was prepared for validation.

RESULTS

Our study identified a specific overexpression of the lncRNA NCALD in luminal BC, which correlated with an unfavorable prognosis. Suppression of lncRNA NCALD or ESR1 led to inhibition of GRHL2 expression, while concurrent overexpression of ESR1 and lncRNA NCALD potentially elevated GRHL2 expression. Mechanistically, ERα may drive the expression of lncRNA NCALD. Furthermore, the 1-151 nt fragment of lncRNA NCALD was found to recruit ERα and interact with its oest-Recep domain located in the promoter region of GRHL2, ultimately inducing GRHL2 transcription.

CONCLUSIONS

These findings reveal the involvement of lncRNA NCALD and its specific expression pattern in luminal BC. Targeting lncRNA NCALD could be a potential therapeutic strategy for delaying the progression of BC.

Decoding LINC00052 role in breast cancer by bioinformatic and experimental analyses

LncRNA is a group of transcripts with a length exceeding 200 nucleotides that contribute to tumour development. Our research group found that LINC00052 expression was repressed during the formation of breast cancer (BC) multicellular spheroids. Intriguingly, LINC00052 precise role in BC remains uncertain. We explored LINC00052 expression in BC patients` RNA samples (TCGA) in silico, as well as in an in-house patient cohort, and inferred its cellular and molecular mechanisms. In vitro studies evaluated LINC00052 relevance in BC cells viability, cell cycle and DNA damage. Results. Bioinformatic RNAseq analysis of BC patients showed that LINC00052 is overexpressed in samples from all BC molecular subtypes. A similar LINC00052 expression pattern was observed in an in-house patient cohort. In addition, higher LINC00052 levels are related to better BC patient´s overall survival. Remarkably, MCF-7 and ZR-75-1 cells treated with estradiol showed increased LINC00052 expression compared to control, while these changes were not observed in MDA-MB-231 cells. In parallel, bioinformatic analyses indicated that LINC00052 influences DNA damage and cell cycle. MCF-7 cells with low LINC00052 levels exhibited increased cellular protection against DNA damage and diminished growth capacity. Furthermore, in cisplatin-resistant MCF-7 cells, LINC00052 expression was downregulated. Conclusion. This work shows that LINC00052 expression is associated with better BC patient survival. Remarkably, LINC00052 expression can be regulated by Estradiol. Additionally, assays suggest that LINC00052 could modulate MCF-7 cells growth and DNA damage repair. Overall, this study highlights the need for further research to unravel LINC00052 molecular mechanisms and potential clinical applications in BC.

Long non-coding RNA AC099850.4 correlates with advanced disease state and predicts worse prognosis in triple-negative breast cancer

Our understanding of the function of long non-coding RNAs (lncRNAs) in health and disease states has evolved over the past decades due to the many advances in genome research. In the current study, we characterized the lncRNA transcriptome enriched in triple-negative breast cancer (TNBC, n = 42) and estrogen receptor (ER+, n = 42) breast cancer compared to normal breast tissue (n = 56). Given the aggressive nature of TNBC, our data revealed selective enrichment of 57 lncRNAs in TNBC. Among those, AC099850.4 lncRNA was chosen for further investigation where it exhibited elevated expression, which was further confirmed in a second TNBC cohort (n = 360) where its expression correlated with a worse prognosis. Network analysis of AC099850.4high TNBC highlighted enrichment in functional categories indicative of cell cycle activation and mitosis. Ingenuity pathway analysis on the differentially expressed genes in AC099850.4high TNBC revealed the activation of the canonical kinetochore metaphase signaling pathway, pyridoxal 5'-phosphate salvage pathway, and salvage pathways of pyrimidine ribonucleotides. Additionally, upstream regulator analysis predicted the activation of several upstream regulator networks including CKAP2L, FOXM1, RABL6, PCLAF, and MITF, while upstream regulator networks of TP53, NUPR1, TRPS1, and CDKN1A were suppressed. Interestingly, elevated expression of AC099850.4 correlated with worse short-term relapse-free survival (log-rank p = 0.01). Taken together, our data are the first to reveal AC099850.4 as an unfavorable prognostic marker in TNBC, associated with more aggressive clinicopathological features, and suggest its potential utilization as a prognostic biomarker and therapeutic target in TNBC.

Involvement of lncRNAs in cancer cells migration, invasion and metastasis: cytoskeleton and ECM crosstalk

Cancer is the main cause of death worldwide and metastasis is a major cause of poor prognosis and cancer-associated mortality. Metastatic conversion of cancer cells is a multiplex process, including EMT through cytoskeleton remodeling and interaction with TME. Tens of thousands of putative lncRNAs have been identified, but the biological functions of most are still to be identified. However, lncRNAs have already emerged as key regulators of gene expression at transcriptional and post-transcriptional level to control gene expression in a spatio-temporal fashion. LncRNA-dependent mechanisms can control cell fates during development and their perturbed expression is associated with the onset and progression of many diseases including cancer. LncRNAs have been involved in each step of cancer cells metastasis through different modes of action. The investigation of lncRNAs different roles in cancer metastasis could possibly lead to the identification of new biomarkers and innovative cancer therapeutic options.

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.

Lnc-ing epigenetic mechanisms with autophagy and cancer drug resistance

Long noncoding RNAs (lncRNAs) comprise a diverse class of RNA molecules that regulate various physiological processes and have been reported to be involved in several human pathologies ranging from neurodegenerative disease to cancer. Therapeutic resistance is a major hurdle for cancer treatment. Over the past decade, several studies has emerged on the role of lncRNAs in cancer drug resistance and many trials have been conducted employing them. LncRNAs also regulate different cell death pathways thereby maintaining a fine balance of cell survival and death. Autophagy is a complex cell-killing mechanism that has both cytoprotective and cytotoxic roles. Similarly, autophagy can lead to the induction of both chemosensitization and chemoresistance in cancer cells upon therapeutic intervention. Recently the role of lncRNAs in the regulation of autophagy has also surfaced. Thus, lncRNAs can be used in cancer therapeutics to alleviate the challenges of chemoresistance by targeting the autophagosomal axis. In this chapter, we discuss about the role of lncRNAs in autophagy-mediated cancer drug resistance and its implication in targeted cancer therapy.

Metformin Treatment Modulates Long Non-Coding RNA Isoforms Expression in Human Cells

Long noncoding RNAs (lncRNAs) undergo splicing and have multiple transcribed isoforms. Nevertheless, for lncRNAs, as well as for mRNA, measurements of expression are routinely performed only at the gene level. Metformin is the first-line oral therapy for type 2 diabetes mellitus and other metabolic diseases. However, its mechanism of action remains not thoroughly explained. Transcriptomic analyses using metformin in different cell types reveal that only protein-coding genes are considered. We aimed to characterize lncRNA isoforms that were differentially affected by metformin treatment on multiple human cell types (three cancer, two non-cancer) and to provide insights into the lncRNA regulation by this drug. We selected six series to perform a differential expression (DE) isoform analysis. We also inferred the biological roles for lncRNA DE isoforms using in silico tools. We found the same isoform of an lncRNA (AC016831.6-205) highly expressed in all six metformin series, which has a second exon putatively coding for a peptide with relevance to the drug action. Moreover, the other two lncRNA isoforms (ZBED5-AS1-207 and AC125807.2-201) may also behave as cis-regulatory elements to the expression of transcripts in their vicinity. Our results strongly reinforce the importance of considering DE isoforms of lncRNA for understanding metformin mechanisms at the molecular level.