Long non-coding RNAs in diabetic wound healing: Current research and clinical relevance.

Diabetic wounds are a protracted complication of diabetes mainly characterised by chronic inflammation, obstruction of epithelialization, damaged blood vessels and collagen production (maturation), as well as neuropathy. As a non-coding RNA (ncRNA) that lack coding potential, long non-coding RNAs (lncRNAs) have recently been reported to play a salient role in diabetic wound healing. Here, this review summarises the roles of lncRNAs in the pathology and treatments of diabetic wounds, providing references for its potential clinical diagnostic criteria or therapeutic targets in the future.

PD-L1 Triggered by Binding eIF3I Contributes to the Amelioration of Diabetes-Associated Wound Healing Defects by Regulating IRS4.

Persistent chronic inflammation and delayed epithelialization lead to stalled healing in diabetic ulcers (DUs). PD-L1 shows anti-inflammatory and proliferative activities in healing defects, whereas its function in DU pathogenesis remains unknown. Lower levels of PD-L1 were found in DU tissues, and exogenous PD-L1 has therapeutic effects in the healing process by accelerating re-epithelialization and attenuating prolonged inflammation, which contributed to the delayed wound closure. We detected the downstream effectors of PD-L1 using transcriptional profiles and screened the interacting proteins using immunoprecipitation in combination with mass spectrometry and coimmunoprecipitation assays. The biological functions of eIF3I‒PD-L1‒IRS4 axis were tested both in vivo and in vitro. Finally, we validated the expression levels of eIF3I, PD-L1, and IRS4 in DU tissues from human clinical samples by immunohistochemistry staining. Mechanistically, PD-L1 binds to eIF3I and promotes cutaneous diabetic wound healing by downregulating IRS4. These findings identify that the eIF3I‒PD-L1‒IRS4 axis contributes to wound healing defects, which can serve as a potential therapeutic target in DUs.

Gene set enrichment analysis and ingenuity pathway analysis to identify biomarkers in Sheng-ji Hua-yu formula treated diabetic ulcers.

ETHNOPHARMACOLOGICAL RELEVANCE: Sheng-ji Hua-yu (SJHY) formula is a Chinese herbal prescription for diabetic ulcers (DUs) treatment, which can accelerate wound reconstruction and shorten the healing time. However, its mechanism role maintains unclear. AIM OF THE STUDY: To elucidate the molecular mechanisms of SJHY application on DUs. MATERIALS AND METHODS: To begin with, transcriptome sequencing was adopted to identified differentially expression mRNAs among normal ulcers, DUs, and DUs + SJHY treatment in vivo. Liquid chromatography-tandem mass spectrometry was applied for the quality control of SJHY formula. GO and KEGG enrichment analysis were used to identify the mechanisms underlying the therapeutic effect of SJHY formula, and then gene set enrichment analysis and ingenuity pathway analysis were conducted for functional analysis. Further, qPCR detection was performed in vivo for validation. RESULTS: SJHY administration could regulate the glucose metabolic process, AMPK and HIF-1 pathway to accelerate healing processes of DUs. Besides, CRHR1, SHH, and GAL were identified as the critical targets, and SLC6A3, GRP, FGF23, and CYP27B1 were considered as the upstream genes of SJHY treatment. Combined with animal experiments, the prediction results were validated in DUs mice model. CONCLUSIONS: This study used modular pharmacology analysis to identify the biomarkers of SJHY formula and provide the potential therapeutic targets for DUs treatment as well.

The Identification of the Biomarkers of Sheng-Ji Hua-Yu Formula Treated Diabetic Wound Healing Using Modular Pharmacology.

Sheng-Ji Hua-Yu (SJHY) formula has been proved to reduce the severity of diabetic wound healing without significant adverse events in our previous clinical trials. However, based on multi-target characteristics, the regulatory network among herbs, ingredients, and hub genes remains to be elucidated. The current study aims to identify the biomarkers of the SJHY formula for the treatment of diabetic wound healing. First, a network of components and targets for the SJHY formula was constructed using network pharmacology. Second, the ClusterONE algorithm was used to build a modular network and identify hub genes along with kernel pathways. Third, we verified the kernel targets by molecular docking to select hub genes. In addition, the biomarkers of the SJHY formula were validated by animal experiments in a diabetic wound healing mice model. The results revealed that the SJHY formula downregulated the mRNA expression of Cxcr4, Oprd1, and Htr2a, while upregulated Adrb2, Drd, Drd4, and Hrh1. Besides, the SJHY formula upregulated the kernel pathways, neuroactive ligand-receptor interaction, and cAMP signaling pathway in the skin tissue homogenate of the diabetic wound healing mice model. In summary, this study identified the potential targets and kernel pathways, providing additional evidence for the clinical application of the SJHY formula for the treatment of diabetic wound healing.

Prognostic Nomogram for Liver Metastatic Colon Cancer Based on Histological Type, Tumor Differentiation, and Tumor Deposit: A TRIPOD Compliant Large-Scale Survival Study.

OBJECTIVE: A proportional hazard model was applied to develop a large-scale prognostic model and nomogram incorporating clinicopathological characteristics, histological type, tumor differentiation grade, and tumor deposit count to provide clinicians and patients diagnosed with colon cancer liver metastases (CLM) a more comprehensive and practical outcome measure. METHODS: Using the Transparent Reporting of multivariable prediction models for individual Prognosis or Diagnosis (TRIPOD) guidelines, this study identified 14,697 patients diagnosed with CLM from 1975 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) 21 registry database. Patients were divided into a modeling group (n=9800), an internal validation group (n=4897) using computerized randomization. An independent external validation cohort (n=60) was obtained. Univariable and multivariate Cox analyses were performed to identify prognostic predictors for overall survival (OS). Subsequently, the nomogram was constructed, and the verification was undertaken by receiver operating curves (AUC) and calibration curves. RESULTS: Histological type, tumor differentiation grade, and tumor deposit count were independent prognostic predictors for CLM. The nomogram consisted of age, sex, primary site, T category, N category, metastasis of bone, brain or lung, surgery, and chemotherapy. The model achieved excellent prediction power on both internal (mean AUC=0.811) and external validation (mean AUC=0.727), respectively, which were significantly higher than the American Joint Committee on Cancer (AJCC) TNM system. CONCLUSION: This study proposes a prognostic nomogram for predicting 1- and 2-year survival based on histopathological and population-based data of CLM patients developed using TRIPOD guidelines. Compared with the TNM stage, our nomogram has better consistency and calibration for predicting the OS of CLM patients.

Modular pharmacology-based approach to identify hub genes and kernel pathways of taodan granules treated psoriasis.

ETHNOPHARMACOLOGICAL RELEVANCE: Taodan granules (TDG) have been observed to decrease interleukins, or psoriasis area and severity index (PASI) score for psoriasis vulgaris, without significant adverse events. However, the regulatory network remains elucidated. AIM OF THE STUDY: The objective is to identify critical genes and kernel pathways of TDG treated psoriasis. MATERIALS AND METHODS: Firstly, construct a network of components-targets of TDG using network pharmacology. Secondly, the ClusterONE algorithm was used to build a modular network and identify critical genes and corresponding pathways. Thirdly, the critical genes and kernel pathways were verified in imiquimod (IMQ) induced psoriasis-like mice model. RESULTS: The results validated that TDG downregulated the mRNA expression of MMP2 (degree = 5, P < 0.05), IL6 (degree = 9, P < 0.05), TNF (degree = 14, P < 0.05), CCL2 (degree = 8, P < 0.05), CXCL2 (degree = 8, P < 0.05), IL1B (degree = 9, P < 0.05), and JUN (degree = 9, P < 0.05), while upregulated IL10 (degree = 8) expression. Besides, TDG were observed to regulate IL17 signaling pathway and TNF signaling pathway (size = 18), via the skin tissue homogenate of psoriasis-like mice. CONCLUSION: In summary, this study identified the potential targets and pathways, providing additional evidence for the clinical application of TDG treated psoriasis.

A Novel Evaluation System of Psoriasis Curative Effect Based on Bayesian Maximum Entropy Weight Self-Learning and Extended Set Pair Analysis.

BACKGROUND: Psoriasis is a complex skin disease and difficult to evaluate, and this study aimed to provide an objective and systematic approach for evaluating the efficacy of psoriasis. METHODS: We sought to construct a Bayesian network from sixteen indicators in four aspects of psoriasis (skin lesion conditions, laboratory indexes, quality of life, and accompanying symptoms) and obtained weights of each index by combining the analytic hierarchy process with maximum entropy self-learning. Furthermore, we adopted stability analysis to calculate the minimum sample size of the system. The extended set pair analysis was utilized to evaluate the efficacy based on improved weights, which overcomes the limitation of set pair analysis (unable to evaluate the efficacy with uncertain grades and thresholds). RESULTS: A total of 100 psoriasis vulgaris patients were included to evaluate the curative effect by the system. We obtained the weights of each index and the Euclidean distance for efficacy evaluation of 100 patients. The sensitivity analysis proved that the results had no significant change with the variation of single patient's indexes, which indicated that our results were stable to assess the effectiveness. CONCLUSIONS: We provided an available method of comprehensive effective evaluation of various indicators of psoriasis and based on both subjective and objective weights.

An update on the role of long non-coding RNAs in psoriasis.

ABSTRACT: Increasing evidence suggests that long non-coding RNAs (lncRNAs) are of vital importance for various biological processes, and dysregulation of lncRNAs is frequently associated with various diseases such as psoriasis. LncRNAs modulate gene expression at the transcriptional, post-transcriptional, and translational levels; however, the specific regulatory mechanisms of lncRNAs in psoriasis remain largely unexplored. This review provides an overview of recent studies investigating mechanisms and functions of lncRNAs in psoriasis, especially focusing on the role of lncRNAs in keratinocytes, T cells, and dendritic cells.