Prediction of Clinical Outcome in Endometrial Carcinoma Based on a 3-lncRNA Signature

Combination of circulating tumor cells, lncRNAs and DNA methylation for the diagnosis of endometrial carcinoma

Endometrial carcinoma (EC) is one of the most common gynecological malignant neoplasms, the prognosis of which is strongly related to the time of diagnosis, with an earlier diagnosis leading to a better prognosis. Therefore, effective diagnostic indicators and methods are needed to ensure early detection. The present study explored the following in EC: Circulating tumor cells (CTCs); the long noncoding RNAs (lncRNAs) RP4-616B8.5, RP11-389G6.3 and carboxy-terminal domain (CTD)-2377D24.6; and the methylation of cysteine dioxygenase type 1 (CDO1) and CUGBP Elav-like family member 4 (CELF4). In total, 85 patients, including 71 with EC, and 14 without EC (NO-EC) but with uterine fibroids or polyps, were included in the present study. In total, 46 patients with EC and 8 NO-EC patients underwent CTC detection. In the evaluation of the EC vs. NO-EC groups, the results showed that the CTC-positive rate of the EC group was 80.43% and that the area under the curve (AUC) value of CTCs was 0.8872 (P=0.0098). A total of 35 patients with EC and 14 NO-EC patients underwent detection of the RP4-616B8.5, RP11-389G6.3 and CTD-2377D24.6 lncRNAs. When the levels of the three lncRNAs RP4-616B8.5, RP11-389G6.3 and CTD-2377D24.6 were compared between the EC and NO-EC groups, they were higher in the EC group; the P-values were 0.0002, 0.0001 and <0.0001, respectively, and the AUC values were 0.8184, 0.8347 and 0.8265, respectively. In addition, a total of 35 patients with EC and 8 NO-EC patients underwent CDO1 and CELF4 DNA methylation analysis. The positive rates of the methylated genes CDO1 and CELF4 were 20% (7/35) and 5.71% (2/35), and the P-values of the comparisons between the EC and NO-EC groups were 0.1748 and 0.5004, respectively; the AUC values were 0.6000 and 0.5286. Furthermore, the combination of CTCs, and lncRNAs RP4-616B8.5, RP11-389G6.3 and CTD-2377D24.6 exhibited high performance in the detection of EC (AUC=0.9375).

microRNA 21 and long non-coding RNAs interplays underlie cancer pathophysiology: A narrative review

Non-coding RNAs (ncRNAs) are a diverse group of functional RNA molecules that lack the ability to code for proteins. Despite missing this traditional role, ncRNAs have emerged as crucial regulators of various biological processes and have been implicated in the development and progression of many diseases, including cancer. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two prominent classes of ncRNAs that have emerged as key players in cancer pathophysiology. In particular, miR-21 has been reported to exhibit oncogenic roles in various forms of human cancer, including prostate, breast, lung, and colorectal cancer. In this context, miR-21 overexpression is closely associated with tumor proliferation, growth, invasion, angiogenesis, and chemoresistance, whereas miR-21 inactivation is linked to the regression of most tumor-related processes. Accordingly, miR-21 is a crucial modulator of various canonical oncogenic pathways such as PTEN/PI3K/Akt, Wnt/β-catenin, STAT, p53, MMP2, and MMP9. Moreover, interplays between lncRNA and miRNA further complicate the regulatory mechanisms underlying tumor development and progression. In this regard, several lncRNAs have been found to interact with miR-21 and, by functioning as competitive endogenous RNAs (ceRNAs) or miRNA sponges, can modulate cancer tumorigenesis. This work presents and discusses recent findings highlighting the roles and pathophysiological implications of the miR-21-lncRNA regulatory axis in cancer occurrence, development, and progression. The data collected indicate that specific lncRNAs, such as MEG3, CASC2, and GAS5, are strongly associated with miR-21 in various types of cancer, including gastric, cervical, lung, and glioma. Indeed, these lncRNAs are well-known tumor suppressors and are commonly downregulated in different types of tumors. Conversely, by modulating various mechanisms and oncogenic signaling pathways, their overexpression has been linked with preventing tumor formation and development. This review highlights the significance of these regulatory pathways in cancer and their potential for use in cancer therapy as diagnostic and prognostic markers.

Interplay between LncRNA/miRNA and TGF-β Signaling in the Tumorigenesis of Gynecological Cancer

Gynecologic cancers are among the most common malignancies with aggressive features and poor prognosis. Tumorigenesis in gynecologic cancers is a complicated process that is influenced by multiple factors, including genetic mutations that activate various oncogenic signaling pathways, including the TGF-β pathway. Aberrant activation of TGF-β signaling is correlated with tumor recurrence and metastasis. It has been shown that non-coding RNAs (ncRNAs) have crucial effects on cancer cell proliferation, migration, and metastasis. Upregulation of various ncRNAs, including long non-coding RNAs (lncRNA) and microRNAs (miRNAs), has been reported in several tumors, like cervical, ovarian, and endometrial cancers, but their cellular mechanisms remain to be investigated. Thus, recognizing the role of ncRNAs in regulating the TGF-β pathway may provide novel strategies for better treatment of cancer patients. The present study summarizes recent findings on the role of ncRNAs in regulating the TGF-β signaling involved in tumor progression and metastasis in gynecologic cancers.

Bioinformatics Analysis of the Inflammation-Associated lncRNA-mRNA Coexpression Network in Type 2 Diabetes

Introduction

Diabetes is a chronic inflammatory state, and a key role of lncRNAs in diabetes complications is a new area of research.

Methods

In this study, key lncRNAs related to diabetes inflammation were identified by RNA-chip mining and lncRNA-mRNA coexpression network construction and finally verified by RT-qPCR.

Results

We ultimately obtained 12 genes, including A1BG-AS1, AC084125.4, RAMP2-AS1, FTX, DBH-AS1, LOXL1-AS1, LINC00893, LINC00894, PVT1, RUSC1-AS1, HCG25, and ATP1B3-AS1. RT-qPCR assays verified that LOXL1-AS1, A1BG-AS1, FTX, PVT1, and HCG25 were upregulated in the HG+LPS-induced THP-1 cells, and LINC00893, LINC00894, RUSC1-AS1, DBH-AS1, and RAMP2-AS1 were downregulated in the HG+LPS-induced THP-1 cells.

Conclusions

lncRNAs and mRNAs are extensively linked and form a coexpression network, and lncRNAs may influence the development of type 2 diabetes by regulating the corresponding mRNAs. The ten key genes obtained may become biomarkers of inflammation in type 2 diabetes in the future.

Endoplasmic reticulum stress related IncRNA signature predicts the prognosis and immune response evaluation of uterine corpus endometrial carcinoma

Background

Endoplasmic reticulum (ER) stress is closely related to the occurrence, development and treatment of tumors. Recent studies suggest ER stress as a therapeutic strategy of choice for cancer. However, ER stress-related long non-coding RNA (lncRNA) predictive value in endometrial carcinoma (UCEC) remains to be further evaluated. The purpose of this study was to establish relies on the signature of ER stress-related lncRNA forecast to predict the prognosis of patients with UCEC.

Methods

We downloaded the RNA expression profile dataset and matched clinical data from the Cancer Genome Atlas (TCGA) database, and applied univariate and multivariate Cox regression analysis to build predictive signature. Kaplan-meier method was used to evaluate overall survival (OS) and disease-free survival (DFS). Gene set enrichment analysis (GSEA) was used to study the functional characteristics. Single sample Gene set enrichment analysis (ssGSEA) was used to analyze the relationship between immune status and predicted signature. Correlations between the potential usefulness of treatment for UCEC patients and predictive signature were also analyzed.

Results

We established a signature composed of eight ER stress-related lncRNAs (MIR34AHG, AC073842.2, PINK1AS, AC024909.2, MIR31HG, AC007422.2, AC061992.1, AC003102.1). The signature of ER stress-related lncRNA provided better diagnostic value compared with age and tumor grade, and the area under the receiver operating curve was 0.788. The overall and disease-free survival probability of patients in the high-risk group is lower than that in the low-risk group. GSEA indicated that the pathways were mainly enriched for cancer, immunity and reproduction related pathways. ss-GSEA shows that prediction signature and activation of dendritic cells, immature dendritic cells, T helper cells and immune status of the Treg are significantly related. High-risk groups may against PD - 1/L1 immunotherapy and JNK inhibitors VIII, Z.LLNle.CHO, DMOG and JNK. 9 l more sensitive.

Conclusion

The ER stress signature can independently predict the prognosis of UCEC patients, and provide guidance for conventional chemotherapy and immunotherapy of UCEC patients.

A novel necroptosis-related lncRNA signature for predicting prognosis and anti-cancer treatment response in endometrial cancer

BACKGROUND

Necroptosis, a form of programmed cell death, underlies tumorigenesis and the progression of cancers. Anti-cancer strategies targeting necroptosis have increasingly been shown to present a potential cancer therapy. However, the predictive utility and anticancer sensitivity value of necroptosis-related lncRNAs (NRLs) for endometrial cancer (EC) are currently unknown.

METHODS

EC patient gene expression profiles and the corresponding clinical information collected from The Cancer Genome Atlas were used to identify NRLs that constituted a predictive signature for EC. The functional pathways, immune status, clinicopathological correlation, and anticancer drug sensitivity of the patients relative to the NRLs signatures were analyzed.

RESULTS

A signature composed of 7 NRLs (AC019080.5, BOLA3-AS1, AC022144.1, AP000345.2, LEF1-AS1, AC010503.4, and RPARP-AS1) was identified. The high-risk patient group with this signature exhibited a poorer prognosis and lower survival rate than low-risk group lacking this signature. This necroptosis-related lncRNA signature had a higher predictive accuracy compared with other clinicopathological variables (area under the receiver operating characteristic curve of the risk score: 0.717). Additionally, when patients were stratified based on other clinicopathological variables, the overall survival was significantly shorter in the high-risk versus low-risk group across all cohorts. Gene set enrichment analysis (GSEA) revealed that immune- and tumor-related signaling pathways and biological processes were enriched in the high-risk group compared to the low-risk group. Single-sample gene set enrichment analysis (ssGSEA) additionally showed that the resulting risk score was strongly correlated with EC patient immune status. Finally, patients with high-risk scores were more sensitive to the anti-cancer drugs such as Docetaxel, Mitomycin.C, Vinblastine, AZD.2281 (olaparib), AZD6244, and PD.0332991 (Palbociclib).

CONCLUSION

These findings reveal a novel necroptosis-related lncRNA signature for predicting EC patient prognosis and shed new light on anticancer therapy strategies for EC.

Prognostic signature of endoplasmic reticulum stress-related long noncoding RNAs in head and neck squamous cell carcinoma: Association with somatic mutation and tumor immune microenvironment

Background/purpose

Analysis of endoplasmic reticulum stress (ERS)-related long noncoding RNAs (LncRNAs) may enable prognostic stratification in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to comprehensively analyze the ERS-related LncRNAs signature and its effects on the prognosis, tumorigenesis, and tumor immune microenvironment in HNSCC.

Materials and methods

The transcriptome data of HNSCC were obtained from TCGA. Least absolute shrinkage selection operator algorithm, and multivariate Cox regression were used to screen LncRNAs for the signature construction. Somatic mutation, gene enrichment, and immune infiltration analyses were further performed.

Results

458 ERS-related LncRNAs were identified and 55 of which were correlated with HNSCC prognosis. Ten ERS-related LncRNAs were selected to establish a risk prediction signature. When dividing patients into high-risk and low-risk groups by signature score, high-risk group correlated with worse survival rates (hazard ratio = 1.211; 95% confidence interval 1.123-1.306, P < 0.001). The area under the curve was 0.751 and 0.716 in the training and validation cohorts at 3-year. Moreover, high-risk group have increased somatic mutation rates and reduced infiltration abundancy of B cells and CD8+ T cells.

Conclusion

The prognostic signature based on ERS-related LncRNAs may serve as a predictor of altered oncogene mutations and immune microenvironment, which provided an insight into the relationship between ERS, LncRNAs, and tumor progression.

Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when

This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm² and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.