LncRNA HOXA­AS2 is a prognostic and clinicopathological predictor in patients with cancer: A meta­analysis.

Elevated expression of long non-coding RNA homeobox A cluster antisense RNA 2 (lncRNA HOXA-AS2) is known to have prognostic value in various solid tumors. The present meta-analysis aimed to comprehensively quantify its prognostic significance across a wider spectrum of malignancies and to provide an updated synthesis of evidence that could refine prognostic models. To achieve this aim, multiple databases were carefully searched for lncRNA HOXA-AS2-related articles published in the past 10 years. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to demonstrate the prognostic value of lncRNA HOXA-AS2 using Stata 15.0 software. The function of lncRNA HOXA-AS2 was inferred from its associations with key clinical outcomes such as lymph node metastasis, distant metastasis, tumor stage and tumor size, which may reflect its role in tumor biology. In the present systematic review and meta-analysis of 454 patients across 7 studies, it was found that high lncRNA HOXA-AS2 expression was significantly associated with a shorter overall survival (OS) time in patients with cancer (HR=2.14; 95% CI, 1.40-3.27; P<0.001). High lncRNA HOXA-AS2 expression was also associated with lymph node metastasis [odds ratio (OR)=2.06; 95% CI, 1.07-3.99; P=0.032], distant metastasis (OR=2.11; 95% CI, 1.15-3.88; P=0.016), advanced tumor stage (OR=2.71; 95% CI, 1.50-4.89; P=0.001) and larger tumor size (OR=2.02; 95% CI, 0.86-4.78; P=0.006). However, no significant association was observed with age (OR=1.00; 95% CI, 0.63-1.59; P=0.991) or sex (OR=1.55; 95% CI, 0.72-3.34; P=0.258). In conclusion, elevated expression of lncRNA HOXA-AS2 was significantly related to poor clinical outcomes in various cancer types, such as osteosarcoma, non-small cell lung cancer and papillary thyroid carcinoma, a finding that was further confirmed by the present study. Specifically, the potential of lncRNAHOXA-AS2 as a biomarker in assessing tumor stage, metastasis risk and OS in patients was demonstrated. However, the results of the present study also indicated that the expression of lncRNA HOXA-AS2 was not significantly associated with age or sex, suggesting its role in cancer progression might be independent of these factors. This insight may direct future research to place more focus on the relationship between lncRNA HOXA-AS2 and specific cancer types and clinical characteristics.

IGF2BP2-related modification patterns in pancreatic cancer: A machine learning-driven approach towards personalized treatment.

Pancreatic cancer (PC) is a malignant digestive system tumor with a very poor prognosis. N6-methyladenosine (m6A) is mediated by a variety of readers and participates in important regulatory roles in PC. Based on TCGA_PAAD, ICGC_AU_PAAD, ICGC_CA_PAAD, GSE28735 and GSE62452 datasets, We mapped the multi-omics changes of m6A readers in PC and found that m6A readers, especially IGF2BP family genes, had specific changes and were significantly associated with poor prognosis. An unsupervised consensus clustering algorithm was used to explore the correlation between specific expression patterns of m6A readers in PC and enrichment pathways, tumor immunity and clinical molecular subtypes. Then, the principal component analysis (PCA) algorithm was used to quantify specific expression patterns and screen core genes. Machine learning algorithms such as Bootstrapping and RSF were used to quantify the expression patterns of core genes and construct a prognostic scoring model for PC patients. What's more, pharmacogenomic databases were used to screen sensitive drug targets and small molecule compounds for high-risk PC patients in an all-around and multi-angle way. Our study has not only provided new insights into personalized prognostication approaches, but also thrown light on integrating tailored risk stratification with precision therapy based on IGF2BP2-mediated m6A modification patterns.

Work function mediated interface charge kinetics for boosting photocatalytic water sterilization.

Photocatalytic sterilization is an eco-friendly strategy to utilize solar energy for treating water contaminated with resistant bacteria. Here, we propose interface engineering to induce an internal electric field (IEF) in leaf-like Ti3C2Tx/TiO2 based on the work function (Φ) theory, which enhances photocatalytic sterilization performance by steering interface charge kinetics. Density functional theory (DFT) calculations and in situ irradiation X-ray photoelectron spectroscopy (ISI-XPS) results show that photogenerated charge carriers can be directionally separated by the IEF. The efficient charge kinetics benefits the reactive oxygen species (ROS) generation and hence a superior broad-spectrum sterilization performance. We employ the intrinsic physical characteristics of MXene to steer interface charge kinetics for photocatalysis, which exhibits great potential in water disinfection.

Identification of biomarkers for hepatocellular carcinoma based on single cell sequencing and machine learning algorithms.

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers around the world. Precision oncology will be crucial for further improving the prognosis of HCC patients. Compared with traditional bulk RNA-seq, single-cell RNA sequencing (scRNA-seq) enables the transcriptomes of a great deal of individual cells assayed in an unbiased manner, showing the potential to deeply reveal tumor heterogeneity. In this study, based on the scRNA-seq results of primary neoplastic cells and paired normal liver cells from eight HCC patients, a new strategy of machine learning algorithms was applied to screen core biomarkers that distinguished HCC tumor tissues from the adjacent normal liver. Expression profiles of HCC cells and normal liver cells were first analyzed by maximum relevance minimum redundancy (mRMR) to get a top 50 signature gene feature. For further analysis, the incremental feature selection (IFS) method and leave-one-out cross validation (LOOCV) were conducted to build an optimal classification model and to extract 21 potentially essential biomarkers for HCC cells. Our results provided new insights into HCC pathogenesis that might be valuable for HCC diagnosis and therapy.

Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer's disease.

Alzheimer's disease (AD) is a complex, and multifactorial neurodegenerative disease. Previous studies have revealed that oxidative stress, synaptic toxicity, autophagy, and neuroinflammation play crucial roles in the progress of AD, however, its pathogenesis is still unclear. Recent researches have indicated that ferroptosis, an iron-dependent programmed cell death, might be involved in the pathogenesis of AD. Therefore, we aim to screen correlative ferroptosis-related genes (FRGs) in the progress of AD to clarify insights into the diagnostic value. Interestingly, we identified eight FRGs were significantly differentially expressed in AD patients. 10,044 differentially expressed genes (DEGs) were finally identified by differential expression analysis. The following step was investigating the function of DEGs using gene set enrichment analysis (GSEA). Weight gene correlation analysis was performed to explore ten modules and 104 hub genes. Subsequently, based on machine learning algorithms, we constructed diagnostic classifiers to select characteristic genes. Through the multivariable logistic regression analysis, five features (RAF1, NFKBIA, MOV10L1, IQGAP1, FOXO1) were then validated, which composed a diagnostic model of AD. Thus, our findings not only developed genetic diagnostics strategy, but set a direction for further study of the disease pathogenesis and therapy targets.

Identification of EMT-Related lncRNAs as Potential Prognostic Biomarkers and Therapeutic Targets for Pancreatic Adenocarcinoma.

Epithelial-mesenchymal transition (EMT) can promote carcinoma progression by multiple mechanisms; many studies demonstrated the invasiveness of pancreatic adenocarcinoma (PAAD) associated with the EMT, but how it acts through an lncRNA-dependent manner is unknown. Here, we investigated 146 samples from The Cancer Genome Atlas (TCGA) and 92 samples from the International Cancer Genome Consortium (ICGC). By gene set variation analysis (GSVA) and weighted correlation network analysis (WGCNA), we explored the EMT-related long noncoding RNAs (EMTlnc). Then, we performed univariate Cox regression analysis to screen their prognostic value for PAAD. The least absolute contraction and selection operator (LASSO) Cox regression was used to establish EMT-related lncRNA prognostic signal (EMT-LPS). In addition, we established a competitive endogenous ceRNA network. Then, we identified 33 prognostic EMTlnc as prognostic lncRNAs and established an EMT-LPS which showed strong prognostic ability in stratification analysis. By corresponding risk scores, patients were divided into low-risk and high-risk subgroups. Principal component analysis (PCA) showed that these subgroups had individual EMT status. Enrichment analysis showed that in the high-risk subgroup, biological processes, pathways, and hallmarks related to malignant tumors are more common. What is more, we constructed a nomogram that had powerful ability to predict the overall survival rate (OS) of PAAD patients in two datasets. So, EMT-LPS are a principal element in PAAD's carcinoma progression and may help us in choosing the way of prognosis assessment and provide some clues to design the new drugs for PAAD.

The NF-κB/miR-488/ERBB2 axis modulates pancreatic cancer cell malignancy and tumor growth through cell cycle signaling.

Pancreatic cancer is one of the malignancies having the poorest prognosis due to late diagnoses and lack of efficient treatment regimens. The identification of potential miRNA-targeted gene axes could act as targets for developing novel treatment strategies. Herein, it was assessed that miR-488 expression was markedly downregulated within pancreatic carcinoma. Higher expression of miR-488 was shown to be linked to better prognosis rates of pancreatic carcinoma as per online data. Within two pancreatic tumor cells, MIA PaCa-2 and PANC-1, miR-488 overexpression significantly suppressed malignant cytological behavior by inhibiting cell viability, enhancing cell apoptosis, and inducing cell cycle G2/M-phase arrest. Moreover, miR-488 overexpression also decreased the protein levels of cell cycle regulators, including cyclin A, cyclin B, CDK1, and CDK2. miR-488 directly targets ERBB2 (receptor tyrosine-protein kinase2) to suppress the expression of ERBB2 by targeting its 3'UTR. ERBB2 knockdown in MIA PaCa-2 and PANC-1 cell lines suppressed, but miR-488 inhibition enhanced the cancer cell biological malignant behavior; the effects of miR-488 inhibition on pancreatic cancer cells were significantly reversed by ERBB2 knockdown. NF-κB suppressed the expression of miR-488 transcriptionally via targeting its promoter region, consequentially repressing the tumor-suppressive effects of miR-488 upon pancreatic tumor cells. Thus, an NF-κB/miR-488/ERBB2 axis modulating pancreatic cancer cell malignancy and tumor growth through cell cycle signaling was conclusively demonstrated.

Ferroptosis-Related IncRNAs Are Prognostic Biomarker of Overall Survival in Pancreatic Cancer Patients.

Pancreatic cancer (PC) is one of the most lethal malignancies, the mortality and morbidity of which have been increasing over the past decade. Ferroptosis, a newly identified iron-dependent cell death pattern, can be induced by iron chelators and small lipophilic antioxidants. Nonetheless, the prognostic significance of ferroptosis-related lncRNAs in PC remains to be clarified. We obtained the lncRNA expression matrix and clinicopathological information of PC patients from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) datasets in the current study. Firstly, we conducted Pearson correlation analysis to delve into the ferroptosis-related lncRNAs, and univariate Cox analysis was implemented to examine the prognostic values in PC patients. Twenty-three prognostic ferroptosis-related lncRNAs were confirmed and loaded into the least absolute shrinkage and selection operator Cox (LASSO-Cox) analysis, then a ferroptosis-related lncRNA prognostic marker (Fe-LPM) was established in the TCGA dataset. Risk scores of patients were calculated and segregated PC patients into low-risk and high-risk subgroups in each dataset. The prognostic capability of Fe-LPM was also confirmed in the ICGC dataset. Gene set enrichment analysis (GSEA) revealed that several ferroptosis-related pathways were enriched in low-risk subgroups. Furthermore, we adopted a multivariate Cox regression to establish a nomogram based on risk score, age, pathological T stage and primary therapy outcome. A competing endogenous RNA (ceRNA) network was also created relied on four of the twenty-three ferroptosis-related lncRNAs. In conclusion, the eight Fe-LPM can be utilized to anticipate the overall survival (OS) of PC patients, which are meaningful to guiding clinical strategies in PC.

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients.

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

DSCR9/miR-21-5p axis inhibits pancreatic cancer proliferation and resistance to gemcitabine via BTG2 signaling.

The outcome of pancreatic adenocarcinoma (PAAD) patients is poor, given resistance to gemcitabine. Long noncoding RNA (lncRNA) has been implicated in the carcinogenesis of pancreatic cancer; however, its function and mechanism in PAAD resistance to gemcitabine (GEM) are yet unknown. Herein, we demonstrate that lncRNA DSCR9 is significantly reduced in PAAD in vitro and in vivo. CCK-8, BrdU and flow cytometry assays show that overexpression of DSCR9 markedly suppresses pancreatic cancer cell proliferation and invasion, and promotes apoptosis under gemcitabine treatment. BTG2 acts as a tumor suppressor by reducing the proliferation and invasion of pancreatic cancer cells and increasing gemcitabine-induced apoptosis. Immunofluorescence (IF) staining combined with fluorescence in situ hybridization (FISH) of pancreatic cancer tissues shows that DSCR9 and BTG2 are both increased in pancreatic cancer tissues. Luciferase assay shows that miR-21-5p simultaneously binds to DSCR9 and 3'UTR of BTG2; DSCR9 relieves miR-21-5p-induced inhibition of BTG2 by competing with BTG2 for miR-21-5p binding. Overexpression of miR-21-5p enhances the invasiveness of pancreatic cancer cells by promoting cancer cell proliferation and invasion and attenuating gemcitabine-induced apoptosis. Overexpression of miR-21-5p attenuates the effect of DSCR9 overexpression on BTG2 expression and invasiveness of pancreatic cancer cells. Finally, miR-21-5p expression is increased, while BTG2 expression is decreased in pancreatic cancer tissues. miR-21-5p is negatively correlated with DSCR9 and BTG2. In conclusion, the DSCR9/miR-21-5p/BTG2 axis modulates pancreatic cancer proliferation, invasion, and gemcitabine resistance.

Interplay of four types of RNA modification writers revealed distinct tumor microenvironment and biological characteristics in pancreatic cancer.

Background: Pancreatic cancer (PC) is one of the most lethal malignancies and carries a dismal mortality and morbidity. Four types of RNA modification (namely m6A, m1A, APA and A-to-I) could be catalyzed by distinct enzymatic compounds ("writers"), mediating numerous epigenetic events in carcinogenesis and immunomodulation. We aim to investigate the interplay mechanism of these writers in immunogenomic features and molecular biological characteristics in PC. Methods: We first accessed the specific expression pattern and transcriptional variation of 26 RNA modification writers in The Cancer Genome Atlas (TCGA) dataset. Unsupervised consensus clustering was performed to divide patients into two RNA modification clusters. Then, based on the differentially expressed genes (DEGs) among two clusters, RNA modification score (WM_Score) model was established to determine RNA modification-based subtypes and was validated in International Cancer Genome Consortium (ICGC) dataset. What's more, we manifested the unique status of WM_Score in transcriptional and post-transcriptional regulation, molecular biological characteristics, targeted therapies and immunogenomic patterns. Results: We documented the tight-knit correlations between transcriptional expression and variation of RNA modification writers. We classified patients into two distinct RNA modification patterns (WM_Score_high and _low), The WM_Score_high subgroup was correlated with worse prognosis, Th2/Th17 cell polarization and oncogenic pathways (e.g. EMT, TGF-ß, and mTORC1 signaling pathways), whereas the WM_Score_low subgroup associated with favorable survival rate and Th1 cell trend. WM_Score model also proved robust predictive power in interpreting transcriptional and post-transcriptional events. Additionally, the potential targeted compounds with related pathways for the WM_Score model were further identified. Conclusions: Our research unfolds a novel horizon on the interplay network of four RNA modifications in PC. This WM_Score model demonstrated powerful predictive capacity in epigenetic, immunological and biological landscape, providing a theoretical basis for future clinical judgments of PC.

Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma.

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

Identification of the function and mechanism of m6A reader IGF2BP2 in Alzheimer's disease.

Alzheimer's disease, the most common form of dementia in the elderly, is a kind of neurodegenerative disease. However, its pathogenesis and diagnosis remain unclear. M6A is related to nervous system development and neurodegenerative diseases. Here in this study, using multiple RNA-seq datasets of Alzheimer's brain tissues, along with bioinformatic analysis, we innovatively found that m6A reader protein IGF2BP2 was abnormally highly expressed in Alzheimer's patients. After compared between Alzheimer's and normal brain samples, and between IGF2BP2- high and IGF2BP2- low subgroups of Alzheimer's patients, we took the shared differentially expressed genes as the relevant gene sets of IGF2PB2 affecting Alzheimer's disease occurrence for subsequent analysis. Then, weight gene correlation analysis was conducted and 17 functional modules were identified. The module that most positively correlated with Alzheimer's disease and IGF2PB2-high subgroups were mainly participated in ECM receptor interaction, focal adhesion, cytokine-cytokine receptor interaction, and TGF-beta signaling pathway. Afterwards, a hub gene-based model including 20 genes was constructed by LASSO regression and validated by ROC curve for Alzheimer diagnosis. Finally, we preliminarily elucidated that IGF2BP2 could bind with mRNAs in a m6A-dependent manner. This study first elucidates the pathogenic role of IGF2BP2 in Alzheimer's disease. IGF2BP2 and its relevant m6A modifications are potential to be new diagnostic and therapeutic targets for Alzheimer's patients.

Essential moieties of myricetins, quercetins and catechins for binding and inhibitory activity against α-Glucosidase.

α-Glucosidase inhibition of 11 flavonoids, including myricetins, quercetins and catechins were studied through initial reaction velocity, IC50 value, inhibition kinetics, fluorescence quenching and molecular docking. It was found that C4 = O, C2 = C3, 3-OH and 5'-OH were essential moieties for α-glucosidase inhibition of myricetin that was shown with the highest inhibitory activity. The trans-conformational catechins was shown with stronger inhibition effects than the cis-conformational ones. Further, gallocatechin was an uncompetitive inhibitor, while myricetin, myricetrin, quercetin and catechin were competitive ones. 3-OH and 5'-OH promoted myricetin to bind with the enzyme active site through hydrogen bondings. The presence of C4 = O and C2 = C3 increased electron delocalization in ring A-C for myricetin and quercetin, and this enhanced stability of π-conjugations with aromatic residues of amino acids. However, 5'-OH decreased the quenching effects because it limited π-conjugations of ring B with key fluorescent residues. Notably, for same flavonoid sort, the constants that indicate binding affinity of flavonoids to α-glucosidase, including reciprocal of competitive inhibition constant, fluorescence quenching constant and binding energy followed same order as the inhibitory activity, indicating that α-glucosidase inhibition of the flavonoids resulted from binding interactions between them, and that the methods above can be combined reasonably to characterize flavonoid-enzyme binding interactions.

Expressional and Prognostic Value of S100A16 in Pancreatic Cancer Via Integrated Bioinformatics Analyses.

Studies have shown that the calcium-binding protein family S100 may play a role in the development of pancreatic cancer (PC), but the role of S100A16 in PC is still unknown. In this study, Oncomine was first used to detect the expression level and prognosis of S100A16 in PC and other tumors. The results showed that S100A16 was highly expressed in PC tissues compared with a normal pancreas, and the increased expression level may be related to poor prognosis in PC patients. The TCGA and ICGC RNA-seq data of PC patients were downloaded, and the S100A16-related differentially expressed genome (DEGs) was defined by taking the intersection of two gene sets. The GO and KEGG pathways were then analyzed. For clinical analysis, boxplots were depicted for the correlation between clinical characteristics and S100A16 expression. Then Cox regression was applied for exploring the prognostic value of S100A16 for PDAC patients. Based on the Cox regression model, we further estabished a S100A16-related risk score system to strengthen the ability to predict patients' prognosis. After integrating the risk score model and multiple clinicopathological factors, we finally established a nomogram that could predict the survival time of patients. Moreover, Gene set enrichment the effect of S100A16 expression differences on downstream biological processes. At last, using TIMER, ImmuneCellAI and GSEA we analyzed the correlation between S100A16 and pancreatic cancer immune infiltration and predicted the response of patients to checkpoint Blocker (ICB). In summary, S100A16 is involved in the occurrence and development of PC, affecting the prognosis of patients, and may have potential reference values for the immunotherapy of PC.

S100A14 promotes progression and gemcitabine resistance in pancreatic cancer.

S100 calcium binding protein A14 (S100A14) plays an important role in the progression of several types of cancer. However, its roles in pancreatic ductal adenocarcinoma (PDAC) are largely unexplored. Here, we characterized the functional roles of S100A14 in the progression and chemoresistance of PDAC. Gene expression microarray identified that S100A14 was significantly highly expressed in four pairs of human PDAC tumor compared with corresponding non-tumor tissues genes. Quantitative reverse transcription PCR (qRT-PCR), western blotting and immunohistochemical staining (IHC) showed that S100A14 was frequently overexpressed in PDAC cell lines and tissues. Moreover, expression level of S100A14 was positively correlated to advanced cancer stages. Further, Kaplan-Meier survival analysis suggested that PDAC patients with low S100A14 expression had longer overall survival in TCGA PDAC datasets. Transient overexpressing of S100A14 promoted cell proliferation, anchorage-independent colony formation, cell migration and invasion in cell lines with low endogenous S100A14 levels, while transient silencing of S100A14 inhibited cell proliferation, anchorage-independent colony formation, cell migration and invasion in cell lines with high endogenous S100A14 levels. Persistent knockdown of S100A14 by transducing shRNAs carrying lentivirus inhibited subcutaneous tumor formation in nude mice, and sensitized the PDAC cells to gemcitabine treatment. Taken together, S100A14 exhibited oncogenic properties by promoting cell proliferation, transformation, migration and invasion, and enhanced in vivo tumor growth. More importantly, inhibition of S100A14 could effectively abrogate the cancerous properties of the PDAC cells. Our study indicated that S100A14 was a valuable target for the development of therapeutic strategy, as well as a diagnostic and prognosis biomarker for PDAC patients.

Meta-analysis of the Diagnostic Performance of Circulating MicroRNAs for Pancreatic Cancer.

Background: Numerous studies have suggested that differentially expressed miRNAs may be promising diagnostic markers for pancreatic cancer (PC), but the results are inconsistent. We aimed to summarize the diagnostic accuracy of circulating miRNAs, carbohydrate antigen 19-9 (CA19-9), and the combination of miRNAs and CA19-9. Material and Methods: A literature search of online databases including PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI) and WanFang was conducted. Relative data were extracted from eligible included studies, and a meta-analysis was performed. Results: A total of 46 studies involving 4,326 PC patients and 4,277 non-PC controls were included. The pooled sensitivity (SEN), specificity (SPE) and AUC of the circulating miRNAs for differentiating PC patients from non-PC controls were 0.79 (0.77-0.81), 0.77 (0.75-0.79), and 0.85 (0.81-0.87), respectively. The combination of miRNAs and CA19-9 greatly improved the SEN, SPE and AUC to 0.84 (0.80-0.87), 0.91 (0.89-0.93) and 0.94 (0.92-0.96), respectively. Moreover, circulating miRNAs also yielded an acceptable diagnostic accuracy for early-stage PC with a SEN of 0.79 (0.76-0.82), a SPE of 0.74 (0.68-0.79) and an AUC of 0.81 (0.77-0.84). Conclusion: Circulating miRNAs exhibited satisfactory diagnostic performance for PC and even early-stage PC. The combination of circulating miRNAs and CA19-9 can further improve the diagnostic accuracy, providing a novel strategy for PC diagnosis.

Identification and Development of Long Non-coding RNA Associated Regulatory Network in Pancreatic Adenocarcinoma.

BACKGROUND AND AIMS: Pancreatic adenocarcinoma (PAAD) is the most lethal cancer type around the world. With the in-depth exploration of the function of long non-coding RNAs (lncRNAs), the competing endogenous RNA (ceRNA) mechanism has shown its potential to partially reveal the pathogenesis of PAAD. This study aimed to construct a lncRNA-associated ceRNA network and explore ceRNA regulatory axes with experimental and prognostic value in PAAD. METHODS: First, we applied differential expression analysis in the TCGA_PAAD dataset. Then, interaction analysis and survival analysis in multiple RNA interaction databases were conducted to construct a ceRNA network. Finally, a potential regulatory axis was validated using clinical samples and cell lines by quantitative realtime PCR (qRT-PCR). RESULTS: A ceRNA network comprising 13 lncRNAs, 96 miRNAs, and 30 mRNAs was successfully constructed. Survival analysis further narrowed this network to five lncRNAs, three miRNAs, and seven mRNAs, which were significantly associated with patients' overall survival. A potential regulatory axis CASC8-miR-129-5p-TOB1 was further experimentally validated. The expression of these genes was associated with clinicopathological factors and their expression trend was consistent with ceRNA mechanism. Specifically, knockdown of lncRNA-CASC8 led to the overexpression of miR-129-5p and down-regulation of TOB1, while overexpression of CASC8 showed opposite effects. CONCLUSION: This novel ceRNA regulatory network could provide new insight into the pathogenesis of PAAD. The new regulatory axis CASC8-miR-129-5p-TOB1 might serve as a potential therapeutic target for patients.

Multiomics integrative analysis for gene signatures and prognostic values of m6A regulators in pancreatic adenocarcinoma: a retrospective study in The Cancer Genome Atlas project.

N6-methyladenosine(m6A) is the most abundant post-transcriptional RNA modification in eukaryotes. However, little is known about its role in pancreatic adenocarcinoma (PAAD). The aim of our study was to identify gene signatures and prognostic values of m6A regulators in PAAD. Patients from 3 different datasets with complete genomic and transcriptomic sequencing data were enrolled. Survival analysis for different gene alterations was performed using log-rank tests and Cox regression model. The association between alteration of m6A regulators and clinicopathological characteristics was examined using chi-square test. Results showed a high frequency of copy number alterations (CNAs) of m6A regulatory genes in PAAD patients, but somatic mutations were rarely happened. CNAs and mutations of m6A regulatory genes was associated with patient's gender, pathologic stage and resected tumor size. Patients with "gain of function" for m6A "reader" genes combined with copy number loss of "writers" or "erasers" had worse overall survival (OS) compared with other patterns. Moreover, copy number gain of m6A "reader" gene insulin growth factor 2 binding protein 2 (IGF2BP2) was an independent risk factor for OS (HR = 2.392, 95%CI: 1.392-4.112, p<0.001) and disease-free survival (DFS) (HR = 2.400, 95%CI: 1.236-4.659, p=0.010). Gene Set Enrichment Analysis (GSEA) indicated that IGF2BP2 was correlated with multiple biological processes associated with cancer, of which the most significant processes were relevant to cancer cell cycle, cell immortalization and tumor immunity. To sum up, a significant relationship was found between m6A genomic alterations and worse clinical outcomes. These innovative findings are expected to guide further research on the mechanism of m6A in PAAD.

The MicroHand S robotic-assisted versus Da Vinci robotic-assisted radical resection for patients with sigmoid colon cancer: a single-center retrospective study.

BACKGROUND: Sigmoid colon cancer is a lethal disease and has a strong indication for surgery. Robotic-assisted surgery is one of the promising alternative treatment for this disease. Nowadays, the MicroHand S surgical system and the Da Vinci surgical system have been assembled in China. However, there is still no report to study the therapeutic effects of the two robotic-assisted surgical systems. Thus, the purpose of this study was to compare clinical and economic outcomes of patients with sigmoid colon cancer undergoing robot-assisted radical surgery via The MicroHand S or Da Vinci surgical system. METHODS: The clinical data of 45 patients with sigmoid colon cancer undergoing the MicroHand S or Da Vinci robotic-assisted surgery at The Third Xiangya Hospital of Central South University from January 2017 to January 2019 were retrospectively analyzed. RESULTS: Twenty-one patients received MicroHand S robotic-assisted radical surgery and 24 patients received Da Vinci robot-assisted radical surgery. No significant differences were observed in terms of operation time, number of lymph node harvested, blood loss, intestinal exhaust time, time of oral feeding resumption, volume of abdominal cavity 24-h drainage, hospital stay, complication and rate of conversion, removal time of drainage tube and catheter between MicroHand S and Da Vinci group. However, the MicroHand S group had significantly lower hospitalization costs (P = 0.002) and shorter time to get out of bed after surgery (P = 0.04). In addition, no recurrence and metastases were observed in both groups during the follow-up. CONCLUSIONS: In patients with sigmoid colon cancer, the Da Vinci surgical system did not show obvious clinical advantages compared to the MicroHand S surgical system in surgical outcomes. However, the MicroHand S surgical platform showed advantages in terms of the hospitalization costs and length of postoperative bedtime. The outcome of this study will probably result in a shift to the MicroHand S surgical system as treatment preference in China.