Biomedical subjects

Lili Niu

Publications and source records attributed to Lili Niu.

4 recordsLinked to original sources

PubMed2026

Associations between gut microbiota on carcass traits and meat quality in Neijiang pigs, Yorkshire pigs, and their hybrids.

This study was designed as an exploratory analysis to compare carcass performance, meat quality traits, and gut microbiota of Neijiang pigs (NN), Yorkshire pigs (YY), and Yorkshire × Neijiang hybrid pigs (YN), with the goal of generating testable hypotheses regarding potential links between gut microbial composition and production phenotypes. Compared with NN pigs, YN hybrids exhibited improved carcass performance while inheriting the favorable meat quality characteristics of Neijiang pigs. The results of 16S rRNA sequencing analysis showed that the relative abundance of the microbiota was similar to that of NN pigs. LDA effect size (LEfSe) results showed that Streptococcus, Treponema, probable_genus_10 and Fibrobacter were the differentially enriched taxa in YN pigs (p < 0.05). Correlation analysis was performed on carcass, meat quality and intestinal microbiota screened out by LEfSe. The results showed that Akkermansia tended to positively associate with body length and oblique length in YN pigs; Dialister correlated positively with dressing rate and pH45min; Treponema showed positive trends with a*45min and a*24h (p < 0.05). Finally, the correlation network model preliminarily mapped associations among production traits, gut microbiota, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for exploratory screening. Nine core microbial taxa exhibited close correlations with phenotypic indicators, which implied that these microbes might modulate metabolic pathways to shape pig performance. Overall, hybrids inherited superior parental carcass and meat quality but harbored unique gut microbial communities relative to purebreds-these preliminary correlative observations generate new hypotheses that gut microbiota may contribute to heterosis-associated phenotypic advantages, which require further targeted validation.

Animals↗

PubMed2026

A machine learning-derived intratumoral heterogeneity-related signature predicts the prognosis for and therapeutic response in patients with skin cutaneous melanoma.

BACKGROUND: Reliable biomarkers for predicting prognosis and therapeutic response in skin cutaneous melanoma (SKCM) remain limited. This study aimed to develop an intratumoral heterogeneity (ITH)-related prognostic signature for SKCM using integrative machine learning. METHODS: RNA sequencing (RNA-seq) data from 472 SKCM patients in The Cancer Genome Atlas (TCGA) and 214 patients in the GSE65904 cohort were analyzed. ITH scores were calculated using the DEPTH2 algorithm. Differentially expressed genes (DEGs) were identified between high- and low-ITH groups [|log2fold change (FC)| ≥1, false discovery rate (FDR) <0.05]. Based on 38 prognostic DEGs identified by univariate Cox regression, we employed an integrative framework of 101 machine learning algorithm combinations to construct prognostic models in the TCGA training cohort. The model with the highest average concordance index (C-index) was validated in the GSE65904 cohort and selected as the prognostic ITH-related signature (PIRS). Associations of the PIRS risk score with tumor mutational burden (TMB), immune cell infiltration, immune checkpoint gene expression, and drug sensitivity were systematically evaluated. Model performance was assessed using receiver operating characteristic (ROC) curves and Cox regression analyses. RESULTS: A 38-gene PIRS was constructed using the plsRcox algorithm. Patients with high PIRS risk scores exhibited significantly poorer overall survival (OS) in both the TCGA and Gene Expression Omnibus (GEO) cohorts. The PIRS was identified as an independent prognostic factor, with area under the curve (AUC) values of 0.779, 0.734, and 0.756 for 1-, 3-, and 5-year survival, respectively. High-risk samples displayed significantly lower TMB (P<0.05), reduced immune and stromal cell infiltration (P<0.001), downregulated immune function, and decreased expression of immune checkpoint genes. Additionally, high- and low-PIRS risk score groups exhibited distinct sensitivity patterns to different classes of targeted agents. CONCLUSIONS: The machine learning-derived PIRS robustly predicts prognosis in SKCM patients. Its clinical application is promising for optimizing patient risk stratification and treatment decisions, though further prospective validation is warranted.

Skin cutaneous melanoma (SKCM)↗

PubMed2026

tRNA methylation: functional insights and epitranscriptomic regulation.

tRNAs, one of the most conserved and abundant RNAs, are central components of protein synthesis, transferring genetic information from DNA to proteins through a precise base-pairing mechanism. Post-transcriptional modifications of tRNAs by tRNA modifying enzymes are essential for maintaining their normal physiological functions, including methylation, isomerization and glycosylation. tRNA methylation, particularly 1-methyladenosine (m1A), 5-methylcytidine (m5C), and 7-methylguanosine (m7G), are among the most abundant and diverse types of post-transcriptional modifications of tRNA, which promote the stability of tRNA secondary and tertiary structures and allow for proper translation. In addition, tRNA methylation affects the production and function of tsRNA (tRNA-derived small RNA), small fragments of RNA that further regulate gene expression and protein synthesis. In our review, we discuss the relevant biological functions of tRNA methylation, including tRNA stability, protein translation, and tsRNA biogenesis.

RNA, Transfer↗

PubMed2024

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium.

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Proteomics↗