Background: Polycystic kidney disease (PKD) represents the most prevalent inherited progressive kidney disorder in humans. Due to complexity of the genetic network behind the disease, the molecular mechanisms of PKD are still poorly understood yet. Objectives: This study aimed to develop a ciliogenesis-associated gene network for PKD patients and comprehensively understand the molecular mechanisms underlying the disease. Method: The potential hub genes were selected based on the differential expression analysis from the GEO database. Meanwhile, the primary hub genes were further elucidated by both in vivo and in vitro experiments. Results: In this study, we established a comprehensive differentially expressed genes profile (including GNAS, PI4KB, UMOD, SLC7A13, and MIOX) for PKD patients compared with the control specimen. At the same time, enrichment analysis was utilized to demonstrate that the G-protein-related signaling and cilia assembling signaling pathways were closely associated with PKD development. The further investigations of the interaction between 2 genes (GNAS and PI4KB) with in vivo and in vitro analyses revealed that PI4KB functioned as a downstream factor for GNAS and spontaneously activated the phosphorylation of Akt into p-Akt for ciliogenesis in PKD formation. The PI4KB depletion mutant zebrafish model displayed a PKD phenotype as well as absence of primary cilia in the kidney. Conclusions: Collectively, our work discovered an innovative potential signaling pathway model for PKD formation, which provided a valuable insight for future study of the mechanism of this disease.

Cholangiocarcinoma (CCA) is a malignancy arising from multiple locations along the biliary tree, which is still lacking effective diagnostic biomarkers. The present study aimed to provide a comprehensive differential gene expression profile for the disease. The differentially expressed genes (DEGs) for CCA were explored in‑depth using a Gene Expression Omnibus (GEO) dataset, an internal cohort of clinical participants as well as in vitro experiments with the HUCCT1 cell line. Based on the GEO dataset, potential biomarker genes were proposed and the enriched biological processes as well as signaling pathways were further investigated. A protein‑protein interaction network of target genes was established. In the clinical specimens, the functions of the primary candidate, FBJ murine osteosarcoma viral oncogene homolog B (FOSB), were evaluated by reverse transcription‑quantitative (RT‑q)PCR and western blot analysis. A Cell Counting Kit‑8 (CCK‑8) assay was used for a functional study on FOSB. The results indicated that, compared with non‑tumor bile duct tissues, primary CCA samples had 676 differentially expressed genes, including 277 downregulated and 399 upregulated ones. Among these, HBD, FOSB, HBB, ITIH2, FCGBP, MT1JP, PIJR, SLC38A1, COL10A1 and MMP19 were determined to be the most significant DEGs. At the same time, upregulated genes were enriched in ‘vasculature development’ and ‘IL‑17 signaling pathways’. Downregulated genes were enriched in ‘extracellular matrix progress’ and ‘glucuronate signaling pathway’. The patients with CCA displayed decreased levels of hemoglobin. Compared with paracancerous tissues, CCA cancerous tissues exhibited increased RNA and protein expression levels of FOSB according to RT‑qPCR and western blot analysis, respectively. Furthermore, FOSB expression influenced the proliferation/viability of the CCA cell line HUCCT1. In conclusion, the present study suggested that the FOSB gene may serve as a primary biomarker candidate for CCA, providing a valuable reference for its clinical implementation.

The aim of this work was to systematically compare the differences between sorafenib and lenvatinib for patients with hepatocellular carcinoma (HCC) from genetic and clinical perspectives. The mRNA and miRNA sequencing information of patients with HCC treated with either sorafenib or lenvatinib was analyzed using differential expression and a protein–protein interaction assay. The clinical manifestations and adverse events of the 2 drugs were also investigated. Compared with patients with HCC treated with sorafenib, patients treated with lenvatinib developed 8 differentially expressed genes (DEGs, FGF4, FGF23, UNC13C, RIMBP2, STXBP5L, PHOX2B, NEUROD4, and POU4F2) and 3 miRNAs (DEMs, has-miR-548ah, has-miR-888, and has-miR-196a-1), of which hsa-miR-548 regulated 4 target genes, the largest number among the 3 miRNAs. The functions of these DEMs and DEGs were verified by external experiments in the HCC cell line Hep3B2.1–7. We further investigated the adverse events of the drugs for patients with advanced HCC in clinical treatment. The patients in the sorafenib group developed less frequent symptoms of hypertension and diarrhea. Also, the frequency of hand-foot skin reactions in patients treated with lenvatinib was lower than that of patients treated with sorafenib (P<0.05). There were no significant differences in nausea, fatigue, frequent urination, and dizziness (P>0.05). In a time of increasing interest in chemotherapy drug treatments for patients with HCC, this study provided a better understanding of the clinical evaluations of sorafenib and lenvatinib.

Purpose: To develop a lipoprotein receptor-related protein 1B (LRP1B) gene mutation-based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categorized into high- and low-risk groups. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. The functions of LRP1B and risk factors in the model were verified using both in vivo and in vitro experiments. Results: It could be demonstrated that LRP1B was a potential negative predictor for HCC patients prognosis with high mutation frequency. The functions of LRP1B were verified with ELISA and Quantitative Real-time PCR method based on clinic-recruited HCC participants. Eleven genes displayed significant differences according to LRP1B status, which could better predict HCC patient prognosis. The functions of these genes were examined using HCC cell line HCCLM3, suggesting they played a pivotal role in determining HCC cell proliferation and apoptosis. From the immune cell infiltration ratio analysis, there was a significant difference in the infiltration degree of seven types of immune cells and two immune checkpoints between high- and low-risk HCC patients. Conclusion: The present study hypothesized a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for HCC, which provided a systematic reference for future understanding of clinical research.

Purpose: The aim of this study was to systematically establish a comprehensive tumour microenvironment (TME)-relevant prognostic gene and target miRNA network for breast cancer patients.Methods: Based on a large-scale screening of TME-relevant prognostic genes (760 genes) for breast cancer patients, the prognostic model was established. The primary TME prognostic genes were selected from the constructing database and verified in the testing database. The internal relationships between the potential TME prognostic genes and the prognosis of breast cancer patients were explored in depth. The associated miRNAs for the TME prognostic genes were generated, and the functions of each primary TME member were investigated in the breast cancer cell line.Results: Compared with sibling controls, breast cancer patients showed 55 differentially expressed TME prognostic genes, of which 31 were considered as protective genes, while the remaining 24 genes were considered as risk genes. According to the lambda values of the LASSO Cox analysis, the 15 potential TME prognostic genes were as follows: ENPEP, CCDC102B, FEZ1, NOS2, SCG2, RPLP2, RELB, RGS3, EMP1, PDLIM4, EPHA3, PCDH9, VIM, GFI1, and IRF1. Among these, there was a remarkable linear internal relationship for CCDC102B but non-linear relationships for others with breast cancer patient prognosis. Using the siRNA technique, we silenced the expression of each TME prognostic gene. Seven of the 15 TME prognostic genes (NOS2, SCG2, RGS3, EMP1, PDLIM4, PCDH9, and GFI1) were involved in enhancing cell proliferation, destroying cell apoptosis, promoting cell invasion, or migration in breast cancer. Six of them (CCDC102B, RPLP2, RELB, EPHA3, VIM, and IRF1) were favourable for maintaining cell invasion or migration. Only two of them (ENPEP and FEZ1) were favourable for the processes of cell proliferation and apoptosis.Conclusions: This integrated study hypothesised an innovative TME-associated genetic functional network for breast cancer patients. The external relationships between these TME prognostic genes and the disease were measured. Meanwhile, the internal molecular mechanisms were also investigated.