Shuqun Zhang

Background: Glioblastoma multiforme (GBM) is a highly aggressive brain tumor with a poor prognosis. Current treatment options are limited and often ineffective. CAR T cell therapy has shown success in treating hematologic malignancies, and there is growing interest in its potential application in solid tumors, including GBM. However, current CAR T therapy lacks clinical efficacy against GBM due to tumor-related resistance mechanisms and CAR T cell deficiencies. Therefore, there is a need to improve CAR T cell therapy efficacy in GBM. Methods: We conducted large-scale CRISPR interference (CRISPRi) screens in GBM cell line U87 MG cells co-cultured with B7-H3 targeting CAR T cells to identify genetic modifiers that can enhance CAR T cell-mediated tumor killing. Flow cytometry-based tumor killing assay and CAR T cell activation assay were performed to validate screening hits. Bioinformatic analyses on bulk and single-cell RNA sequencing data and the TCGA database were employed to elucidate the mechanism underlying enhanced CAR T efficacy upon knocking down the selected screening hits in U87 MG cells. Results: We established B7-H3 as a targetable antigen for CAR T therapy in GBM. Through large-scale CRISPRi screening, we discovered genetic modifiers in GBM cells, including ARPC4, PI4KA, ATP6V1A, UBA1, and NDUFV1, that regulated the efficacy of CAR T cell-mediated tumor killing. Furthermore, we discovered that TNFSF15 was upregulated in both ARPC4 and NDUFV1 knockdown GBM cells and revealed an immunostimulatory role of TNFSF15 in modulating tumor-CAR T interaction to enhance CAR T cell efficacy. Conclusions: Our study highlights the power of CRISPR-based genetic screening in investigating tumor-CAR T interaction and identifies potential druggable targets in tumor cells that confer resistance to CAR T cell killing. Furthermore, we devised targeted strategies that synergize with CAR T therapy against GBM. These findings shed light on the development of novel combinatorial strategies for effective immunotherapy of GBM and other solid tumors.

Purposes: To identify potent DNA methylation candidates that could predict response to temozolomide (TMZ) in glioblastomas (GBMs) that do not have glioma‐CpGs island methylator phenotype (G‐CIMP) but have an unmethylated promoter of O‐6‐methylguanine‐DNA methyltransferase (unMGMT).Methods: The discovery‐validation approach was planned incorporating a series of G‐CIMP−/unMGMT GBM cohorts with DNA methylation microarray data and clinical information, to construct multi‐CpG prediction models. Different bioinformatic and experimental analyses were performed for biological exploration.Results: By analyzing discovery sets with radiotherapy (RT) plus TMZ versus RT alone, we identified a panel of 64 TMZ efficacy‐related CpGs, from which a 10‐CpG risk signature was further constructed. Both the 64‐CpG panel and the 10‐CpG risk signature were validated showing significant correlations with overall survival of G‐CIMP−/unMGMT GBMs when treated with RT/TMZ, rather than RT alone. The 10‐CpG risk signature was further observed for aiding TMZ choice by distinguishing differential outcomes to RT/TMZ versus RT within each risk subgroup. Functional studies on GPR81, the gene harboring one of the 10 CpGs, indicated its distinct impacts on TMZ resistance in GBM cells, which may be dependent on the status of MGMT expression.Conclusions: The 64 TMZ efficacy‐related CpGs and in particular the 10‐CpG risk signature may serve as promising predictive biomarker candidates for guiding optimal usage of TMZ in G‐CIMP−/unMGMT GBMs.

Triple-negative breast cancer (TNBC) has a very poor prognosis due to the disease’s lack of established targeted treatment options. Glia maturation factor γ (GMFG), a novel ADF/cofilin superfamily protein, has been reported to be differentially expressed in tumors, but its expression level in TNBC remains unknown. The question of whether GMFG correlates with the TNBC prognosis is also unclear. In this study, data from the Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Human Protein Atlas (HPA), and Genotype-Tissue Expression (GTEx) databases were used to analyze the expression of GMFG in pan-cancer and the correlation between clinical factors. Gene Set Cancer Analysis (GSCA) and Gene Set Enrichment Analysis (GSEA) were also used to analyze the functional differences between the different expression levels and predict the downstream pathways. GMFG expression in breast cancer tissues, and its related biological functions, were further analyzed by immunohistochemistry (IHC), immunoblotting, RNAi, and function assay; we found that TNBC has a high expression of GMFG, and this higher expression was correlated with a poorer prognosis in TCGA and collected specimens of the TNBC. GMFG was also related to TNBC patients’ clinicopathological data, especially those with histological grade and axillary lymph node metastasis. In vitro, GMFG siRNA inhibited cell migration and invasion through the EMT pathway. The above data indicate that high expression of GMFG in TNBC is related to malignancy and that GMFG could be a biomarker for the detection of TNBC metastasis.

Several studies have shown that female reproductive factors are associated with breast cancer (BC), but the results differ. We conducted two-sample MR in the present work. The raw data applied in the MR study were all from the Genome-wide association study (GWAS) database. The causal effect of reproductive factors on breast cancer were mainly estimated by the standard inverse variance weighted (IVW) method. Cochran’s Q test and I² statistics were used to assess heterogeneity. The pleiotropy was evaluated by MR-Egger intercept test and MR-PRESSO. Finally, the leave-one-out analysis was performed to evaluate the robustness of the MR results. We found that there was a negative causal effect of the age at last live birth on BC (OR = 0.687, 95%CI = 0.539–0.875, p = 0.002) and positive effect of the age at menopause on BC (OR = 1.054, 95%CI = 1.034–1.075, p = 8.010 × 10⁻⁸). Additionally, there were null effects of the age at menarche (OR = 0.977, 95%CI = 0.915–1.043, p = 0.484), the age at first sexual intercourse (OR = 1.053, 95%CI = 0.958–1.157, p = 0.284) and the age at first birth (OR = 0.981, 95%CI = 0.936–1.027, p = 0.404) on BC. All these results were reliable and stable. In conclusion, the present study showed that younger age at last birth and older age at menopause could increase the risk of BC.

Paclitaxel is widely used in clinical treatment of cancer and peripheral neuropathy is a common adverse side effect of paclitaxel. Diverse mechanisms contribute to the development and maintenance of paclitaxel-induced peripheral neuropathy. However, the role of spinal notch pathway in paclitaxel-induced peripheral neuropathy is not completely understood. In this study, we established an animal model of paclitaxel-induced peripheral neuropathy using nab-paclitaxel. We found that the mechanical and thermal pain threshold of rats was decreased with treatment of nab-paclitaxel, accompanied by increased expression of Notch pathway related proteins, enhanced apoptosis, neuronal and axonal injury, and glial responses. Using DAPT, an inhibitor for notch pathway, could restore the mechanical and thermal threshold and decrease apoptosis, neuronal and axonal injury, and glial responses induced by nab-paclitaxel. DAPT relieved paclitaxel-induced peripheral neuropathy by inhibiting activation of Notch pathway related proteins, then inhibited HMGB1/TLR4 signaling pathway, reduced the expression of inflammatory factor tumor necrosis factor-α and interleukin-1β and accelerated nerve regeneration. Taking together, our study demonstrated that Notch pathway may serve as a potential target for paclitaxel-induced peripheral neuropathy intervention.

Background: Long noncoding RNAs (lncRNAs) have been discovered to play a regulatory role in genomic instability (GI), which participates in the carcinogenesis of various cancers, including hepatocellular carcinoma (HCC). We endeavored to establish a GI-derived lncRNA signature (GILncSig) as a potential biomarker and explore its impact on immune infiltration and prognostic significance.Methods: Combining expression and somatic mutation profiles from The Cancer Genome Atlas database, we identified GI-related lncRNAs and conducted functional analyses on co-expressed genes. Based on Cox regression analysis, a GILncSig was established in the training cohort (n = 187), and an independent testing patient cohort (n = 183) was used to validate its predictive ability. Kaplan-Meier method and receiver operating characteristic curves were adopted to evaluate the performance. The correlation between GI and immune infiltration status was investigated based on the CIBERSORT algorithm and single sample gene set enrichment analysis. In addition, a comprehensive nomogram integrating the GILncSig and clinicopathological variables was constructed to efficiently assess HCC patient prognosis in clinical applications.Results: A total of 88 GI-related lncRNAs were screened out and the functional analyses indicated diversified effects on HCC progression. The GILncSig was established using four independent lncRNAs (AC116351.1, ZFPM2-AS1, AC145343.1, and MIR210HG) with significant prognostic value (p < 0.05). Following evaluation with the GILncSig, low-risk patients had significantly better clinical outcomes than high-risk patients in the training cohort (p < 0.001), which was subsequently validated in the independent testing cohort. High-risk group exhibited more immunocyte infiltration including B cells memory, macrophages M0 and neutrophils and higher expression of HLA gene set and immune checkpoint genes. Compared to existing HCC signatures, the GILncSig showed better prognosis predictive performance [area under the curve (AUC) = 0.709]. Furthermore, an integrated nomogram was constructed and validated to efficiently and reliably evaluate HCC patient prognosis (3-years survival AUC = 0.710 and 5-years survival AUC = 0.707).Conclusion: The GILncSig measuring GI and impacting immune infiltration serves as a potential biomarker and independent predictor of HCC patient prognosis. Our results highlight further investigation of GI and HCC molecular mechanisms.

Introduction: Breast cancer is the most common cancer in women worldwide. The association between body mass index (BMI) and breast cancer risk has been paid more attention in the past few years, but the findings are still controversial. To obtain a more reliable conclusion, we performed a dose–response meta-analysis on 12 prospective cohort studies comprising 22,728,674 participants. Methods: Linear and nonlinear trend analyses were conducted to explore the dose–response relationship between BMI and breast cancer risk. The summary relative risk (SRR) and 95% confidence intervals (CIs) were used to evaluate the cancer risk. Results: The overall results showed a weak positive association between a 5-unit increase in BMI and breast cancer risk, indicating that a 5 kg/m² increase in BMI corresponded to a 2% increase in breast cancer risk (SRR: 1.02, 95% CI: 1.01–1.04, ppP_nonlinearity=0.754). Conclusion: In summary, this dose–response meta-analysis of prospective cohort studies showed that every 5 kg/m2 increase in BMI corresponded to a 2% increase in breast cancer risk in women. However, higher BMI could be a protective factor in breast cancer risk for premenopausal women. Further studies are necessary to verify these findings and elucidate the pathogenic mechanisms.