A panel of 7 prognosis-related long non-coding RNAs to improve platinum-based chemoresistance prediction in ovarian cancer

Long non-coding RNA GRASLND links melanoma differentiation and interferon-gamma response

Melanoma is a highly malignant tumor, that stands as the most lethal form of skin cancer and is characterized by notable phenotypic plasticity and intratumoral heterogeneity. Melanoma plasticity is involved in tumor growth, metastasis and therapy resistance. Long non-coding RNAs (lncRNAs) could influence plasticity due to their regulatory function. However, their role and mode of action are poorly studied. Here, we show a relevance of lncRNA GRASLND in melanoma differentiation and IFNγ signaling. GRASLND knockdown revealed switching of differentiated, melanocytic melanoma cells towards a dedifferentiated, slow-proliferating and highly-invasive cell state. Interestingly, GRASLND is overexpressed in differentiated melanomas and associated with poor prognosis. Accordingly, we found GRASLND expressed in immunological "cold" tumors and it negatively correlates with gene signatures of immune response activation. In line, silencing of GRASLND under IFNγ enhanced the expression of IFNγ-stimulated genes, including HLA-I antigen presentation, demonstrating suppressive activity of GRASLND on IFNγ signaling. Our findings demonstrate that in differentiated melanomas elevated expression of GRASLND interferes with anti-tumor effects of IFNγ, suggesting a role of GRASLND in tumor immune evasion.

The Role of Long Non-Coding RNF144A-AS1 in Cancer Progression

RNAs transcribing more than 200 nucleotides without encoding proteins are termed long non-coding RNAs (LncRNAs). LncRNAs can be used as decoy molecules, signal molecules, scaffolds, and guide molecules. Long non-coding RNAs can interact with DNA, chromatin-modifying complexes, and transcriptional regulatory proteins, regulating gene expression in the cell nucleus. It is distributed in cytoplasm; they also participate in mRNA degradation and translational regulation via miRNAs, other transcription products, and proteins. They play a significant role in the development of various diseases, including tumors. Cancer seriously threatens human life and health. Regretfully, a great deal of newly diagnosed cancer patients found to have metastasized. RNF144A-AS1, also referred to as GRASLND, was initially recognized for its regulation of chondrogenic differentiation in MSCs. Focusing on RNF144A-AS1, this review summarizes and discusses the latest progress of RNF144A-AS1 in bladder cancer, glioblastoma, papillary renal cell carcinoma, gastric cancer, osteosarcoma, head and neck squamous cell carcinoma, and ovarian cancer. RNF144A-AS1 has good potential in tumor treatment and diagnosis.

Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, and chemosensitivity in colorectal cancer patients

Background

Coagulation is critically involved in the tumor microenvironment, cancer progression, and prognosis assessment. Nevertheless, the roles of coagulation-related long noncoding RNAs (CRLs) in colorectal cancer (CRC) remain unclear. In this study, an integrated computational framework was constructed to develop a novel coagulation-related lncRNA signature (CRLncSig) to stratify the prognosis of CRC patients, predict response to immunotherapy and chemotherapy in CRC, and explore the potential molecular mechanism.

Methods

CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. We performed unsupervised consensus clustering of CRLs to classify patients into distinct molecular subtypes. We then used stepwise regression to establish the CRLncSig risk model, which stratified patients into high- and low-risk groups. Subsequently, diversified bioinformatics algorithms were used to explore prognosis, biological pathway alteration, immune microenvironment, immunotherapy response, and drug sensitivity across patient subgroups. In addition, weighted gene coexpression network analysis was used to construct an lncRNA-miRNA-mRNA competitive endogenous network. Expression levels of CRLncSig, immune checkpoints, and immunosuppressors were determined using RT-qPCR.

Results

We identified two coagulation subclusters and constructed a risk score model using CRLncSig in CRC, where the patients in cluster 2 and the low-risk group had a better prognosis. The cluster and CRLncSig were confirmed as the independent risk factors, and a CRLncSig-based nomogram exhibited a robust prognostic performance. Notably, the cluster and CRLncSig were identified as the indicators of immune cell infiltration, immunoreactivity phenotype, and immunotherapy efficiency. In addition, we identified a new endogenous network of competing CRLs with microRNA/mRNA, which will provide a foundation for future mechanistic studies of CRLs in the malignant progression of CRC. Moreover, CRLncSig strongly correlated with drug susceptibility.

Conclusion

We developed a reliable CRLncSig to predict the prognosis, immune landscape, immunotherapy response, and drug sensitivity in patients with CRC, which might facilitate optimizing risk stratification, guiding the applications of immunotherapy, and individualized treatments for CRC.

A Platinum Resistance-Related lncRNA Signature for Risk Classification and Prognosis Prediction in Patients with Serous Ovarian Cancer

Accurate risk stratification for patients with serous ovarian cancer (SOC) is pivotal for treatment decisions. In this study, we identified a lncRNA-based signature for predicting platinum resistance and prognosis stratification for SOC patients. We analyzed the RNA-sequencing data and the relevant clinical information of 295 SOC samples obtained from The Cancer Genome Atlas (TCGA) database and 180 normal ovarian tissues from the Genotype-Tissue Expression (GTEx) database. A total of 284 differentially expressed lncRNAs were screened out between platinum-sensitive and platinum-resistant groups by univariate Cox regression analysis. Then, a signature consisting of eight prognostic lncRNAs was used to construct a lncRNA score model by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The ROC analysis showed that this signature had a good predictive performance for chemotherapy response in the training set (AUC = 0.8524) and the testing and whole sets with 0.8142 and 0.8393 of AUC, respectively. Dichotomized by the risk score of lncRNAs (lncScore), the high-risk patients showed significantly shorter progression-free survival (PFS) and overall survival (OS). Based on the final Cox model, a nomogram comprising the 8-lncRNA signature and 3 clinicopathological risk factors was then established for clinical application to predict the 1, 2, and 3-year PFS of SOC patients. The gene set enrichment analysis (GSEA) revealed that genes in the high-risk group were active in ATP synthesis, coupled electron transport, and mitochondrial respiratory chain complex assembly. Overall, our findings demonstrated the potential clinical significance of the 8-lncRNA-based classifier as a novel biomarker for outcome prediction and therapy decisions in SOC patients with platinum treatment.

Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture

The identification of lncRNA-protein interactions (LPIs) is important to understand the biological functions and molecular mechanisms of lncRNAs. However, most computational models are evaluated on a unique dataset, thereby resulting in prediction bias. Furthermore, previous models have not uncovered potential proteins (or lncRNAs) interacting with a new lncRNA (or protein). Finally, the performance of these models can be improved. In this study, we develop a Deep Learning framework with Dual-net Neural architecture to find potential LPIs (LPI-DLDN). First, five LPI datasets are collected. Second, the features of lncRNAs and proteins are extracted by Pyfeat and BioTriangle, respectively. Third, these features are concatenated as a vector after dimension reduction. Finally, a deep learning model with dual-net neural architecture is designed to classify lncRNA-protein pairs. LPI-DLDN is compared with six state-of-the-art LPI prediction methods (LPI-XGBoost, LPI-HeteSim, LPI-NRLMF, PLIPCOM, LPI-CNNCP, and Capsule-LPI) under four cross validations. The results demonstrate the powerful LPI classification performance of LPI-DLDN. Case study analyses show that there may be interactions between RP11-439E19.10 and Q15717, and between RP11-196G18.22 and Q9NUL5. The novelty of LPI-DLDN remains, integrating various biological features, designing a novel deep learning-based LPI identification framework, and selecting the optimal LPI feature subset based on feature importance ranking.

The Long Non-Coding RNA SNHG12 as a Mediator of Carboplatin Resistance in Ovarian Cancer via Epigenetic Mechanisms

Simple Summary

Epithelial ovarian cancer is a lethal malignancy in which recurrence and therapy resistance are the major causes of death. We investigated the transcriptome and DNA methylation profile of ovarian cancer cell lines sensitive and resistant to carboplatin, aiming to identify genes associated with therapy resistance. We focused on long non-coding RNAs (lncRNAs), known as epigenetic regulators of several cellular and biological processes. We found 11 lncRNAs associated with carboplatin resistance, including SNHG12 (small nucleolar RNA host gene 12), also confirmed in an external dataset (The Cancer Genome Atlas). SNHG12 gene silencing increased the sensitivity to carboplatin, giving evidence that this lncRNA contributes to resistance to carboplatin in ovarian cancer cell lines. We also demonstrated that SNHG12 could control the expression of nearby genes probably by altering epigenetic markers and modifying the transcript levels.

Abstract

Genetic and epigenetic changes contribute to intratumor heterogeneity and chemotherapy resistance in several tumor types. LncRNAs have been implicated, directly or indirectly, in the epigenetic regulation of gene expression. We investigated lncRNAs that potentially mediate carboplatin-resistance of cell subpopulations, influencing the progression of ovarian cancer (OC). Four carboplatin-sensitive OC cell lines (IGROV1, OVCAR3, OVCAR4, and OVCAR5), their derivative resistant cells, and two inherently carboplatin-resistant cell lines (OVCAR8 and Ovc316) were subjected to RNA sequencing and global DNA methylation analysis. Integrative and cross-validation analyses were performed using external (The Cancer Genome Atlas, TCGA dataset, n = 111 OC samples) and internal datasets (n = 39 OC samples) to identify lncRNA candidates. A total of 4255 differentially expressed genes (DEGs) and 14529 differentially methylated CpG positions (DMPs) were identified comparing sensitive and resistant OC cell lines. The comparison of DEGs between OC cell lines and TCGA-OC dataset revealed 570 genes, including 50 lncRNAs, associated with carboplatin resistance. Eleven lncRNAs showed DMPs, including the SNHG12. Knockdown of SNHG12 in Ovc316 and OVCAR8 cells increased their sensitivity to carboplatin. The results suggest that the lncRNA SNHG12 contributes to carboplatin resistance in OC and is a potential therapeutic target. We demonstrated that SNHG12 is functionally related to epigenetic mechanisms.

(In)Distinctive Role of Long Non-Coding RNAs in Common and Rare Ovarian Cancers

Rare ovarian cancers (ROCs) are OCs with an annual incidence of fewer than 6 cases per 100,000 women. They affect women of all ages, but due to their low incidence and the potential clinical inexperience in management, there can be a delay in diagnosis, leading to a poor prognosis. The underlying causes for these tumors are varied, but generally, the tumors arise due to alterations in gene/protein expression in cellular processes that regulate normal proliferation and its checkpoints. Dysregulation of the cellular processes that lead to cancer includes gene mutations, epimutations, non-coding RNA (ncRNA) regulation, posttranscriptional and posttranslational modifications. Long non-coding RNA (lncRNA) are defined as transcribed RNA molecules, more than 200 nucleotides in length which are not translated into proteins. They regulate gene expression through several mechanisms and therefore add another level of complexity to the regulatory mechanisms affecting tumor development. Since few studies have been performed on ROCs, in this review we summarize the mechanisms of action of lncRNA in OC, with an emphasis on ROCs.

Leveraging Genomics, Transcriptomics, and Epigenomics to Understand the Biology and Chemoresistance of Ovarian Cancer

Ovarian cancer is a major cause of fatality due to a gynecological malignancy. This lethality is largely due to the unspecific clinical manifestations of ovarian cancer, which lead to late detection and to high resistance to conventional therapies based on platinum. In recent years, we have advanced our understanding of the mechanisms provoking tumor relapse, and the advent of so-called omics technologies has provided exceptional tools to evaluate molecular mechanisms leading to therapy resistance in ovarian cancer. Here, we review the contribution of genomics, transcriptomics, and epigenomics techniques to our knowledge about the biology and molecular features of ovarian cancers, with a focus on therapy resistance. The use of these technologies to identify molecular markers and mechanisms leading to chemoresistance in these tumors is discussed, as well as potential further applications.

STAT3-induced ZBED3-AS1 promotes the malignant phenotypes of melanoma cells by activating PI3K/AKT signaling pathway

Melanoma is considered as the most frequent primary malignancy occurring in skin. Accumulating studies have suggested that long non-coding RNAs (lncRNAs) play critical parts in multiple cancers. In this study, we explored the molecular mechanism of ZBED3 antisense RNA 1 (ZBED3-AS1) in melanoma. We observed that ZBED3-AS1 expression was remarkably up-regulated in melanoma tissues, and high ZBED3-AS1 level was linked to unsatisfactory survival of melanoma patients. Then, we discovered that ZBED3-AS1 was overexpressed in melanoma cells compared with human epidermal melanocytes. In addition, loss-of-function assays verified that ZBED3-AS1 knockdown restrained cell proliferation, migration, epithelial-mesenchymal transition (EMT), and stemness in melanoma. In addition, signal transducer and activator of transcription 3 (STAT3), which also showed tumour-facilitating functions in melanoma, was confirmed as a transcriptional activator of ZBED3-AS1. Moreover, ZBED3-AS1 enhanced the expression of AT-rich interaction domain 4B (ARID4B) through sequestering miR-381-3p. Importantly, we further confirmed that ZBED3-AS1 promoted the malignant progression of melanoma by regulating miR-381-3p/ARID4B axis to activate the phosphatidylinositol 3-kinase/AKT serine/threonine kinase (PI3K/AKT) signalling pathway. In a word, our research might provide a novel therapeutic target for melanoma.

LncRNA RNF144A-AS1 Promotes Bladder Cancer Progression via RNF144A-AS1/miR-455-5p/SOX11 Axis

Background

Bladder cancer (BC) is the most commonly occurring malignant tumor of the urinary system worldwide. Long non-coding RNAs (lncRNAs), including lncRNA RNF144A-AS1 (RNF144A-AS1), perform an oncogenic role in BC progression. However, how RNF144A-AS1 is regulated in BC has not been fully investigated, and its role in BC is mostly obscure. In this study, we explore its role in BC progression.

Materials and Methods

The expression level of RNF144A-AS1 in BC tissues was explored via bioinformatics analysis and quantitative real-time PCR (qRT-PCR). We used RNF144A-AS1 siRNA (si-RNF144A-AS1) to inhibit the RNF144A-AS1 level in BC cell lines (J82 and 5637 cells). A series of experimental studies in vitro (CCK-8 assay, colony formation assay and Transwell assay) was performed to explore the role of si-RNF144A-AS1 on the proliferation, migration and invasion of J82 and 5637 cells. A BC xenograft model was established, and the effect of si-RNF144A-AS1 on xenograft growth was explored in vivo. The interactions among RNF144A-AS1, miR-455-5p and SOX11 were predicted by bioinformatics miRanda and Targetscan database, and verified by the luciferase reporter assay and RNA pull-down assay. Finally, miR-455-5p inhibitor and si-RNF144A-AS1 were cotransfected into J82 and 5637 cells.

Results

RNF144A-AS1 is overexpressed in BC tumors and cells, and its overexpression is correlated with poor prognosis. Knockdown of RNF144A-AS1 markedly suppressed the proliferation, migration and invasion of J82 and 5637 cells and significantly inhibited xenograft growth in nude mice, compared to si-NC. We found that RNF144A-AS1 serves as a sponge for miR-455-5p. Furthermore, a binding site of miR-455-5p was found in 3ʹ UTR of SOX11 gene, and overexpression of miR-455-5p suppressed SOX11 levels. RNF144A-AS1 knockdown markedly decreased SOX11 expression levels, while miR-455-5p inhibitor restored this repressive effect. Restoration of SOX11 could reverse this repressive effect of RNF144A-AS1 on cell proliferation, migration and invasion abilities.

Conclusion

Overall, our findings underline the critical role of RNF144A-AS1 in BC development, and our study reveals for the first time that RNF144A-AS1 promotes BC progression via the RNF144A-AS1/miR-455-5p/SOX11 axis.

The Non-Coding RNA GAS5 and Its Role in Tumor Therapy-Induced Resistance

The growth arrest-specific transcript 5 (GAS5) is a >200-nt lncRNA molecule that regulates several cellular functions, including proliferation, apoptosis, invasion and metastasis, across different types of human cancers. Here, we reviewed the current literature on the expression of GAS5 in leukemia, cervical, breast, ovarian, prostate, urinary bladder, lung, gastric, colorectal, liver, osteosarcoma and brain cancers, as well as its interaction with various miRNAs and its effect on therapy-related resistance in these malignancies. The general consensus is that GAS5 acts as a tumor suppressor across different tumor types and that its up-regulation results in tumor sensitization to chemotherapy or radiotherapy. GAS5 seems to play a previously unappreciated, but significant role in tumor therapy-induced resistance.

The Interplay between Long Noncoding RNAs and Proteins of the Epigenetic Machinery in Ovarian Cancer

Comprehensive large-scale sequencing and bioinformatics analyses have uncovered a myriad of cancer-associated long noncoding RNAs (lncRNAs). Aberrant expression of lncRNAs is associated with epigenetic reprogramming during tumor development and progression, mainly due to their ability to interact with DNA, RNA, or proteins to regulate gene expression. LncRNAs participate in the control of gene expression patterns during development and cell differentiation and can be cell and cancer type specific. In this review, we described the potential of lncRNAs for clinical applications in ovarian cancer (OC). OC is a complex and heterogeneous disease characterized by relapse, chemoresistance, and high mortality rates. Despite advances in diagnosis and treatment, no significant improvements in long-term survival were observed in OC patients. A set of lncRNAs was associated with survival and response to therapy in this malignancy. We manually curated databases and used bioinformatics tools to identify lncRNAs implicated in the epigenetic regulation, along with examples of direct interactions between the lncRNAs and proteins of the epigenetic machinery in OC. The resources and mechanisms presented herein can improve the understanding of OC biology and provide the basis for further investigations regarding the selection of novel biomarkers and therapeutic targets.

Identification of Novel lncRNA Markers in Glioblastoma Multiforme and Their Clinical Significance: A Study Based on Multiple Sequencing Data

Background

Long non-coding RNAs (lncRNAs) have been verified to have a vital role in the progression of glioblastoma multiforme (GBM). Our research was about to identify the potential lncRNAs which was closely associated with the pathogenesis and prognosis of glioblastoma multiforme.

Methods

All RNA sequence profiling data from patients with GBM were obtained from The Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA). Differently expressed genes identified from GBM and control samples were used to construct competing endogenous RNA (ceRNA) network and perform corresponding functional enrichment analysis. Univariate Cox regression followed by lasso regression and multivariate Cox was used to validate independent lncRNA factors and construct a risk prediction model. Quantitative polymerase chain reaction (qPCR) was performed to verify the expression levels of potential lncRNA biomarkers in human GBM clinical specimens. A gene set enrichment analysis (GSEA) was subsequently conducted to explore potential signaling pathways in which critical lncRNAs may be involved. Moreover, nomogram plot was applied based on our prediction model and significant clinical covariates to visualize the prognosis of GBM patients.

Results

A total of 2023 differentially expressed genes (DEGs) including 56 lncRNAs, 1587 message RNAs (mRNAs) and 380 other RNAs were included. Based on predictive databases, 16lncRNAs, 32 microRNAs (miRNAs) and 99 mRNAs were used to construct a ceRNA network. Moreover, we performed a novel risk prediction model with 5 potential prognostic lncRNAs, in which 4 of them were newly identified in GBM, to predict the prognosis of GBM patients. Finally, a nomogram plot was constructed to illustrate the potential relationship between the prognosis of GBM and our risk prediction model and significant clinical covariates.

Conclusion

In this study, we identified 4 novel potential lncRNA biomarkers and constructed a prediction model of GBM prognosis. A simple-to-use nomogram was provided for further clinical application.

Long Non-coding RNAs Involved in Resistance to Chemotherapy in Ovarian Cancer

Ovarian cancer (OC) accounts for more than 150,000 deaths worldwide every year. Patients are often diagnosed at an advanced stage with metastatic dissemination. Although platinum- and taxane-based chemotherapies are effective treatment options, they are rarely curative and eventually, the disease will progress due to acquired resistance. Emerging evidence suggests a crucial role of long non-coding RNAs (lncRNAs) in the response to therapy in OC. Transcriptome profiling studies using high throughput approaches have identified differential expression patterns of lncRNAs associated with disease recurrence. Furthermore, several aberrantly expressed lncRNAs in resistant OC cells have been related to increased cell division, improved DNA repair, up-regulation of drug transporters or reduced susceptibility to apoptotic stimuli, supporting their involvement in acquired resistance. In this review, we will discuss the key aspects of lncRNAs associated with the development of resistance to platinum- and taxane-based chemotherapy in OC. The molecular landscape of OC will be introduced, to provide a background for understanding the role of lncRNAs in the acquisition of malignant properties. We will focus on the interplay between lncRNAs and molecular pathways affecting drug response to evaluate their impact on treatment resistance. Additionally, we will discuss the prospects of using lncRNAs as biomarkers or targets for precision medicine in OC. Although there is still plenty to learn about lncRNAs and technical challenges to be solved, the evidence of their involvement in OC and the development of acquired resistance are compelling and warrant further investigation for clinical applications.

Long non-coding RNA F11-AS1 inhibits HBV-related hepatocellular carcinoma progression by regulating NR1I3 via binding to microRNA-211-5p

Long non-coding RNAs (lncRNAs) could regulate growth and metastasis of hepatocellular carcinoma (HCC). In this study, we aimed to investigate the mechanism of lncRNA F11-AS1 in hepatitis B virus (HBV)-related HCC. The relation of lncRNA F11-AS1 expression in HBV-related HCC tissues to prognosis was analysed in silico. Stably HBV-expressing HepG2.2.15 cells were established to explore the regulation of lncRNA F11-AS1 by HBx protein, as well as to study the effects of overexpressed lncRNA F11-AS1 on proliferation, migration, invasion and apoptosis in vitro. Subsequently, the underlying interactions and roles of lncRNA F11-AS1/miR-211-5p/NR1I3 axis in HBV-related HCC were investigated. Additionally, the influence of lncRNA F11-AS1 and miR-211-5p on tumour growth and metastasis capacity of HepG2.2.15 cells were studied on tumour-bearing nude mice. Poor expression of lncRNA F11-AS1 was correlated with poor prognosis in patients with HBV-related HCC, and its down-regulation was caused by the HBx protein. lncRNA F11-AS1 was proved to up-regulate the NR1I3 expression by binding to miR-211-5p. Overexpression of lncRNA F11-AS1 reduced the proliferation, migration and invasion, yet induced apoptosis of HepG2.2.15 cells in vitro, which could be abolished by overexpression of miR-211-5p. Additionally, either lncRNA F11-AS1 overexpression or miR-211-5p inhibition attenuated the tumour growth and metastasis capacity of HepG2.2.15 cells in vivo. Collectively, lncRNA F11-AS1 acted as a modulator of miR-211-5p to positively regulate the expression of NR1I3, and the lncRNA F11-AS1/miR-211-5p/NR1I3 axis participated in HBV-related HCC progression via interference with the cellular physiology of HCC.

Novel Tumor Suppressor lncRNA Growth Arrest-Specific 5 (GAS5) In Human Cancer

Long noncoding RNAs (lncRNAs) play crucial regulatory roles in fundamental biological processes, and deregulations of lncRNAs have been linked to numerous human diseases, especially cancers. Of particular interest in this regard is lncRNA GAS5, which is mainly identified as a tumor suppressor in several cancers. GAS5 was significantly low expressed in multiple cancers and was associated with clinic-pathological characteristics and patient survival, indicating a novel potential diagnostic and prognostic biomarker, and a therapeutic target for cancer. Functionally, GAS5 is involved in cell proliferation, metastasis, invasion, apoptosis, epithelial-mesenchymal transition (EMT), and drug resistance, among others, via multiple molecular mechanisms, such as binding to DNA sequences, forming RNA-DNA triplex complex, triggering or suppressing the expression of genes, binding proteins to form chromatin-modifying complex, which activates or represses gene expression, and acting as miRNA sponge to suppress miRNA expression, leading to regulation of miRNA target genes. This review provides an overview of the current state of knowledge and role of GAS5 in clinical relevance, biological functions and molecular mechanisms underlying the dysregulation of expression and function of GAS5 in cancer. Finally, the potential prospective role as diagnostic and prognostic biomarker and therapeutic target in cancer is discussed.

LINC01118 Modulates Paclitaxel Resistance of Epithelial Ovarian Cancer by Regulating miR-134/ABCC1

BACKGROUND Epithelial ovarian cancer (EOC) has a high mortality rate and is a common malignant tumor of women, seriously impairing health. Chemoresistance is one of the major causes of poor prognosis. Therefore, analyzing the molecular mechanism of paclitaxel resistance has great significance. MATERIAL AND METHODS We analyzed aberrantly expressed lncRNAs in chemoresistant EOC cells by microarray and confirmed LINC01118 expression by real-time PCR. The paclitaxel sensitivity alternation was analyzed by MTS, flow cytometry, and Transwell assay, while wound healing assays were performed to assess apoptosis, migration, and invasion in vitro. The interaction between LINC01118 and miR-134 was confirmed by luciferase assay. RESULTS LINC01118 was highly expressed in EOC tissues and chemoresistant cells. Biological function experiments showed LINC01118 could facilitate paclitaxel resistance and promote migration and invasion while inhibiting apoptosis of EOC cells. Moreover, LINC01118 targets miR-134 and then affects ABCC1 expression. CONCLUSIONS LINC01118 acted as an oncogene and modulated EOC paclitaxel sensitivity by regulating miR-134/ABCC1.