Ni Zhao’s research while affiliated with First Affiliated Hospital of China Medical University and other places

Colorectal cancer (CRC) is a serious global health problem. Even with improvements in CRC diagnosis and treatment, many patients are diagnosed with metastatic disease, indicating the tumor has metastasized, and the survival rate for those with advanced CRC is still low. Immune checkpoint inhibitors (ICIs) have shown some promise for certain groups of CRC patients, specifically for those with mismatch repair deficiencies or microsatellite instability, but their overall effectiveness is still limited. Novel biomarkers and treatment targets are critically needed for the improvement of the diagnosis and treatment of CRC, ultimately improving patient outcomes. MDM4 (murine double minute 4) protein is important in controlling the tumor suppressor p53. MDM4 is similar in structure to MDM2 and is known to block p53's transcriptional ability, which can contribute to tumorigenesis. MDM4 is often found at higher levels in many cancers, including CRC, and has been linked to cancer progression through mechanisms that don't involve p53. However, MDM4's role in the tumor immune microenvironment of CRC remains unclear; its role in CRC prognosis and response to immunotherapy isn't fully understood. This study explores the biological, clinical, and immunological impact of MDM4 in CRC, focusing on its potential as a marker for prognosis and treatment target. This study is the first to comprehensively link MDM4 overexpression in CRC to immune evasion through reduced infiltration of CD8+ T cells and dendritic cells, establishing its role as an independent prognostic marker and a potential immunotherapy target. We explored the role of MDM4 in CRC by combining bioinformatic analyses and laboratory experiments. We gathered data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databasesWe performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA) to identify the key biological pathways linked to MDM4 in CRC. We also explored how MDM4 expression is associated with the immune microenvironment by examining the tumor‐infiltrating lymphocytes in CRC tissues. Laboratory experiments were conducted to test the functional role of MDM4 in CRC cell lines. Our analysis showed that MDM4 expression was higher in CRC than in normal colorectal tissues, with even higher levels found in more advanced tumor stages. Increased MDM4 expression was linked to poorer progression‐free survival (PFS) in CRC patients and was identified as an independent predictor of prognosis. Through pathway enrichment analyses, we found that MDM4 was involved in important tumor‐related and immune pathways, including those regulating cell cycle progression and immune response. Notably, overexpression of MDM4 was associated with lower infiltration of CD8 T cells, natural killer (NK) cells, and dendritic cells in the tumor microenvironment, suggesting that MDM4 might help the tumor evade the immune system. In vitro experiments further confirmed these findings, showing that reducing MDM4 expression significantly slowed CRC cell growth and induced apoptosis. These results highlight the tumor‐promoting role of MDM4 in CRC and suggest its possibility of becoming a therapeutic target. MDM4 is important in the progression and immune evasion of CRC. Its increased expression is implicated in disease progression and worse clinical outcomes, making it a valuable independent prognostic marker for CRC. Furthermore, MDM4's involvement in immune regulation, particularly in decreasing immune cell infiltration, suggests its potential as a target for immunotherapy. Targeting MDM4 could provide a new CRC treatment strategy, potentially improving patient outcomes by inhibiting tumor growth and boosting immune responses. Further research is needed to confirm MDM4 as a therapeutic target and to gain a deeper understanding of its function in CRC immunotherapy.

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of cancer therapy. This study aimed to develop novel risk classifiers to predict the risk of immune-related adverse events (irAEs) and the probability of clinical benefits. Patients with cancer who received ICIs from the First Affiliated Hospital of Xi 'an Jiaotong University from November 2020 to October 2022 were recruited and followed up. Logistic regression analyses were performed to identify independent predictive factors for irAEs and clinical response. Two nomograms were developed to predict the irAEs and clinical responses of these individuals, with a receiver operating characteristic curve to assess their predictive ability. Decision curve analysis was performed to estimate the clinical utility of the nomogram. This study included 583 patients with cancer. Among them, 111 (19.0%) developed irAEs. Duration of treatment (DOT)>3 cycles, hepatic-metastases, IL2>2.225 pg/mL, and IL8>7.39 pg/mL were correlated with higher irAEs risk. A total of 347 patients were included in the final efficacy analysis, with an overall clinical benefit rate of 39.7%. DOT>3 cycles, nonhepatic-metastases, and irAEs and IL8>7.39 pg/mL were independent predictive factors of clinical benefit. Ultimately, 2 nomograms were successfully established to predict the probability of irAEs and their clinical benefits. Ultimately, 2 nomograms were successfully established to predict the probability of irAEs and clinical benefits. The receiver operating characteristic curves yielded acceptable nomogram performance. Calibration curves and decision curve analysis supported the hypothesis that nomograms could provide more significant net clinical benefits to these patients. Specific baseline plasma cytokines were closely correlated with irAEs and clinical responses in these individuals.

Background Elderly cancer patients are more predisposed to developing nosocomial infections during anti-neoplastic treatment, and are associated with a bleaker prognosis. This study aimed to develop a novel risk classifier to predict the in-hospital death risk of nosocomial infections in this population. Methods Retrospective clinical data were collected from a National Cancer Regional Center in Northwest China. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was utilized to filter the optimal variables for model development and avoid model overfitting. Logistic regression analysis was performed to identify the independent predictors of the in-hospital death risk. A nomogram was then developed to predict the in-hospital death risk of each participant. The performance of the nomogram was evaluated using receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA). Results A total of 569 elderly cancer patients were included in this study, and the estimated in-hospital mortality rate was 13.9%. The results of multivariate logistic regression analysis showed that ECOG-PS (odds ratio [OR]: 4.41, 95% confidence interval [CI]: 1.95-9.99), surgery type (OR: 0.18, 95%CI: 0.04-0.85), septic shock (OR: 5.92, 95%CI: 2.43-14.44), length of antibiotics treatment (OR: 0.21, 95%CI: 0.09-0.50), and prognostic nutritional index (PNI) (OR: 0.14, 95%CI: 0.06-0.33) were independent predictors of the in-hospital death risk of nosocomial infections in elderly cancer patients. A nomogram was then constructed to achieve personalized in-hospital death risk prediction. ROC curves yield excellent discrimination ability in the training (area under the curve [AUC]=0.882) and validation (AUC=0.825) cohorts. Additionally, the nomogram showed good calibration ability and net clinical benefit in both cohorts. Conclusion Nosocomial infections are a common and potentially fatal complication in elderly cancer patients. Clinical characteristics and infection types can vary among different age groups. The risk classifier developed in this study could accurately predict the in-hospital death risk for these patients, providing an important tool for personalized risk assessment and clinical decision-making.

Objectives: Pancreatic undifferentiated carcinoma accounts for 2%–7% of pancreatic carcinomas. We aimed to investigate the pathological and genetic characteristics of pancreatic undifferentiated carcinoma with osteoclast-like giant cells and the key points of treatment. Methods: The clinical data and follow-up results of four patients diagnosed with pancreatic undifferentiated carcinoma with osteoclast-like giant cells between May 2015 and May 2020 at the First Affiliated Hospital of Xi’an Jiaotong University were retrospectively analyzed. Results: Chief complaints included “pain and discomfort in the upper abdomen” (2/4), “nausea and vomiting” (1/4) or no symptoms (1/4). Preoperative mildly elevated tumor markers included carcinoembryonic antigen (1/4) and CA19-9 (1/4). The tumors were located in the tail of the pancreas in three patients and the head and neck in one patient. Tumor metastasis was found in pancreatic adipose tissue in two patients and lymph node metastasis in one patient, with microscopic heterogeneous mononuclear cells and scattered osteoclast-like giant cells of various sizes. One patient (1/4) had a mucinous cystic tumor of the pancreas, and two patients (2/4) had adenocarcinoma of the pancreatic duct. Only one patient received postoperative gemcitabine combined with albumin-bound paclitaxel chemotherapy. Conclusion: Currently, treatment guidelines are lacking for PUC-OGC, and prognosis varies markedly. More cases must be reported to clarify its origination. The long-term follow-up of diagnosed patients and genetic mutation testing can also contribute to improving treatment and prognosis of this disease.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of cancer. The aim of this study was to develop novel risk classifiers to predict the risk of irAEs and probability of clinical benefits of these individuals. Methods: The cancer patients received ICIs from the First Affiliated Hospital of Xi 'an Jiaotong University from November 2020 to October 2022 were collected and followed up. The logistic regression analyses were adopted to identify independent predictive factors of irAEs and clinical response. Two nomograms were developed to predict the irAEs and clinical response of these individuals, with receiver operating characteristic curve (ROC) and calibration curve being generated to assess their predictive ability. Besides, decision curve analysis (DCA) was performed to estimate the clinical utility of the nomograms. Results: This study included 583 cancer patients from 2434 cancer patients. Among them, 111 patients (19.0%) developed irAEs. The multivariate analysis indicated that duration of treatment (DOT)＞3 cycles, Hepatic-metastases, IL2＞2.225pg/ml, and IL8＞7.39pg/ml were correlated with higher irAEs risk. Overall, 347 patients were included in the final efficacy analysis, with an overall clinical benefit rate of 39.7% being observed. The multivariate analysis indicated that DOT＞3cycles, non-hepatic-metastases, irAEs and IL8＞7.39pg/ml were independent predictive factors of clinical benefit. Ultimately, two nomograms were successfully established to predict the probability of irAEs and clinical benefits. ROC curves yield acceptable performance of nomograms. Calibration curves showed satisfying consistencies between actual and predicted probability. DCA supported that the nomograms could provide more significant net clinical benefits to these patients. Conclusion: Specific baseline serum cytokines are closely correlated to irAEs and clinical response in these individuals. We established two nomograms that could effectively predict the risk of irAEs and probability of clinical response by integration of common clinicopathological parameters and serumcytokines.

[This corrects the article DOI: 10.3389/fonc.2022.923531.].

Background At present, immunotherapy has become an important treatment for lung cancer. With the widespread use of immune checkpoint inhibitors (ICIs), we must be strict with the emergence of immune related adverse events (irAEs). There are also some patients who do not respond to immunotherapy. However, there was no biomarkers to predict the safety and efficacy of immunotherapy. The selection of immunotherapy beneficiaries contributes to improving the efficacy and safety of lung cancer treatment. Method The electronic medical records of 221 lung cancer patients with complete clinical data who received immunotherapy from the First Affiliated Hospital of Xi ‘an Jiaotong University from November 2020 to October 2021 were collected and followed up. IBM SPSS Statistic 26.0 and R 4.1.2 software were used for statistical analysis and mapping. Results 1. A total of 221 lung cancer patients receiving immunotherapy were included in the study. Higher baseline levels of IL-1β (7.88 vs 16.16pg/mL, P=0.041) and IL-2 (1.28 vs 2.48pg/mL, P=0.001) were significantly associated with irAEs. Higher levels of IL-5 (2.64 vs 5.68pg/mL, P=0.013), IFN-α (1.70 vs 3.56pg/mL, P=0.004) and IFN-γ (6.14 vs 21.31pg/mL, P=0.022) after the first cycle therapy were associated with irAEs. There was no statistical significance between cytokines and irAEs after the second cycle therapy. Higher IL-5 levels in peripheral blood (9.50 vs 3.57pg/mL, P=0.032) were associated with the occurrence of irAEs after the third cycle therapy. 2.The efficacy of immunotherapy was assessed in 142 lung cancer patients. There was no statistical significance between baseline cytokine levels and clinical benefit. After the first cycle therapy, the level of serum cytokines had no statistical significance with the occurrence of immunotherapy clinical benefit. Lower serum levels of IL-10 (2.66 vs 1.26pg/mL, P=0.016) and IL-17 (8.47 vs 2.81pg/mL, P=0.015) were associated with clinical benefit after the second cycle therapy. Lower serum levels of IL-6 (10.19 vs 41.07pg/mL, P=0.013) and IL-8 (8.01 vs 17.22pg/mL, P=0.039) were associated with clinical benefit of immunotherapy after the third cycle therapy. Conclusion 1. Baseline IL-1β and IL-2 levels in peripheral blood were associated with the occurrence of irAEs in lung cancer patients. The levels of IL-5, IFN-α and IFN-γ during treatment were associated with irAEs. 2. Baseline cytokine levels in peripheral blood were not associated with immunotherapy efficacy. The levels of IL-6, IL-8, IL-10, and IL-17 levels during treatment were associated with immunotherapy efficacy.

Analyses of the composition of peripheral cytokines hold promise for providing a basis for determining the prognosis of lung cancer treated with immunotherapy. In this study, we assessed correlations between interleukins in peripheral blood and the disease prognosis in patients with lung cancer. We retrospectively collected eligible adult patients with histologically confirmed lung cancer. Patients with immune-related adverse events (AE) from immunotherapy had higher pretreatment levels of IL-2 (p=0.002), IL-17 (p=0.01), and IFN-α (p=0.02) than patients with nonimmune-related adverse events (NAE). Univariate analysis showed that changes in IL-2 (p=0.04), IL-5 (p=0.007), IFN-α (p=0.003), IFN-γ (p=0.012) and TNF-α (p=0.049) levels were significantly increased in patients with AE compared with those with NAE before the second cycle of therapy. Patients with a clinical benefit had higher levels of IL-17 before the third cycle than patients without a clinical benefit. No significant cytokine differences were observed between patients with and without a clinical benefit undergoing ICI pretreatment or in the first two cycles of therapy. Plasma cytokines are related to immune-related adverse events and clinical responses, which are potential predictive markers for anti-PD-1/PD-L1 therapy in lung cancer patients and may play an important role in selecting patients who would benefit from PD-1/PD-L1 inhibitors.

Background: As a member of the exon junction complex (EJC), RNA-binding motif protein 8A (RBM8A) plays a crucial role in the maintenance of mRNA and multiple activities of an organism. Immunotherapy has been proven to be a staple type of cancer treatment. However, the role of RBM8A and immunity across cancer types is unclear. Objective: This study aims to visualize the expression, prognosis, mutations, and coexpressed gene results of RBM8A across cancer types and to explore the link between RBM8A expression and immunity. Methods: In this study, data were collected from multiple online databases. We analyzed the data using the HPA, UALCAN Database, COSMIC, cBioPortal, Cancer Regulome tools, Kaplan-Meier Plotter, and TIMER website. Results: For the expression of RBM8A in normal tissues, higher expression of RBM8A was observed in immune-related cells than in nonimmune organs. The expression level of RBM8A was related to tumor type. Missense mutations in RBM8A were found in most tumors and affected the prognosis of carcinomas with coexpressed genes. RBM8A was strongly associated with immune-infiltrating cells and immune checkpoint inhibitors, especially in LIHC. Conclusions: RBM8A is a gene worth exploring and may be a unique immune target in the future.