Identification of clinical and molecular features of recurrent serous borderline ovarian tumour.

Background: Serous borderline ovarian tumour (SBOT) is the most common type of BOT. Fertility sparing surgery (FSS) is an option for patients with SBOT, though it may increase the risk of recurrence. The clinical and molecular features of its recurrence are important and need to be investigated in detail. Methods: An internal cohort of 319 patients with SBOT was collected from Aug 1, 2009 to July 31, 2019 from the Obstetrics and Gynecology Hospital of Fudan University in China. An external cohort of 100 patients with SBOT was collected from Aug 1, 2009 to Nov 30, 2019 from the Shandong Provincial Hospital in China. The risk factors for the recurrence were identified by multivariate cox analysis. Several computational methods were tested to establish a prediction tool for recurrence. Whole genome sequencing, RNA-seq, metabolomics and lipidomics were used to understand the molecular characteristics of the recurrence of SBOT. Findings: Five factors were significantly correlated with SBOT recurrence in a Han population: micropapillary pattern, advanced stage, FSS, microinvasion, and lymph node invasion. A random forest-based online recurrence prediction tool was established and validated using an internal cohort and an independent external cohort for patients with SBOT. The multi-omics analysis on the original SBOT samples revealed that recurrence is related to metabolic regulation of immunological suppression. Interpretation: Our study identified several important clinical and molecular features of recurrent SBOT. The prediction tool we established could help physicians to estimate the prognosis of patients with SBOT. These findings will contribute to the development of personalised and targeted therapies to improve prognosis. Funding: JL was funded by MOST 2020YFA0803600, 2018YFA0801300, NSFC 32071138, and SKLGE-2118 to Jin Li; JY was funded by the Initial Project for Young and Middle-aged Medical Talents of Wuhan City, Hubei Province ([2014] 41); HH was funded by MOST 2019YFA0801900 and 2020YF1402600 to He Huang; JS was funded by NSFC 22,104,080; CG was funded by Natural Science Foundation of Shanghai 20ZR1408800 and NSFC82171633; BL was funded by Natural Science Foundation of Shanghai 19ZR1406800.

LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis.

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for biomedical research by providing a variety of valuable information with the advancement of computational tools. Lineage analysis based on scRNA-seq provides key insights into the fate of individual cells in various systems. However, such analysis is limited by several technical challenges. On top of the considerable computational expertise and resources, these analyses also require specific types of matching data such as exogenous barcode information or bulk assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) data. To overcome these technical challenges, we developed a user-friendly computational algorithm called "LINEAGE" (label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis). Aiming to screen out endogenous markers of lineage located on mitochondrial reads from label-free scRNA-seq data to conduct lineage inference, LINEAGE integrates a marker selection strategy by feature subspace separation and de novo "low cross-entropy subspaces" identification. In this process, the mutation type and subspace-subspace "cross-entropy" of features were both taken into consideration. LINEAGE outperformed three other methods, which were designed for similar tasks as testified with two standard datasets in terms of biological accuracy and computational efficiency. Applied on a label-free scRNA-seq dataset of BRAF-mutated cancer cells, LINEAGE also revealed genes that contribute to BRAF inhibitor resistance. LINEAGE removes most of the technical hurdles of lineage analysis, which will remarkably accelerate the discovery of the important genes or cell-lineage clusters from scRNA-seq data.

HCV poly U/UC sequence-induced inflammation leads to metabolic disorders in vulvar lichen sclerosis.

Vulvar lichen sclerosis (VLS) is a dermatologic disorder that affects women worldwide. Women with VLS have white, atrophic papules on the vulva. They suffer from life-long intense pruritus. Corticosteroids are the first-line of treatments and the most effective medicines for VLS. Although VLS has been speculated as an autoimmune disease for a long time, its pathogenesis and the molecular mechanism is largely unknown. We performed a comprehensive multi-omics analysis of paired samples from VLS patients as well as healthy donors. From the RNA-seq analysis, we found that VLS is correlated to abnormal antivirus response because of the presence of Hepatitis C Virus poly U/UC sequences. Lipidomic and metabolomic analysis revealed that inflammation-induced metabolic disorders of fatty acids and glutathione were likely the reasons for pruritus, atrophy, and pigment loss in the vulva. Thus, the present study provides an initial interpretation of the pathogenesis and molecular mechanism of VLS and suggests that metabolic disorders that affect the vulva may serve as therapeutic targets for VLS.

Potential applications of MEG3 in cancer diagnosis and prognosis.

LncRNAs are emerging as integral functional and regulatory components of normal biological activities and are now considered as critically involved in the development of different diseases including cancer. In this review, we summarized recent findings on maternally expressed gene 3 (MEG3), a noncoding lncRNA, locates in the imprinted DLK1-MEG3 locus on human chromosome 14q32.3 region. MEG3 is expressed in normal tissues but is either lost or decreased in many human tumors and tumor derived cell lines. Studies have demonstrated that MEG3 is associated with cancer initiation, progression, metastasis and chemo-resistance. MEG3 may affect the activities of TP53, MDM2, GDF15, RB1 and some other key cell cycle regulators. In addition, the level of MEG3 showed good correlation with cancer clinicopathological grade. In summary, MEGs is an RNA-based tumor suppressor and is involved in the etiology, progression, and chemosensitivity of cancers. The alteration of MEG3 levels in various cancers suggested the possibility of using MEG3 level for cancer diagnosis and prognosis.