Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms.
In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics.
The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort.
This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

UNASSIGNED: Luminal A and B subtypes of breast cancer (BC) comprises up to 70% of all BC patients. LncRNAs can affect many biological and pathological processes, and dysregulation of them is related to human cancers. The potential role of lncRNA LINC00968 in luminal BC is still unclear.
UNASSIGNED: We analyzed the LINC00968 expression across 44 paired luminal BC tissues from the TCGA-BRCA RNA sequencing dataset. Besides, we used the GEPIA2 web server and GENEVESTIGATOR software, as well. Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) assay was performed to confirm the LINC00968 expression in 71 paired luminal BC tissues and two luminal A cell lines (MCF7 and T47D). Moreover, to better understanding the potential role of LINC00968 in luminal BC, computational data analyses including co-expression analysis, functional annotation analysis, and genetic alteration analysis have been done.
UNASSIGNED: The results of data analyses retrieved from BRCA dataset and databases revealed the significant downregulation of LINC00968 in luminal A and B BC. Also, the results of qRT-PCR in luminal BC tissues and cell lines confirmed the earlier data. LINC00968 expression was negatively associated with tumor stage and lymph node metastasis. Additionally, functional annotation analyses revealed that LINC00968 might be involved in vascular development and angiogenesis, extracellular matrix organization, and cell motility and migration. LINC00968 might play role in some cancer-related signaling pathways.
UNASSIGNED: Our study found that downregulation of LINC00968 might promote tumorigenesis, invasion, and metastasis of luminal BC.

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide and the third leading cause of cancer-related fatalities. Long non-coding RNAs (lncRNAs) are key regulators of diverse physiological processes and are dysregulated in a wide range of pathophysiological circumstances such as CRC. Studies revealed that aberrant expressions of lncRNAs clearly modulate the expression level of p53 gene in CRC, thereby transactivating multiple downstream pathways. P53 is regarded as a crucial tumor suppressor gene which promotes cell-cycle arrest, DNA repair, senescence or apoptosis in response to cellular stresses. P53 is also mutated in CRC as well as various types of human malignancies. Therefore, lncRNAs interact with the p53 signaling pathway in numerus ways and significantly influence CRC-related processes. The current findings in the investigation of the crosstalk between lncRNAs and the P53 pathway in controlling CRC carcinogenesis, tumor progression, and therapeutic resistance are summarized in the this review. A deeper knowledge of CRC carcinogenesis may also have implications in CRC prevention and treatment through more research.

The prevalence of pediatric obesity is rising rapidly worldwide, and \"omic\" approaches are helpful in investigating the molecular pathophysiology of obesity. This work aims to identify transcriptional differences in the subcutaneous adipose tissue (scAT) of children with overweight (OW), obesity (OB), or severe obesity (SV) compared with those of normal weight (NW). Periumbilical scAT biopsies were collected from 20 male children aged 1-12 years. The children were stratified into the following four groups according to their BMI z-scores: SV, OB, OW, and NW. scAT RNA-Seq analyses were performed, and a differential expression analysis was conducted using the DESeq2 R package. A pathways analysis was performed to gain biological insights into gene expression. Our data highlight the significant deregulation in both coding and non-coding transcripts in the SV group when compared with the NW, OW, and OB groups. A KEGG pathway analysis showed that coding transcripts were mainly involved in lipid metabolism. A GSEA analysis revealed the upregulation of lipid degradation and metabolism in SV vs. OB and SV vs. OW. Bioenergetic processes and the catabolism of branched-chain amino acids were upregulated in SV compared with OB, OW, and NW. In conclusion, we report for the first time that a significant transcriptional deregulation occurs in the periumbilical scAT of children with severe obesity compared with those of normal weight or those with overweight or mild obesity.

BACKGROUND: Concurrent existence of lncRNA and circular RNA at both nucleus and cytosol within a cell at different proportions is well reported. Previous studies showed that circular RNAs are synthesized in nucleus followed by transportation across the nuclear membrane and the export is primarily defined by their length. lncRNAs primarily originated through inefficient splicing and seem to use NXF1 for cytoplasm export. However, it is not clear whether circularization of lncRNA happens only in nucleus or it also occurs in cytoplasm. Studies indicate that circular RNAs arise when the splicing apparatus undergoes a phenomenon of back splicing. Minor spliceosome (U12 type) mediated splicing occurs in cytoplasm and is responsible for the splicing of 0.5% of introns of human cells. Therefore, possibility of cRNA biogenesis mediated by minor spliceosome at cytoplasm cannot be ruled out. Secondly, information on genes transcribing both circular and lncRNAs along with total number of RBP binding sites for both of these RNA types is extractable from databases. This study showed how these apparently unconnected pieces of reports could be put together to build a model for exploring biogenesis of circular RNA.
RESULTS: As a result of this study, a model was built under the premises that, sequences with special semantics were molecular precursors in biogenesis of circular RNA which occurred through catalytic role of some specific RBPs. The model outcome was further strengthened by fulfillment of three logical lemmas which were extracted and assimilated in this work using a novel data analytic approach, Integrated Cellular Geography. Result of the study was found to be in well agreement with proposed model. Furthermore this study also indicated that biogenesis of circular RNA was a post-transcriptional event.
CONCLUSIONS: Overall, this study provides a novel systems biology based model under the paradigm of Integrated Cellular Geography which can assimilate independently performed experimental results and data published by global researchers on RNA biology to provide important information on biogenesis of circular RNAs considering lncRNAs as precursor molecule. This study also suggests the possible RBP-mediated circularization of RNA in the cytoplasm through back-splicing using minor spliceosome.

BACKGROUND: It has been suggested that the anti-hyperglycemic effect of metformin could be associated with its impact on long non-coding RNA (lncRNA) expression levels. Accordingly, in the current study, we evaluated the effect of metformin on the expression of H19, MEG3, MALAT1, and GAS5 in in vitro and in vivo situations.
METHODS: The effect of hyperglycemia and metformin treatment on the lncRNAs expression level was evaluated in HepG2 cells. A total of 179 age- and sex-matched subjects, including 88 newly diagnosed patients with type 2 diabetes (T2D) and 91 healthy volunteers, were included in the case-control phase of the study. Moreover, 40 newly diagnosed patients participated in the study\'s open-labeled non-controlled clinical trial phase. The expression levels of lncRNA in HepG2 cells and whole blood samples were determined using QRT-PCR.
RESULTS: In vitro results showed that hyperglycemia induced H19 and MALAT1 and decreased GAS5 expression levels. Moreover, metformin decreased H19 and increased GAS5 expression in high glucose-treated cells. Case-control study findings revealed that the circulating levels of H19, MALAT1, and MEG3 were significantly elevated in T2D patients compared to the control subjects. Finally, results showed that the level of circulating H19 levels decreased while GAS5 increased in T2D patients after taking metformin for 2 months.
CONCLUSIONS: The results of the current study provided evidence that metformin could exert its effect in the treatment of T2D by altering the expression levels of H19 and GAS5.

UNASSIGNED: Acute lymphoblastic leukemia (ALL) is a malignant disease most commonly diagnosed in adolescents and young adults. This study aimed to explore potential signatures and their functions for ALL.
UNASSIGNED: Differentially expressed mRNAs (DEmRNAs) and differentially expressed long non-coding RNAs (DElncRNAs) were identified for ALL from The Cancer Genome Atlas (TCGA) and normal control from Genotype-Tissue Expression (GTEx). DElncRNA-microRNA (miRNA) and miRNA-DEmRNA pairs were predicted using online databases. Then, a competing endogenous RNA (ceRNA) network was constructed. Functional enrichment analysis of DEmRNAs in the ceRNA network was performed. Protein-protein interaction (PPI) network was then constructed. Hub genes were identified. DElncRNAs in the ceRNA network were validated using Real-time qPCR.
UNASSIGNED: A total of 2,903 up- and 3,228 downregulated mRNAs and 469 up- and 286 downregulated lncRNAs were identified for ALL. A ceRNA network was constructed for ALL, consisting of 845 lncRNA-miRNA and 395 miRNA-mRNA pairs. These DEmRNAs in the ceRNA network were mainly enriched in ALL-related biological processes and pathways. Ten hub genes were identified, including SMAD3, SMAD7, SMAD5, ZFYVE9, FKBP1A, FZD6, FZD7, LRP6, WNT1, and SFRP1. According to Real-time qPCR, eight lncRNAs including ATP11A-AS1, ITPK1-AS1, ANO1-AS2, CRNDE, MALAT1, CACNA1C-IT3, PWRN1, and WT1-AS were significantly upregulated in ALL bone marrow samples compared to normal samples.
UNASSIGNED: Our results showed the lncRNA expression profiles and constructed ceRNA network in ALL. Furthermore, eight lncRNAs including ATP11A-AS1, ITPK1-AS1, ANO1-AS2, CRNDE, MALAT1, CACNA1C-IT3, PWRN1, and WT1-AS were identified. These results could provide a novel insight into the study of ALL.

Cervical cancer (CESC), which threatens the health of women, has a very high recurrence rate.
This study aimed to identify the signature long non-coding RNAs (lncRNAs) associated with the prognosis of CESC and predict the prognostic survival rate with the clinical risk factors.
The CESC gene expression profiling data were downloaded from TCGA database and NCBI Gene Expression Omnibus. Afterwards, the differentially expressed RNAs (DERs) were screened using limma package of R software. R package \"survival\" was then used to screen the signature lncRNAs associated with independently recurrence prognosis, and a nomogram recurrence rate model based on these signature lncRNAs was constructed to predict the 3-year and 5-year survival probability of CESC. Finally, a competing endogenous RNAs (ceRNA) regulatory network was proposed to study the functions of these genes.
We obtained 305 DERs significantly associated with prognosis. Afterwards, a risk score (RS) prediction model was established using the screened 5 signature lncRNAs associated with independently recurrence prognosis (DLEU1, LINC01119, RBPMS-AS1, RAD21-AS1 and LINC00323). Subsequently, a nomogram recurrence rate model, proposed with Pathologic N and RS model status, was found to have a good prediction ability for CESC. In ceRNA regulatory network, LINC00323 and DLEU1 were hub nodes which targeted more miRNAs and mRNAs. After that, 15 GO terms and 3 KEGG pathways were associated with recurrence prognosis and showed that the targeted genes PTK2, NRP1, PRKAA1 and HMGCS1 might influence the prognosis of CESC.
The signature lncRNAs can help improve our understanding of the development and recurrence of CESC and the nomogram recurrence rate model can be applied to predict the survival rate of CESC patients in clinical practice.

Long non-coding RNAs (lncRNAs), associated with various cancers including colorectal cancer (CRC), could be collected from body fluids easily. Our aims were to determine the expression level of HOTTIP lncRNA in plasma samples of healthy individuals and CRC patients as well as their relationship with clinico-pathological characteristics of patients. First, total RNA was extracted from the plasma samples of 100 subjects including 50 patients and 50 age and sex matched healthy persons. Then, gene expression was measured using real-time PCR technique. The sensitivity and specificity of HOTTIP dysregulation in CRC and healthy individual\'s plasma was measured by receiver operating characteristic (ROC) analysis. As compared with healthy controls, HOTTIP lncRNA was over expressed in a statistically significant manner in plasma samples of patients (P=0.001). Significant relationship between HOTTIP expression and positive family history of CRC was observed, too (P=0.04). The ROC curve analysis showed an AUC value of 0.775, a specificity of 82%, a sensitivity of 76%, with a cut off value equal to 2.40 (P =0.001). HOTTIP transcript can be proposed as a new biomarker for early diagnosis due to the increased expression in plasma samples of patients with CRC and the relatively high sensitivity and specificity.

We set out to investigate biological functions and potential molecular mechanisms of long non-coding RNA (lncRNA) in hepatocellular carcinoma (HCC). HCC cell line Bel-7404 was cultured and transfected with antisense to the ubiquitin carboxyl-terminal hydrolase L1 (UCHL1-AS1). Viability and mobility were detected by MTT and wound healing assays. Additionally, enrichment analysis and functional networks of UCHL1-AS1 related genes in HCC were performed. Results showed that high level UCHL1-AS1 could effectively inhibit HCC cell migration. However, there was no significant correlation between overexpressed UCHL1-AS1 and HCC proliferation. Meanwhile, BMP4, CALM3, and HRAS were selected from 204 genes that related to UCHL1-AS1. All of these hub genes play critical roles in HCC occurrence and development. Thus, underlying molecular mechanisms among hub genes and UCHL1-AS1 in HCC might be valuable for prognosis and treatment.