The prognostic significance of galectin-1 (Gal-1) expression in cancerous patients has been assessed for several years while the results remain controversial. Thus, we performed the first comprehensive meta-analysis to evaluate the prognostic value of Gal-1 expression in cancerous patients. We searched Pubmed, Embase and Web of Science to recruit studies on the prognostic impact of Gal-1 expression in cancerous patients. Eighteen studies containing 2674 patients were involved in this meta-analysis until March 30, 2018. Pooled hazard ratios (HRs) with 95% confidence interval (95% CI) were calculated to estimate the effect using random-effects model. The pooled results revealed that high Gal-1 expression in cancer tissue associated with a poor OS (HR = 1.79, 95% CI 1.54–2.08, P < 0.001). In the subgroup of tumor type, it’s observed that high Gal-1 expression was significant correlated with poor OS in digestive cancers without heterogeneity (HR = 1.94, 95% CI 1.64–2.30, P < 0.001; fixed-effects model; I² = 20.1%, P = 0.276). Our present meta-analysis indicates that high Gal-1 expression might be a predictive factor of poor prognosis in cancers, particularly in digestive cancers.

The current research was designed to study the role of hydrogen in renal fibrosis and the renal epithelial to mesenchymal transition (EMT) induced by transforming growth factor-β1 (TGF-β1). Hydrogen rich water (HW) was used to treat animal and cell models. Unilateral ureteral obstruction (UUO) was performed on Balb/c mice to create a model of renal fibrosis. Human kidney proximal tubular epithelial cells (HK-2 cells) were treated with TGF-β1 for 36 h to induce EMT. Serum creatinine (Scr) and blood urea nitrogen (BUN) were measured to test renal function, in addition, kidney histology and immunohistochemical staining of alpha-smooth muscle actin (α-SMA) positive cells was performed to examine the morphological changes. The treatment with UUO induced a robust fibrosis of renal interstitium, shrink of glomerulus and partial fracture of basement membrane. Renal function was also impaired in the experimental group with UUO, with an increase of Scr and BUN in serum. After that, Western-blot was performed to examine the expression of α-SMA, fibronectin, E-cadherin, Smad2 and Sirtuin-1 (Sirt1). The treatment with HW attenuated the development of fibrosis and deterioration of renal function in UUO model. In HK-2 cells, the pretreatment of HW abolished EMT induced by TGF-β1. The down-regulation the expression of Sirt1 induced by TGF-β1 which was dampened by the treatment with HW. Sirtinol, a Sirt1 inhibitor, reversed the effect of HW on EMT induced by TGF-β1. HW can inhibit the development of fibrosis in kidney and prevents HK-2 cells from undergoing EMT which is mediated through Sirt1, a downstream molecule of TGF-β1.

TUBA1C is correlated with an unfavourable prognosis and the infiltration of immune cells in several cancers. However, its function as a significant biomarker for the prognosis of immunotherapy in pan-cancer remains unclear. This study aims at assessing the role of TUBA1C in pan-cancer at multiple levels, including mutations, gene expression, methylation, m⁶A methylation, and immune cell infiltration levels. Data retrieved from major public databases, such as TCGA, GEO, GTEx, GSCA, CancerSEA, HPA, and RNAactDrugs, revealed that TUBA1C expression was high in 33 cancer types. Survival analysis revealed that TUBA1C was a poor prognostic factor for 12 tumour types, and mutations, CNVs, and methylation affected the prognosis of some cancer types. Furthermore, TUBA1C was found to be related to immune-related genes, immune cell infiltration, and the immune microenvironment. In addition, the sensitivity of 10 anticancer drugs was associated with high TUBA1C expression. Therefore, TUBA1C may serve as a viable prognostic biomarker for immunotherapy of pan-cancer.

Background: Prostate cancer remains one of the most common malignant tumors among men worldwide, with its incidence showing a continuous global increase. In recent years, mitochondria-targeted therapeutic strategies have emerged as a prominent research focus in oncology. However, a systematic analysis of the research trends concerning mitochondria in prostate cancer treatment is currently lacking. This study employed bibliometric methods to conduct a comprehensive analysis of the dynamic progress in mitochondria-related prostate cancer research, ascertain its significant role, and identify potential mitochondria-targeted therapeutic targets. Furthermore, using computer-aided methods, we designed and optimized a specific antibody, providing a candidate strategy for prostate cancer control. Methods: This study utilized the Web of Science Core Collection database (2015–2023) to perform visual analysis of country-keyword network relationships using CiteSpace and the Bibliometric Online Analysis Platform. Target screening was conducted by integrating bioinformatics and research intelligent agents. Subsequently, inhibitory antibodies were designed and screened based on GeoBiologics, followed by systematic in vitro evaluation of their purity, antigen-antibody affinity, conformational stability, colloidal stability, and enzymatic inhibitory activity. Results: The role of mitochondria in prostate cancer has garnered significant attention. Research trends have shifted from fundamental mechanisms to addressing drug resistance, developing novel delivery systems, and exploring combination therapies, highlighting mitochondria as a promising target for clinical intervention. Lon Peptidase 1(LONP1) is closely associated with mitochondrial homeostasis and prostate cancer progression. Antibody_82-M1 effectively blocks the ATP-binding site of LONP1, demonstrating high affinity, favorable stability, a high degree of humanization, and excellent drug-like properties, indicating strong potential for clinical translation. Conclusion: The designed LONP1 inhibitory antibody offers a novel strategy for prostate cancer treatment. The proposed workflow – “bibliometric guidance – research intelligent agent screening – bioinformatics support” – proves beneficial for enhancing AI-driven scientific research and optimizing the screening of therapeutic targets for diseases.