Hezi Zhang’s research while affiliated with Shenzhen China Star Optoelectronics Technology Co., Ltd and other places

Colorectal cancer (CRC) is a common malignant cancer. Epoxide hydrolases (EHs) are involved in the development of cancer by regulating epoxides, but their relationship with CRC is unclear. We used multiple datasets to confirm the expression of different EPHX family members in CRC tissues, and to explore their association with different clinicopathologic characteristics. The Kaplan–Meier method, correlation analysis and random forest algorithm were used to evaluate the prognostic value of EPHX family members for CRC. Finally, the cell experiment verified function of EPHX4 in CRC. The expressions of EPHX1 and EPHX2 were significantly decreased, while those of EPHX3 and EPHX4 were significantly increased in CRC. The expressions of EPHX family members were correlated with some clinicopathologic features and overall survival. The expressions of the EPHX family were positively associated with CD274, CTLA4, HAVCR2, and TIGIT. EPHX2 and EPHX4 were diagnostic and predictive biomarkers for CRC. EPHX4 promoted the malignant phenotype of CRC cells. Our study firstly elucidated the prognostic significance of EPHX family members in CRC and identified novel diagnostic and prognostic biomarkers for CRC.

Background Cellular senescence (CS) is believed to be a major factor in the evolution of cancer. However, CS-related lncRNAs (CSRLs) involved in colon cancer regulation are not fully understood. Our goal was to create a novel CSRLs prognostic model for predicting prognosis and immunotherapy and exploring its potential molecular function in colon cancer. Methods The mRNA sequencing data and relevant clinical information of GDC TCGA Colon Cancer (TCGA-COAD) were obtained from UCSC Xena platform, and CS-associated genes was acquired from the CellAge website. Pearson correlation analysis was used to identify CSRLs. Then we used Kaplan–Meier survival curve analysis and univariate Cox analysis to acquire prognostic CSRL. Next, we created a CSRLs prognostic model using LASSO and multivariate Cox analysis, and evaluated its prognostic power by Kaplan–Meier and ROC curve analysis. Besides, we explored the difference in tumor microenvironment, somatic mutation, immunotherapy, and drug sensitivity between high-risk and low-risk groups. Finally, we verified the functions of MYOSLID in cell experiments. Results Three CSRLs (AC025165.1, LINC02257 and MYOSLID) were identified as prognostic CSRLs. The prognostic model exhibited a powerful predictive ability for overall survival and clinicopathological features in colon cancer. Moreover, there was a significant difference in the proportion of immune cells and the expression of immunosuppressive point biomarkers between the different groups. The high-risk group benefited from the chemotherapy drugs, such as Teniposide and Mitoxantrone. Finally, cell proliferation and CS were suppressed after MYOSLID knockdown. Conclusion CSRLs are promising biomarkers to forecast survival and therapeutic responses in colon cancer patients. Furthermore, MYOSLID, one of 3-CSRLs in the prognostic model, could dramatically regulate the proliferation and CS of colon cancer.

Purpose: The intratumoral microorganisms participates in the progression and immunotherapy of colorectal cancer (CRC). However, due to technical limitations, the impact of microorganisms on CRC has not been fully understood. Therefore, we conducted a systematic analysis of relationship between bacterial lipopolysaccharide (LPS)-associated genes and immune cells to explore new biomarkers for predicting the prognosis of CRC. Methods: The single-cell RNA sequencing data and the Comparative Toxicogenomics Database were used to screen T cells-associated LPS-related genes (TALRGs). Then, we established and validated the TALRGs risk signature in The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort and GSE39582 cohort. Besides, we compared the differences in tumor-infiltrating immune cell types, immunotherapeutic response, somatic mutation profiles, and tumor mutation burden (TMB) between high-risk group and low-risk group. In addition, the immunotherapeutic cohort (Imvigor210) treated with an anti-PD-L1 agent was performed to explore the potential value of the TALRGs signature on immunotherapy. Results: Five prognostic TALRGs were identified and selected to build the prognostic model. The high-risk group had poor prognosis in both TCGA-COAD cohort (P < 0.0001) and GSE39582 cohort (P = 0.00019). The areas under the curves (AUCs) of TALRGs signature were calculated (TCGA-COAD cohort: 0.624 at 1 years, 0.639 at 3 years, 0.648 at 5 years; anti-PD-L1 cohort was 0.59). The high-risk group had advanced pathological stages and higher TMN stages in both TCGA-COAD cohort and GSE39582 cohort. The high-risk group had the higher infiltration of immunosuppressive cells, the expressions of immune checkpoint molecules, the IC50 values of chemotherapy drugs, and TP53 mutation rate (P < 0.05). In addition, patients with high TMB had worse prognosis (P < 0.05). Furthermore, the Imvigor210 also showed patients with high-risk scores had poor prognosis (platinum-treated cohort: P = 0.0032; non-platinum-treated cohort: P = 0.00017). Conclusions: Microorganisms are closely related to the tumor microenvironment to influence the progression and immune response of CRC via stimulating T cells through LPS-related genes. The TALRGs signature contributed to predict the prognosis and immunotherapy of CRC, and became new therapeutic targets and biomarkers of CRC.

Background Currently, there are few studies on immune-related prognostic analysis of hepatocellular carcinoma (HCC). Our aim was to establish an immune-correlated prognostic model for HCC. Methods Immune-associated cells were obtained from the scRNA-seq dataset (GSE149614) of HCC. Differentially expressed genes between normal and tumor cells from immune-associated cells and the immune-related genes from the ImmPort database were used to identify immune-related differentially expressed genes (IRDEGs). Subsequently, the risk model was established in the TCGA-LIHC cohort (n = 438) from the Cancer Genome Atlas (TCGA) database by using Kaplan-Meier (K-M) survival curve, univariate/multivariate Cox regression analysis. Subsequently, we further analyzed tumor immune microenvironment characteristics, somatic mutation, immune checkpoint and its ligand expression levels between high- and low-risk groups, as well as drug sensitivity prediction. ICGC cohort was set as the validation cohort. TCGA-LIHC cohort and three independent the Gene Expression Omnibus (GEO) datasets (GSE54236, GSE14520, and GSE64041) was used to verify IRDEGs expression, as well as PCR assays using clinical samples. Results The IRDEGs was composed of 4 genes, namely B2M, SPP1, PPIA, and HRG. The 438 HCC patients were divided into high- and low-risk group. The high-risk group was associated with poor prognosis, including higher T stage, advanced pathological stages, less immune cell infiltration, higher TP53 mutation rate, the high expression of CTLA4 and HAVCR2. Besides, high-risk populations benefit from most chemotherapy drugs. Similarly, the performance of the risk model was validated in the ICGC. All four datasets (TCGA-LIHC cohort, GSE54236, GSE14520, and GSE64041) and clinical q-PCR results demonstrated that, compared with normal samples, the expressions of B2M and HRG were lower in tumor samples, and the expression of SPP1 was higher. Conclusion In summary, the immune-related prognostic signature had a good predictive performance on prognosis and immunotherapy for HCC patients.

Objective Pseudomonas aeruginosa has strong drug resistance and can tolerate a variety of antibiotics, which is a major problem in the management of antibiotic-resistant infections. Direct prediction of multi-drug resistance (MDR) resistance phenotypes of P. aeruginosa isolates and clinical samples by genotype is helpful for timely antibiotic treatment. Methods In the study, whole genome sequencing (WGS) data of 494 P. aeruginosa isolates were used to screen key anti-microbial resistance (AMR)-associated genes related to imipenem (IPM), meropenem (MEM), piperacillin/tazobactam (TZP), and levofloxacin (LVFX) resistance in P. aeruginosa by comparing genes with copy number differences between resistance and sensitive strains. Subsequently, for the direct prediction of the resistance of P. aeruginosa to four antibiotics by the AMR-associated features screened, we collected 74 P. aeruginosa positive sputum samples to sequence by metagenomics next-generation sequencing (mNGS), of which 1 sample with low quality was eliminated. Then, we constructed the resistance prediction model. Results We identified 93, 88, 80, 140 AMR-associated features for IPM, MEM, TZP, and LVFX resistance in P. aeruginosa. The relative abundance of AMR-associated genes was obtained by matching mNGS and WGS data. The top 20 features with importance degree for IPM, MEM, TZP, and LVFX resistance were used to model, respectively. Then, we used the random forest algorithm to construct resistance prediction models of P. aeruginosa, in which the areas under the curves of the IPM, MEM, TZP, and LVFX resistance prediction models were all greater than 0.8, suggesting these resistance prediction models had good performance. Conclusion In summary, mNGS can predict the resistance of P. aeruginosa by directly detecting AMR-associated genes, which provides a reference for rapid clinical detection of drug resistance of pathogenic bacteria.

Obstructive sleep apnea (OSA) is a common syndrome characterized by upper airway dysfunction during sleep. Continuous positive airway pressure (CPAP) is the most frequently utilized non-surgical treatment for OSA. Ferroptosis play a crucial role in the physiological diseases caused by chronic intermittent hypoxia, but its involvement in the development of OSA and the exact mechanisms have incompletely elucidated. GSE75097 microarray dataset was used to identify differentially expressed genes between OSA patients and CPAP-treated OSA patients. Subsequently, Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING database, and FerrDb database were conducted to analyze the biological functions of differentially expressed genes and screen ferroptosis-related genes. Finally, GSE135917 dataset employed for validation. There were 1,540 differentially expressed genes between OSA patients and CPAP-treated OSA patients. These differentially expressed genes were significantly enriched in the regulation of interleukin-1-mediated signaling pathway and ferroptosis-related signaling pathway. Subsequently, 13 ferroptosis-related genes (DRD5, TSC22D3, TFAP2A, STMN1, DDIT3, MYCN, ELAVL1, JUN, DUSP1, MIB1, PSAT1, LCE2C, and MIR27A) were identified from the interaction between differentially expressed genes and FerrDb database, which are regarded as the potential targets of CPAP-treated OSA. These ferroptosis-related genes were mainly involved in cell proliferation and apoptosis and MAPK signaling pathway. Furthermore, DRD5 and TFAP2A were downregulated in OSA patients, which showed good diagnostic properties for OSA, but these abnormal signatures are not reversed with short-term effective CPAP therapy. In summary, the identification of 13 ferroptosis-related genes as potential targets for the CPAP treatment of OSA provides valuable insights into the development of novel, reliable, and accurate therapeutic options.

Background: In this study, we compared the prognosis, tumor immune microenvironment (TIM), and drug treatment response between left-sided (LCC) and right-sided (RCC) colon cancer to predict outcomes in patients with LCC and RCC. Methods: Based on identified differentially expressed genes and using single-cell RNA sequencing data, we constructed and validated a prognostic model for LCC and RCC patients in the TCGA-COAD cohort and GSE103479 cohort. Moreover, we compared the differences of TIM characteristics and drug treatment response between LCC and RCC patients. Results: We constructed and validated a five-gene prognostic model for LCC patients and a four-gene prognostic model for RCC patients, and both showed excellent performance. The RCC patients with higher risk scores were significantly associated with greater metastasis (P = 2.6×10-5), N stage (P = 0.012), advanced pathological stage (P = 1.4×10-4), and more stable microsatellite status (P = 0.007) but not T stage (P = 0.200). For LCC patients, the risk scores were not significantly associated with tumor stage and microsatellite status (P > 0.05). Additionally, immune infiltration by CD8 and regulatory T cells and M0, M1, and M2 macrophages differed significantly between LCC and RCC patients (P < 0.05). APC and TP53 mutations were significantly more common in LCC patients (P < 0.05). In contrast, KRAS, SYNE1, and MUC16 mutations were significantly more common in RCC patients (P < 0.05). In addition, tumor mutation burden values were significantly higher in RCC patients than in LCC patients (P = 5.9×10-8). Moreover, the expression of immune checkpoint targets was significantly higher in RCC patients than in LCC patients (P < 0.05), indicating that RCC patients maybe more sensitive to immunotherapy. However, LCC and RCC patients did not differ significantly in their sensitivity to eight selected chemicals or target drugs (P > 0.05). The average half-maximal inhibitory concentrations for camptothecin, teniposide, vinorelbine, and mitoxantrone were significantly lower in low-risk than in high-risk RCC patients (P < 0.05), indicating that the lower risk score of RCC patients, the more sensitive they were to these four drugs. Conclusions: We investigated the differences in prognosis, TIM, and drug treatment response between LCC and RCC patients, which may contribute to accurate colon cancer prognosis and treatment of colon cancer.