Revisiting the Genomic and Transcriptomic Landscapes from Female Malignancies Could Provide Molecular Markers and Targets for Precision Medicine

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

Identification of long non-coding RNAs and RNA binding proteins in breast cancer subtypes

Breast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

Dysregulated LncRNAs Act as Competitive Endogenous RNAs and Are Associated With Cervical Cancer Development in UYGHUR Women

Cervical cancer is one of the most malignant tumors in women, particularly those in rural and remote areas. Its underlying molecular mechanisms, including the functions of non-coding RNA (ncRNAs), require more extensive investigation. In this study, high throughput transcriptome sequencing (RNA-seq) was used to identify differentially expressed lncRNAs and mRNAs in normal, cervical intraepithelial neoplasia and cervical cancer tissues from Uyghur women in western China. Dysregulated lncRNAs were found to extensively participate in cervical cancer development, including viral carcinogenesis, cell cycle and cytokine-cytokine receptor signaling. Two miRNA-host lncRNAs, LINC00925 and MIR155HG, showed elevated expression in cervical cancer samples, but prolonged the survival time of cervical cancer patients. The 2 mature miRNAs of the above 2 lncRNAs, miR-9 and miR-155, also showed similar features in cervical cancer. In addition, we identified 545 lncRNAs with potential functions in regulating these 2 miRNAs as competing endogenous RNAs (ceRNAs). In summary, our study demonstrated the dysregulated lncRNAs/miRNAs, particularly LINC00925/miR-9 and MIR155HG/miR-155, regulate the development of cervical cancer by forming a interaction network with mRNAs, highlighting the importance of elucidating the underlying mechanisms of ncRNAs in cervical cancer development.

Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival

Long non-coding RNA’s (lncRNA) are RNA sequences that do not encode proteins and are greater than 200 nucleotides in length. They regulate complex cellular mechanisms and have been associated with prognosis in various types of cancer. We aimed to identify lncRNA sequences that are associated with high grade serous ovarian cancer (HGSC) and assess their impact on overall survival. RNA was extracted from 112 HGSC patients and 12 normal fallopian tube samples from our Biobank tissue repository. RNA was sequenced and the Ultrafast and Comprehensive lncRNA detection and quantification pipeline (UClncR) was used for the identification of lncRNA sequences. Univariate logistic and multivariate lasso regression analyses identified lncRNA that was associated with HGSC. Univariate and multivariate Cox proportional hazard ratios were used to evaluate independent predictors of survival. 1943 of 16,325 investigated lncRNA’s were differentially expressed in HGSC as compared to controls (p < 0.001). Nine of these demonstrated association with cancer after multivariate lasso regression. Our multivariate analysis of survival identified four lncRNA’s associated with survival in HGSC. Three out of these four were found to be independently significant after accounting for all clinical covariates. Lastly, seven lncRNAs were independently associated with initial response to chemotherapy; four portended a worse response, while three were associated with improved response. More research is needed, but there is potential for these lncRNAs to be used as biomarkers of HGSC or predictors of treatment outcome in the future.