Zhixian Liu

Background: Brigatinib is approved in multiple countries for the treatment of patients with anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC). Despite its superior efficacy, the dermal toxicities caused by brigatinib cannot be overlooked. However, its underlying mechanism remains unknown.Methods: The effects of brigatinib on the proliferation ability of human immortalized keratinocyte (HaCaT) cells were evaluated using Cell Counting Kit-8 (CCK-8) proliferation, colony formation, and 5-ethynyl-2′-deoxyuridine (EdU) incorporation assays. The effects of brigatinib on apoptosis were detected using Annexin FITC/PI and Acridine Orange (AO) staining assays. Cell cycle was assessed with flow cytometry. An analysis of transcriptome by RNA sequencing procedures (RNA-seq) was performed to reveal the key regulatory genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to find out the biological function and related signal pathways. The expressions of amphiregulin, epiregulin and transforming growth factor alpha (TGFA) and the protein levels of Phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) and Cleaved-Caspase three were measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot assay.Results: Brigatinib inhibits cell proliferation with an IC₅₀ value of 2.9 μmol/L and significantly increases apoptosis rates. Transcriptome sequencing (RNA-seq) indicates that brigatinib could significantly downregulate the expression of amphiregulin, epiregulin and TGFA. In addition, we demonstrated that brigatinib reduced the protein expression of amphiregulin, epiregulin, TGFA, PI3K, AKT and phosphorylated AKT (p-AKT).Conclusion: This study confirms the inhibition of HaCaT cells growth and progression by brigatinib and highlights the potential value of the PI3K/AKT pathway as a therapeutic target for brigatinib-induced dermal toxicities.

Background: The tyrosine receptor kinase inhibitor (TRKi) entrectinib is used to treat neurotrophic tyrosine receptor kinase (NTRK) fusion-positive solid tumors and ROS1-positive patients. Despite its impressive efficacy against cancer, the clinical application is still limited by the central nervous system (CNS)-related toxicities. However, the precise mechanism of such CNS-related toxicities remains elusive.Methods: The effect of entrectinib-induced nerve cell damage was evaluated by the nerve cells (PC12, HT22 and SK-N-SH) based in vitro models. Various assays, including CCK-8, colony formation and EdU incorporation assays were utilized to estimate the cellular viability and proliferation ability. Cell apoptosis was measured by flow cytometry. Next, transcriptome sequencing technology was performed to identify differentially expressed genes (DEGs). Gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) analysis and gene set enrichment analysis (GSEA) were applied to predict the potential functions of DEGs. Quantitative real time polymerase chain reaction (qRT-PCR) and Western blotting assays were performed to measure the expressions of thrombospondin-1 (THBS1), TGF-β1, PI3K, AKT and phosphorylated AKT (p-AKT) in the entrectinib-treated nerve cells. Additionally, we Preliminary observed and validated whether THBS1 overexpression could rescue nerve cell damage and the abnormalities in PI3K-AKT and TGF-β signaling pathways.Results: Entrectinib significantly inhibited the nerve cells proliferation and colony formation, and induced nerve cells apoptosis. Transcriptome sequencing analysis and qRT-PCR revealed that THBS1 was downregulated within entrectinib treatment. KEGG and GSEA analysis also suggested that entrectinib directly caused the abnormalities in proliferation-related signaling pathway like PI3K-AKT pathway, and apoptosis-related signaling pathway including TGF-β pathway. We further demonstrated that THBS1, TGF-β1, PI3K, AKT and p-AKT were downregulated by entrectinib. Meanwhile, pretreatment with THBS1 overexpression plasmids significantly rescued nerve cells (PC12, HT22 and SK-N-SH) from cell death and the abnormalities in PI3K-AKT and TGF-β signaling pathways.Conclusion: These results identified a critical role of entrectinib in promoting nerve cell damage by downregulating the expression of THBS1 while also inhibiting PI3K-AKT and TGF-β signaling pathways. Our findings will provide potential therapeutic targets for CNS-related toxicities.

Existing algorithms for assessing tumor purity are limited to a single omics data, such as gene expression, somatic copy number variations, somatic mutations, and DNA methylation. Here we proposed the machine learning Multi-omics Tumor Purity prediction (MoTP) algorithm to estimate tumor purity based on multiple types of omics data. MoTP utilizes the Bayesian Regularized Neural Networks as the prediction algorithm, and Consensus Tumor Purity Estimates as labels. We trained MoTP using multi-omics data (mRNA, microRNA, long non-coding RNA, and DNA methylation) across 21 TCGA solid cancer types. By testing MoTP in TCGA validation sets, TCGA test sets, and eight datasets outside the TCGA cancer cohorts, we showed that although MoTP could achieve excellent performance in predicting tumor purity based on a single omics data type, the integration of multiple single omics data-based predictions can enhance the prediction performance. Moreover, we demonstrated the robustness of MoTP by testing it in datasets with Gaussian noise and feature missing. Benchmark analysis showed that MoTP outperformed most established tumor purity prediction algorithms, and that it required less running time and computational resource to fulfill the predictive task. Thus, MoTP would be an attractive option for computational tumor purity inference.

Background: Immune checkpoint inhibitor (ICI) plus chemotherapy is effective in advanced gastric or gastroesophageal junction (G/GEJ) cancer. This study aims to evaluate the clinical effect of first-line immunotherapy in combination with chemotherapy for advanced G/GEJ cancer. Methods: PubMed, Web of Science, Embase and Cochrane databases were systematically searched from the inception of the databases to December 2021. Randomized trials comparing ICI plus chemotherapy with chemotherapy in first-line treatment for advanced G/GEJ cancer were included. The outcomes were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs). Analyses were performed in Stata 14.0 software. The study protocol was registered with PROSPERO, number CRD42022300907. Results: Five trials were included for analysis, involving 2, 814 patients. ICI plus chemotherapy can significantly improve OS (hazards ratio [HR], 0.86; 95% CI 0.78-0.94; P = .002), PFS (HR, 0.79; 95% CI 0.63-0.99; P ＜ .001) and ORR (relative ratio [RR], 1.20; 95% CI 1.11-1.30; P < .001). In safety analyses, there were no significant differences in incidence of all AEs, treatment-related adverse event (TRAE), TRAE of grade 3 or higher, serious TRAE and TRAE leading to death between two arms ( P > .05). Conclusions: ICI plus chemotherapy is more effective first-line treatment for advanced G/GEJ cancer in contrast to chemotherapy regrading to improving OS, PFS and ORR, without increasing TRAE risk. This study will redefine the role of ICI in combination with chemotherapy in the first-line setting for G/GEJ cancer, and provide reference for clinical treatment.

Purpose. Catechol‐O‐methyltransferase (COMT) participates in the regulation of dopaminergic and adrenergic neurotransmission. COMT Val158Met polymorphism influences the efficacy and safety of opioids, but its association with oxycodone treatment in patients with cancer pain is yet to be elucidated. Hence, this study aimed to investigate the influence of COMT Val158Met polymorphism on oxycodone requirements, drug adverse effects, and pain sensitivity in patients with cancer. Methods. Patients with moderate to severe cancer pain treated with oxycodone were enrolled, of which 101 patients completed the study. All patients were genotyped for COMT Val158Met polymorphism using DNA from blood samples and were categorized into the wild‐type group (n = 50) comprising individuals with the Val/Val genotype and the mutant group (n = 51) encompassing those with the Val/Met or Met/Met genotype. Numerical rating scale (NRS) scores, oxycodone requirements, and the incidence of oxycodone‐related adverse drug reactions were compared between the two groups. Results. Patients in the mutant group exhibited higher NRS scores (6.18 ± 1.40) before the oxycodone treatment than those in the wild‐type (5.48 ± 1.54) group (P = 0.017). Patients in the wild‐type group required more oxycodone (96.00 ± 146.19 mg/24 h) than those in the mutant (77.25 ± 83.91 mg/24 h) group (P = 0.0365). The incidence rates of dysuria (2.0% vs. 16.0%, P = 0.016) and fatigue (0.0% vs. 12.0%, P = 0.013) were significantly lower in the mutant group than those in the wild‐type group. Moreover, patients with at least one Met allele showed a lower risk of suffering from oxycodone‐related side effects than those with the wild homozygote (41.2% vs. 68.0%, P = 0.007). Conclusion. Genetic variations in the COMT Val158Met gene may contribute to variability in the efficacy and safety of oxycodone in cancer pain treatment. The findings from this study emphasize the potential of pharmacogenetics in personalizing pain management. Furthermore, oxycodone therapeutic strategies can be designed based on genetic polymorphisms.

Splicing abnormality resulting from somatic mutations in key splicing factor genes (SFG) has been detected in various cancers. Hence, an in-depth study of splicing factor genes mutations’ impact on pan-cancer is meaningful. This study investigated associations of splicing factor genes mutations with clinical features, tumor progression phenotypes, genomic integrity, anti-tumor immune responses, and immunotherapy response in 12 common cancer types from the TCGA database. Compared to SFG-wildtype cancers, SFG-mutated cancers displayed worse survival prognosis, higher tumor mutation burden and aneuploidy levels, higher expression of immunosuppressive signatures, and higher levels of tumor stemness, proliferation potential, and intratumor heterogeneity (ITH). However, splicing factor genes-mutated cancers showed higher response rates to immune checkpoint inhibitors than splicing factor genes-wildtype cancers in six cancer cohorts. Single-cell data analysis confirmed that splicing factor genes mutations were associated with increased tumor stemness, proliferation capacity, PD-L1 expression, intratumor heterogeneity, and aneuploidy levels. Our data suggest that the mutation in key splicing factor genes correlates with unfavorable clinical outcomes and disease progression, genomic instability, anti-tumor immunosuppression, and increased immunotherapy response in pan-cancer. Thus, the splicing factor genes mutation is an adverse prognostic factor and a positive marker for immunotherapy response in cancer.

We propose PreCanCell, a novel algorithm for predicting malignant and non-malignant cells from single-cell transcriptomes. PreCanCell first identifies the differentially expressed genes (DEGs) between malignant and non-malignant cells commonly in five common cancer types-associated single-cell transcriptome datasets. The five common cancer types include renal cell carcinoma (RCC), head and neck squamous cell carcinoma (HNSCC), melanoma, lung adenocarcinoma (LUAD), and breast cancer (BC). With each of the five datasets as the training set and the DEGs as the features, a single cell is classified as malignant or non-malignant by k-NN (k = 5). Finally, the single cell is determined as malignant or non-malignant by the majority vote of the five k-NN classification results. We tested the predictive performance of PreCanCell in 19 single-cell datasets, and reported classification accuracy, sensitivity, specificity, balanced accuracy (the average of sensitivity and specificity) and the area under the receiver operating characteristic curve (AUROC). In all these datasets, PreCanCell achieved above 0.8 accuracy, sensitivity, specificity, balanced accuracy and AUROC. Finally, we compared the predictive performance of PreCanCell with that of seven other algorithms, including CHETAH, SciBet, SCINA, scmap-cell, scmap-cluster, SingleR, and ikarus. Compared to these algorithms, PreCanCell displays the advantages of higher accuracy and simpler implementation. We have developed an R package for the PreCanCell algorithm, which is available at https://github.com/WangX-Lab/PreCanCell.

Background: Although several key molecules have been identified to modulate SARS-CoV-2 invasion of human host cells, the molecules correlated with outcomes in COVID-19 caused by SARS-CoV-2 infection remain insufficiently explored.Methods: This study analyzed three RNA-Seq gene expression profiling datasets for COVID-19 and identified differentially expressed genes (DEGs) between COVID-19 patients and normal people, commonly in the three datasets. Furthermore, this study explored the correlation between the expression of these genes and clinical features in COVID-19 patients.Results: This analysis identified 13 genes significantly upregulated in COVID-19 patients’ leukocyte and SARS-CoV-2-infected nasopharyngeal tissue compared to normal tissue. These genes included OAS1, OAS2, OAS3, OASL, HERC6, SERPING1, IFI6, IFI44, IFI44L, CMPK2, RSAD2, EPSTI1, and CXCL10, all of which are involved in antiviral immune regulation. We found that these genes’ downregulation was associated with worse clinical outcomes in COVID-19 patients, such as intensive care unit (ICU) admission, mechanical ventilatory support (MVS) requirement, elevated D-dimer levels, and increased viral loads. Furthermore, this analysis identified two COVID-19 clusters based on the expression profiles of the 13 genes, termed COV-C1 and COV-C2. Compared with COV-C1, COV-C2 more highly expressed the 13 genes, had stronger antiviral immune responses, were younger, and displayed more favorable clinical outcomes.Conclusions: A strong antiviral immune response is essential in reducing severity of COVID-19.

The roles of the microbiome in human beings have become clearer with the development of next-generation sequencing techniques. Several pieces of evidence showed strong correlations between the microbiome and human health and disease, such as metabolic disorders, infectious diseases, digestive system diseases, and cancers. Among these diverse microbiomes, the role of bacteria in human cancers, especially in cancer cells, has received extensive attention. Latest studies found that bacteria widely existed in cancers, mainly in cancer cells and immune cells. In this review, we summarize the latest advances in understanding the role of bacteria in human cancer cells. We also discuss how bacteria are transported into cancer cells and their physiological significance in cancer progression. Finally, we present the prospect of bacterial therapy in cancer treatment.

• The use of vaccines can significantly reduce COVID-19 deaths and mortality. • The use of vaccines cannot reduce the risk of COVID-19 infection. • The SARS-CoV-2 Delta variant may weaken the effect of vaccines on lessening the severity of COVID-19. • Vaccine usage remains effective in reducing the severity of COVID-19 caused by the SARS-CoV-2 Delta variant infection.