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

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, with limited biomarkers identified to predict its prognosis and treatment response of immune checkpoint blockade (ICB). This study aimed to explore the predictive ability of T cell marker genes score (TMGS) to predict their overall survival (OS) and treatment response to ICB by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Multi-omics data of PDAC were applied in this study. The uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification. The non-negative matrix factorization (NMF) algorithm was applied to molecular subtypes clustering. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was adopted for TMGS construction. The prognosis, biological characteristics, mutation profile, and immune function status between different groups were compared. Two molecular subtypes were identified via NMF: proliferative PDAC (C1) and immune PDAC (C2). Distinct prognoses and biological characteristics were observed between them. TMGS was developed based on 10 T cell marker genes (TMGs) through LASSO-Cox regression. TMGS is an independent prognostic factor of OS in PDAC. Enrichment analysis indicated that cell cycle and cell proliferation-related pathways are significantly enriched in the high-TMGS group. Besides, high-TMGS is related to more frequent KRAS, TP53, and CDKN2A germline mutations than the low-TMGS group. Furthermore, high-TMGS is significantly associated with attenuated antitumor immunity and reduced immune cell infiltration compared to the low-TMGS group. However, high TMGS is correlated to higher tumor mutation burden (TMB), a low expression level of inhibitory immune checkpoint molecules, and a low immune dysfunction score, thus having a higher ICB response rate. On the contrary, low TMGS is related to a favorable response rate to chemotherapeutic agents and targeted therapy. By combining scRNA-seq and bulk RNA-seq data, we identified a novel biomarker, TMGS, which has remarkable performance in predicting the prognosis and guiding the treatment pattern for patients with PDAC.

Background: The clinical and molecular factors that associated with the response to Osimertinib in the patients who have acquired resistance to the first or second-generation of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) is elusive. Objectives: The purpose of this study is to investigate the clinical relevance to the allele frequency (AF) of T790M in circulating tumor DNA (ctDNA) by targeted next generation sequencing (NGS), and to analyze the prognostic value of T790M AF in patients with advanced non-small cell lung cancer (NSCLC) receiving Osimertinib after resistance to the first-or second-generation of EGFR-TKIs. Materials and methods: We retrospectively screened 3011 advanced NSCLC patients in the First Affiliated Hospital of Xi’an Jiaotong University from November 2015 to March 2021. 586 of them experienced the first- or second-generation EGFR-TKIs resistance and had complete follow-up information, 146 of them took NGS gene test and 48 were detected EGFR T790M in ctDNA. The associations among T790M AF, clinical characteristics and objective response rate (ORR) of Osimertinib was performed by Students’ t-test or one-way ANOVA. The patients were divided into T790M-high and T790M-low groups according to a receiver operation curve (ROC) determined cut-off value. The correlation between T790M AF and ORR of Osimertinib was performed by Students’ t-test, and the association between clinical factors and ORR of Osimertinib was assessed by Pearson’s χ² test or Fisher’s exact test, when appropriate. The survival curves were calculated by the Kaplan-Meier method and analyzed by the log-rank test. Cox proportional hazards regression models were also used to evaluate the association between clinical characteristics, T790M AF and survival endpoints. Results: Age (P=0.004), EGFR active mutations (P<0.001), EGFR T790M AF (P=0.014), prior EGFR-TKIs (P=0.024) and liver metastasis before Osimertinib treatment (P=0.031) were significantly associated with progression-free survival (PFS). Multivariate analysis revealed that age (hazard ratio: 0.32, 95%CI: 0.13-0.75, P=0.009) and the AF of EGFR T790M (hazard ratio: 0.39, 95%CI: 0.17-0.90, P=0.027) were independent prognostic factors for PFS in EGFR T790M positive advanced NSCLC patients receiving Osimertinib. EGFR active mutations (P<0.001) was significantly associated with overall survival (OS). EGFR T790M AF tended to be related to OS (P=0.066). Moreover, high EGFR T790M AF (HR: 0.31, 95%CI: 0.10-0.92, P=0.035) and EGFR active mutations (HR: 2.28, 95%CI: 1.07-4.90, P=0.015) were independently predictive factors of OS in multivariate analysis. Conclusion: EGFR T790M AF in ctDNA independently predicted PFS and OS in T790M-positive advanced NSCLC who received Osimertinib after resistance to the first- or second-generation of EGFR-TKIs.

Background: Lipid metabolism plays a pivotal role in cancer progression and metastasis. This study aimed to investigate the prognostic value of lipid metabolism-related genes (LMRGs) in early-stage lung adenocarcinoma (LUAD) and develop a lipid metabolism-related gene prognostic index (LMRGPI) to predict their overall survival (OS) and treatment response. Methods: A total of 774 early-stage LUAD patients were identified from The Cancer Genome Atlas (TCGA, 403 patients) database and Gene Expression Omnibus (GEO, 371 patients) database. The non-negative Matrix Factorization (NMF) algorithm was used to identify different population subtypes based on LMRGs. The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to develop the LMRGPI, with receiver operating characteristic (ROC) curves and concordance index being used to evaluate its performance. The characteristics of mutation landscape, enriched pathways, tumor microenvironment (TME), and treatment response between different LMRGPI groups were also investigated. Results: We identified two population subtypes based on LMRGs in the TCGA-LUAD cohort, with distinct prognosis, TME, and immune status being observed. LMRGPI was developed based on the expression levels of six LMRGs, including ANGPTL4, NPAS2, SLCO1B3, ACOXL, ALOX15, and B3GALNT1. Higher LMRGPI was correlated with poor OS both in TCGA and GSE68465 cohorts. Two nomograms were established to predict the survival probability of early-stage LUAD, with higher consistencies being observed between the predicted and actual OS. Higher LMRGPI was significantly correlated with more frequent TP53 mutation, higher tumor mutation burden (TMB), and up-regulation of CD274. Besides, patients with higher LMRGPI presented unremarkable responses for gefitinib, erlotinib, cisplatin, and vinorelbine, while they tend to have a favorable response for immune checkpoint inhibitors (ICIs). The opposite results were observed in the low-LMRGPI group. Conclusions: We comprehensively investigated the prognostic value of LMRGs in early-stage LUAD. Given its good prognostic ability, LMRGPI could serve as a promising biomarker to predict the OS and treatment response of these patients.

Abstract Background Attributed to the immunosuppression caused by malignancy itself and its treatments, cancer patients are vulnerable to developing nosocomial infections. This study aimed to develop a nomogram to predict the in-hospital death risk of these patients. Methods This retrospective study was conducted at a medical center in Northwestern China. The univariate and multivariate logistic regression analyses were adopted to identify predictive factors for in-hospital mortality of nosocomial infections in cancer patients. A nomogram was developed to predict the in-hospital mortality of each patient, with receiver operating characteristic curves and calibration curves being generated to assess its predictive ability. Furthermore, decision curve analysis (DCA) was also performed to estimate the clinical utility of the nomogram. Results A total of 1,008 nosocomial infection episodes were recognized from 14,695 cancer patients. Extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (15.5%) was the most predominant causative pathogen. Besides, multidrug-resistant strains were discovered in 25.5% of cases. The multivariate analysis indicated that Eastern Cooperative Oncology Group Performance Status 3–4, mechanical ventilation, septic shock, hypoproteinemia, and length of antimicrobial treatment

Background: Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. However, it cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data. Methods: Bulk RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. LUAD scRNA-seq data were acquired from Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Weighted Gene Correlation Network Analysis (WGCNA) was utilized to identify key modules and differentially expressed genes (DEGs). The non-negative Matrix Factorization (NMF) algorithm was used to identify different subtypes based on DEGs. The Cox regression analysis was used to develop the prognostic model. The characteristics of mutation landscape, immune status, and immune checkpoint inhibitors (ICIs) related genes between different risk groups were also investigated. Results: scRNA-seq data of four samples were integrated to identify 13 clusters and 9cell types. After applying differential analysis, NK cells, bladder epithelial cells, and bronchial epithelial cells were identified as significant cell types. Overall, 329 DEGs were selected for prognostic model construction through differential analysis and WGCNA. Besides, NMF identified two clusters based on DEGs in the TCGA cohort, with distinct prognosis and immune characteristics being observed. We developed a prognostic model based on the expression levels of six DEGs. A higher risk score was significantly correlated with poor survival outcomes but was associated with a more frequent TP53 mutation rate, higher tumor mutation burden (TMB), and up-regulation of PD-L1. Two independent external validation cohorts were also adopted to verify our results, with consistent results observed in them. Conclusion: This study constructed and validated a prognostic model for LUAD by integrating 10× scRNA-seq and bulk RNA-seq data. Besides, we observed two distinct subtypes in this population, with different prognosis and immune characteristics.

Background: The prognostic nutritional index (PNI) is related to the prognosis of multiple malignancies. This study investigated whether the PNI has prognostic value in advanced non-small cell lung cancer (NSCLC) patients treated with programmed death 1 (PD-1) inhibitors. Methods: We retrospectively analyzed advanced NSCLC patients treated with PD-1 inhibitors from July 2018 to December 2019. Pretreatment PNI was calculated by peripheral lymphocyte count and serum albumin level, and the cut-off value was determined. Subsequently, we investigated the relationship between PNI and early progression, and evaluated its prognostic role on survival outcomes. Ultimately, based on the results of survival analysis, a nomogram was established. Results: A total of 123 patients were included. Of these, 24 (19.5%) patients had experienced early progression. Multivariate logistic analysis indicated that low PNI (odds ratio, 3.709, 95% confidence interval [CI], 1.354-10.161; P = 0.011) was closely correlated with early progression. Moreover, multivariate Cox regression analysis confirmed that low PNI was an independent risk factor for progression-free survival (hazard ratio [HR], 2.698, 95% CI, 1.752-4.153; P < 0.001) and overall survival (HR, 7.222, 95% CI, 4.081-12.781; P < 0.001), respectively. The prediction accuracy of nomogram based on PNI is moderate. Conclusion: PNI was an independent predictor of early progression and survival outcomes in advanced NSCLC patients treated with PD-1 inhibitors.

Background: Several studies have investigated the relationship between secreted phosphoprotein 1 (SPP1) expression level and prognosis of various tumors, but the results are far from conclusive. Therefore, we performed the present meta-analysis to investigate the prognostic value of SPP1 in pan-cancer. Furthermore, a followed confirmation based on The Cancer Genome Atlas (TCGA) database was also performed to verify our results. Methods: We performed a systematic search from PubMed, Embase, Web of Science, and Cochrane Library databases and 19 articles, including 3403 patients and 9 types of tumors, were pooled in our meta-analysis. Overall survival (OS) and disease-free survival (DFS), which correlated with SPP1 expression, were considered as the primary outcome. Subgroup analyses, sensitivity analysis, and publication bias were used to investigate heterogeneity and reliability of the results. Furthermore, we also explored the relationship between SPP1 expression and clinical parameters of tumor patients. Finally, the results were verified with TCGA database and we further explored the relationship between SPP1 expression and tumor immuno-microenvironment (TIME), DNA methylation, and enriched gene pathway. Results: Our meta-analysis showed that high-expressed SPP1 was significantly related to poor OS and DFS in various cancers, especially in liver hepatocellular carcinoma (LIHC). Furthermore, we also identified that the high expression level of SPP1 was significantly correlated with tumor grade. The expression level of SPP1 in the majority of tumor types were much higher than the corresponding normal tissues analyzed from databases. Besides, we also observed that high-expressed SPP1 was related to poor OS and DFS in LIHC, which supported the conclusion of meta-analysis. In addition, high-expressed SPP1 is related to 6 immune cells in TIME and DNA methylation regulatory genes. Ultimately, the results of Gene Set Enrichment Analysis (GSEA) suggested that tumor-related gene sets, such as hypoxia and lipid metabolism, were significantly enriched in high-expressed SPP1 group. Conclusions: SPP1 is high-expressed in various tumor tissues and correlated with poor prognosis. SPP1 might promote cancer invasion and metastasis by affecting tumor grade, TIME, DNA methylation, hypoxia, and lipid metabolism. SPP1 is expected to become a new clinical indicator for tumor detection and prognosis, and provide a new idea for tumor targeted therapy.