Jiaxin Zhou

Background: The utilization of neoadjuvant therapy is progressively expanding in various clinical settings. However, the absence of a clinically validated biomarker to evaluate the treatment response remains a significant challenge in the field. Circulating tumor DNA (ctDNA) detection, a novel and emerging monitoring approach in the field of oncology, holds promise as a potential prognostic biomarker for patients with cancer. This meta‐analysis investigated the clinical significance of ctDNA detection as a predictive tool for cancer recurrence in patients receiving neoadjuvant treatment.Methods: A comprehensive systematic literature search was conducted using public databases to identify relevant studies that investigated the association between ctDNA detection and cancer recurrence in patients receiving neoadjuvant treatment. Hazard ratios (HRs) and their corresponding 95% confidence intervals (95% CI) were calculated to assess the relationship between cancer recurrence and relevant factors. Cancer recurrence was considered the primary outcome.Results: A total of 23 studies encompassing 1590 patients across eight different cancer types were included in the final analysis. Positive ctDNA detection was significantly associated with higher cancer recurrence, especially at post‐neoadjuvant treatment and post‐surgery time points. The risk values for the different cancer categories and geographic areas also differed significantly.Conclusion: Our comprehensive meta‐analysis revealed a significant positive correlation between ctDNA detection and a higher risk of cancer recurrence in patients receiving neoadjuvant treatment. In addition, the risk of recurrence was influenced by variations in cancer type, timing of detection, and geographic region. These findings highlight the promising clinical applicability of ctDNA as a prognostic marker and monitoring approach for patients with cancer. However, the precise mechanism is unknown and more evidence is needed for further research.

Background: Neurodegenerative diseases (NDs) have posed significant challenges to public health, and it is crucial to understand their mechanisms in order to develop effective therapeutic strategies. Recent studies have highlighted the potential role of selenium in ND pathogenesis, as it plays a vital role in maintaining cellular homeostasis and preventing oxidative damage. However, a comprehensive analysis of the association between selenium and NDs is still lacking. Method: Five public databases, namely PubMed, Web of Science, EMBASE, Cochrane and Clinical Trials, were searched in our research. Random model effects were chosen, and Higgins inconsistency analyses (I²), Cochrane’s Q test and Tau2 were calculated to evaluate the heterogeneity. Result: The association of selenium in ND patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and Huntington’s disease (HD) was studied. A statistically significant relationship was only found for AD patients (SMD = −0.41, 95% CI (−0.64, −0.17), p < 0.001), especially for erythrocytes. However, no significant relationship was observed in the analysis of the other four diseases. Conclusion: Generally, this meta-analysis indicated that AD patients are strongly associated with lower selenium concentrations compared with healthy people, which may provide a clinical reference in the future. However, more studies are urgently needed for further study and treatment of neurodegenerative diseases.

Background. To determine if the use of the Proton Pump Inhibitors (PPI) impacts the clinical efficacy of Immune Checkpoint Inhibitors (ICIs) in Non-Small Cell Lung Cancer (NSCLC), a meta-analysis was conducted. Method. Eleven studies from PubMed, EMBASE, Cochrane Library, Web of Science, and other databases up to May 2022, were selected. The pertinent clinical outcomes were assessed by applying the Progression-free survival (PFS), Overall Survival (OS), Hazard Ratio (HR), and 95 Confidence Interval (CI). Result. This study included eleven articles containing 7,893 NSCLC patients. The result indicated that PPI use was dramatically related to poor OS (HR: 1.30 [1.101.54]), and poor PFS (HR: 1.25 [1.091.42]) in case of patients treated with ICIs. With regard to the subgroup analysis, PPI use was dramatically associated with poor OS (Europe: HR=1.48 [1.26, 1.74], Worldwide: HR=1.54 [1.24, 1.91]), and poor PFS (Europe: HR=1.36 [1.18, 1.57], Worldwide: HR=1.34 [1.16, 1.55]) in patients from Europe and multi-center studies across the world, poor OS in patients with age less than or equal to 65 (HR=1.56 [1.14, 2.15]), poor PFS in patients aged more than 65 (HR=1.36 [1.18, 1.57]), poor OS for patients receiving with PD-1 (HR=1.37 [1.04, 1.79]), poor PFS for patients receiving with PD-L1 (HR=1.33 [1.19, 1.49]), and poor OS (30: HR=1.89 [1.29, 2.78], ±30: HR=1.44 [1.27, 1.64]) and poor PFS (30: HR=1.51 [1.11, 2.05], ±30: HR=1.32 [1.20, 1.45]) for patients who received PPI at 30 days before and/or after starting the ICIs treatment. Conclusion. Our meta-analysis indicated that PPI combined with ICIs in the treatment of NSCLC patients could result in poor OS and PFS. PPI use should be extremely cautious in clinical practices to avoid the impact on the efficacy of the ICIs.

The genomic variant features (mutations, deletions, structural variants, etc.) within gastric cancer impact its evolution and immunogenicity. The tumor has developed several coping strategies to respond to these changes by DNA repair and replication (DRR). However, the intrinsic relationship between the associated DRR-related genes and gastric cancer progression remained unknown. This study selected DRR-related genes with tumor mutation burden based on the TCGA (The Cancer Genome Atlas) database of gastric cancer transcriptome and mutation data. The prognosis model of seven genes (LAMA2, CREB3L3, SELP, ABCC9, CYP1B1, CDH2, and GAMT) was constructed by a univariate and LASSO regression analysis and divided into high-risk and low-risk groups with the median risk score. Survival analysis showed that overall survival (OS) was lower in the high-risk group than that in the low-risk group. Moreover, patients with gastric cancer in the high-risk group have worse survival in different subgroups, including age, gender, histological grade, and TNM stage. The nomogram that included risk scores for DRR-related genes could accurately foresee OS of patients with gastric cancer. Interestingly, the tumor mutation burden score was higher in the low-risk group than that in the high-risk group, and the risk score for DRR-related genes was negatively correlated with tumor mutation burden in gastric cancer. Next, we further combined the risk score and tumor mutation burden to evaluate the prognosis of gastric cancer patients. The low-risk cohort had a better prognosis than the high-risk cohort in the high tumor mutation burden subgroup. The number of mutation types in the high-risk group was lower than that in the low-risk group. In the immune microenvironment of gastric cancer, more naïve B cells, memory resting CD4+ T cells, Treg cells, monocytes cells, and resting mast cells were infiltrated in the high-risk group. At last, PD-L1 and IAP expressions were negatively correlated with the risk scores; patients with gastric cancer in the low-risk group showed better immunotherapy outcomes than those in the high-risk group. Overall, the DRR-related gene signature based on tumor mutation burden is a novel biomarker for prognostic and immunotherapy response in patients with gastric cancer.

Background. Gastric cancer (GC) is the third leading cause of cancer death worldwide with complicated molecular and cellular heterogeneity. Iron metabolism and ferroptosis play crucial roles in the pathogenesis of GC. However, the prognostic role and immunotherapy biomarker potential of ferroptosis-related genes (FRGs) in GC still remains to be clarified. Methods. We comprehensively analyzed the prognosis of different expression FRGs, based on gastric carcinoma patients in the TCGA cohort. The functional enrichment and immune microenvironment associated with these genes in gastric cancer were investigated. The prognostic model was constructed to clarify the relation between FRGs and the prognosis of GC. Meanwhile, the ceRNA network of FRGs in the prognostic model was performed to explore the regulatory mechanisms. Results. Gastric carcinoma patients were classified into the A, B, and C FRGClusters with different features based on 19 prognostic ferroptosis-related differentially expressed genes in the TCGA database. To quantify the FRG characteristics of individual patients, FRGScore was constructed. And the research shows the GC patients with higher FRGScore had worse survival outcome. Moreover, thirteen prognostic ferroptosis-related differentially expressed genes (DEGs) were selected to construct a prognostic model for GC survival outcome with a superior accuracy in this research. And we also found that FRG RiskScore can be an independent biomarker for the prognosis of GC patients. Interestingly, GC patients with lower RiskScore had less immune dysfunction and were more likely to respond to immunotherapy according to TIDE value analysis. Finally, a ceRNA network based on FRGs in the prognostic model was analyzed to show the concrete regulation mechanisms. Conclusions. The ferroptosis-related gene risk signature has a superior potent in predicting GC prognosis and acts as the biomarkers for immunotherapy, which may provide a reference in clinic.

Background. Colorectal cancer (CRC) is the third most common malignancies worldwide. Ferroptosis is a programmed, iron-dependent cell death observed in cancer cells. However, the prognostic potential and immunotherapy biomarker potential of ferroptosis-related genes (FRGs) in CRC patients remains to be clarified. Methods. At first, we comprehensively analysed the different expression and prognosis of related FRGs in CRC patients based on TCGA cohort. The relationship between functional enrichment of these genes and immune microenvironment in CRC was investigated using the TCGA database. Prognostic model was constructed to determine the association between FRGs and the prognosis of CRC. Relative verification was done based on the GEO database. Meanwhile, the ceRNA network of FRGs in the model was also performed to explore the regulatory mechanisms. Results. Eight differentially expressed FRGs were associated with the prognosis of CRC patients. Patients from the TCGA database were classified into the A, B, and C FRG clusters with different features. And FRG scores were constructed to quantify the FRG pattern of individual patients with colorectal cancer. The CRC patients with higher FRG score showed worse survival outcomes, higher immune dysfunction, and lower response to immunotherapy. The prognostic model showed a high accuracy for predicting the OS of CRC. Finally, a ceRNA network was analysed to show the concrete regulation mechanisms of critical FRGs in CRC. Conclusions. The FRG risk score prognostic model based on 8 FRGs exhibit superior predictive performance, providing a novel prognostic model with a high accuracy for CRC patients. Moreover, FRG score can be the potential biomarker of the response of immunotherapy for CRC.