When IGF-1 Meets Metabolic Inflammation and Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder associated with insulin resistance (IR) and hyperandrogenaemia (HA). Metabolic inflammation (MI), characterized by a chronic low-grade inflammatory state, is intimately linked with chronic metabolic diseases such as IR and diabetes and is also considered an essential factor in the development of PCOS. Insulin-like growth factor 1 (IGF-1) plays an essential role in PCOS pathogenesis through its multiple functions in regulating cell proliferation metabolic processes and reducing inflammatory responses. This review summarizes the molecular mechanisms by which IGF-1, via MI, participates in the onset and progression of PCOS, aiming to provide insights for studies and clinical treatment of PCOS.

Identification of Human Global, Tissue and Within-Tissue Cell-Specific Stably Expressed Genes at Single-Cell Resolution.

Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.

Efficacy and safety of laser combination therapy and laser alone therapy for keloid: a systematic review and meta-analysis.

To evaluate the efficacy and safety of laser alone therapy and laser combination therapy (mainly combined with other kinds of laser or steroids) for keloid.PubMed, Embase and Web of Science were searched for relevant articles from inception to June 2020. Comprehensive Meta-Analysis software 2.0 (CMA) was used to perform the meta-analysis.A total of 29 articles were included in this meta-analysis. During the mean follow-up of 14 (1-84) months, the overall improvement rates of baseline Vancouver scar scale (VSS) score and itch were 0.454 (95%CI 0.351-0.561, I2 = 0) and 0.786 (95%CI 0.613-0.895, I2 = 0) in the laser combination therapy group. The improvement rates of scar height and flexibility in the laser combination therapy group were 0.629 (95%CI 0.519-0.727, I2 = 52.089) and 0.784 (95%CI 0.251-0.975, I2 = 89.420). The average improvement rate of the scar score in laser combination therapy was 0.338 (0.201-0.510); however, there were insufficient data for laser alone therapy comparison. The laser combination therapy had a greater pain improvement rate, 0.580 (0.389-0.750) versus 0.420 (0.224-0.645), compared to laser alone therapy, and a greater degree of good or excellent (> 50%) improvement in the overall scar, 0.636 (95%CI 0.347-0.852) versus 0.149 (95%CI 0.032-0.482), with laser alone therapy. Moreover, a lower regrowth rate of 0.187 (0.129-0.263) versus 0.249 (0.060-0.631), a lower post-treatment pigmentation rate of 0.125 (0.091-0.169) versus 0.135 (0.058-0.282), and a lower infection rate of 0.047 (0.009-0.209) versus 0.076 (0.012-0.351) were observed in the laser combination therapy compared with those rates in the laser alone therapy.The overall effect of laser combination therapy was better than that of laser alone therapy, and the incidence of adverse reactions was lower in laser combination therapy than in laser alone therapy.

A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features.

BACKGROUND: Stomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations. METHODS: In this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association. RESULTS: Typical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly. CONCLUSION: The study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.

Improving the diversity of captured full-length isoforms using a normalized single-molecule RNA-sequencing method.

Human genes form a large variety of isoforms after transcription, encoding distinct transcripts to exert different functions. Single-molecule RNA sequencing facilitates accurate identification of the isoforms by extending nucleotide read length significantly. However, the gene or isoform diversity is lowly represented by the mRNA molecules captured by single-molecule RNA sequencing. Here, we show that a cDNA normalization procedure before the library preparation for PacBio RS II sequencing captures 3.2-6.0 fold more full-length high-quality isoform species for different human samples, as compared to the non-normalized capture procedure. Many lowly expressed, functionally important isoforms can be detected. In addition, normalized PacBio RNA sequencing also resolves more allele-specific haplotype transcripts. Finally, we apply the cDNA normalization based long-read RNA sequencing method to profile the transcriptome of human gastric signet-ring cell carcinomas, identify new cancer-specific transcriptome signatures, and thus, bring out the utility of the improved protocols in gene expression studies.