OBJECTIVE: Immune checkpoints have emerged as promising therapeutic targets for autoimmune diseases. However, the specific roles of immune checkpoints in the pathophysiology of ankylosing spondylitis (AS) remain unclear.
METHODS: Hip ligament samples were obtained from two patient groups: those with AS and femoral head deformity, and those with femoral head necrosis but without AS, undergoing hip arthroplasty. Label-Free Quantification (LFQ) Protein Park Analysis was used to identify the protein composition of the ligaments. Peripheral blood samples of 104 AS patients from public database were used to validate the expression of key proteins. KEGG, GO, and GSVA were employed to explore potential pathways regulated by immune checkpoints in AS progression. xCell was used to calculate cell infiltration levels, LASSO regression was applied to select key cells, and the correlation between immune checkpoints and immune cells was analyzed. Drug sensitivity analysis was conducted to identify potential therapeutic drugs targeting immune checkpoints in AS. The expression of key genes was validated through immunohistochemistry (IHC).
RESULTS: HLA-DMB and HLA-DPA1 were downregulated in the ligaments of AS and this has been validated through peripheral blood datasets and IHC. Significant differences in expression were observed in CD8 + Tcm, CD8 + T cells, CD8 + Tem, osteoblasts, Th1 cells, and CD8 + naive T cells in AS. The infiltration levels of CD8 + Tcm and CD8 + naive T cells were significantly positively correlated with the expression levels of HLA-DMB and HLA-DPA1. Immune cell selection using LASSO regression showed good predictive ability for AS, with AUC values of 0.98, 0.81, and 0.75 for the three prediction models, respectively. Furthermore, this study found that HLA-DMB and HLA-DPA1 are involved in Th17 cell differentiation, and both Th17 cell differentiation and the NF-kappa B signaling pathway are activated in the AS group. Drug sensitivity analysis showed that AS patients are more sensitive to drugs such as doramapimod and GSK269962A.
CONCLUSIONS: Immune checkpoints and immune cells could serve as avenues for exploring diagnostic and therapeutic strategies for AS.

Cuproptosis-related genes (CRGs) are important for tumor development. However, the functions of CRGs across cancers remain obscure. We performed a pan-cancer investigation to reveal the roles of CRGs across cancers. In an analysis of 26 cancers, 12 CRGs were differentially expressed, and those CRGs were found to have prognostic value across different cancer types. The expression of CRGs exhibited varied among tumors of 6 immune subtypes and were significantly correlated with the 16 sensitivities of drugs. The expression of CRGs were highly correlated with immunological subtype and tumor microenvironment (TME) of prostate cancer. We also established CRGs-related prognostic signatures that closely correlated with prognosis and drug sensitivity of prostate cancer patients. Single-cell RNA-seq revealed that several CRGs were enriched in the cancer cells. Finally, an in vitro experiment showed that elesclomol, a cuproptosis inducer, targets ferredoxin 1 and suppress cell viability in prostate cancer cells. In conclusion, we carried out a comprehensive investigation for determining CRGs in differential expression, prognosis, immunological subtype, TME, and cancer treatment sensitivity across 26 malignancies; and validated the results in prostate cancer. Our research improves pan-cancer knowledge of CRGs and identifies more effective immunotherapy strategies.

BACKGROUND: Cervical cancer (CC) is a serious global disease with poor prognoses and a significant recurrence rate in patients with advanced disease. Oxidative stress (OS) greatly influences many types of human cancers, making it crucial to understand the functional mechanisms of OS-related genes in CC.
METHODS: The transcriptome and clinical data of three normal samples and 306 patients with CC were obtained from The Cancer Genome Atlas dataset. The GSE44001 dataset was acquired from the Gene Expression Omnibus database. OS-related subtypes in the cohort with CC were identified using unsupervised hierarchical clustering, univariate Cox analysis, gene set enrichment analysis (GSEA), and least absolute shrinkage and selection operator regression analysis. Additionally, molecular pathways that differ across subtypes were determined and OS-related genes linked to the prognosis of patients of CC were determined. Finally, a clinical prognostic gene signature was developed and validated. The relative infiltration level of immune cell subpopulations in different risk groups and subtypes was evaluated using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERPORT) algorithm and single-sample GSEA (ssGSEA) techniques.
RESULTS: The present study established two distinct OS subtypes (OS clusters A and B). Analysis using ssGSEA and CIBERSPORT revealed that OS cluster B exhibited a significant level of immune infiltration. A clinical prognostic gene signature was established using OS-related characteristic genes identified by examining the differentially expressed genes across both subtypes. Furthermore, patients with CC were grouped into high- and low-risk groups, with the low-risk group showing higher survival rates. Additionally, these individuals exhibited significant advantages in terms of survival and immunotherapy. Receiver operating characteristic curve analysis demonstrated the higher predictive value of the clinical prognostic gene signature. The outcomes of the validation group depicted congruence with those recorded in the training group.
CONCLUSIONS: A new model was constructed based on eight OS-related characteristic genes to aid the prediction of the survival rates of individuals with CC. The present study contributes to the existing literature on the mechanisms of OS genes in CC and offers a fresh perspective for future advancements in immunotherapy for such individuals.

UNASSIGNED: Eravacycline is a novel, fully synthetic fluorocycline antibiotic being developed for the treatment of serious infections, with a broad-spectrum antimicrobial activity, including against carbapenem-resistant gram-negative bacteria (CRGNB). However, the in vitro activity of eravacycline against CRGNB has not been well known in China. In this study, we analysed the antibacterial activity of eravacycline against CRGNB isolates in order to provide a theoretical basis for the clinical treatment.
UNASSIGNED: A total of 346 isolates of CRGNB were collected from two different tertiary care hospitals in Zhejiang, China. Carbapenem resistance genes of all isolates were detected by polymerase chain reaction. And we analysed the in vitro activity of eravacycline against CRGNB by antimicrobial susceptibility tests. In addition, the time-kill curves were generated to evaluate the antibacterial effect of tigecycline and eravacycline.
UNASSIGNED: Four different types of carbapenem-resistant isolates were collected, including 50 Escherichia coli isolates, 160 Klebsiella pneumoniae isolates, 42 Enterobacter cloacae complex isolates, and 94 Acinetobacter baumannii isolates. The carbapenem resistance genes were identified in 346 isolates, including bla KPC-2 (48.0%), bla OXA-23 (27.2%), bla NDM-1 (23.1%), and bla NDM-16 (0.3%). The antimicrobial susceptibility testing results showed that the minimum inhibitory concentration (MIC) values of 346 isolates were within the sensitivity range (≤0.0625~16 mg/L) and that the MIC50 or MIC90 of eravacycline was generally approximately 2-fold lower than tigecycline. In addition, the time-kill curves showed that the bactericidal effect of eravacycline was stronger than that of tigecycline against four different types of isolates.
UNASSIGNED: Our research indicated that eravacycline had a good antibacterial effect on CRGNB, which could provide a theoretical basis for the clinical treatment of drug-resistant bacterial infections in the future.

Recent studies have shown that ovarian aging is strongly associated with the risk of breast cancer, however, its prognostic impact on breast cancer is not yet fully understood. In this study, we performed a multicohort genetic analysis to explore its prognostic value and biological features in breast cancer.
The gene expression and clinicopathological data of 3366 patients from the The Cancer Genome Atlas (TCGA) cohort, the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort and the GSE86166 cohort were analyzed. A total of 290 ovarian aging-related genes (OARGs) were included in the establishment of the prognostic model. Furthermore, functional mechanisms analysis, drug sensitivity, and immune cell infiltration were investigated using bioinformatic methods.
An eight OARG-based signature was established and validated using independent cohorts. Two risk subgroups of patients with distinct survival outcomes were identified by the OARG-based signature. A nomogram with good predictive performance was developed by integrating the OARG risk score with clinicopathological factors. Moreover, the OARG-based signature was correlated with DNA damage repair, immune cell signaling pathways, and immunomodulatory functions. The patients in the low-risk subgroup were found to be sensitive to traditional chemotherapeutic, endocrine, and targeted agents (doxorubicin, tamoxifen, lapatinib, etc.) and some novel targeted drugs (sunitinib, pazopanib, etc.). Moreover, patients in the low-risk subgroup may be more susceptible to immune escape and therefore respond less effectively to immunotherapy.
In this study, we proposed a comprehensive analytical method for breast cancer assessment based on OARG expression patterns, which could precisely predict clinical outcomes and drug sensitivity of breast cancer patients.

Following the discovery of Eimeria kongi, we investigated the pathogenicity, immunogenicity, endogenous development and drug sensitivity of this coccidian. Coccidia-free rabbits were inoculated with 1 × 102 to 5 × 104 sporulated oocysts of E. kongi before challenge 14 days post inoculation. E. kongi was moderately pathogenic and induced good immunity against re-infection. All inoculated doses results in reduced food intake and body weight gain, and an inoculation oocyst dose of 1 × 103 or higher caused various degrees of diarrhea. Except for one death of the highest dose group, all rabbits recovered 12 days post inoculation. An inoculation dose of 1 × 103 or 1 × 104 oocysts conferred the most effective protection from re-infection, which reduced oocyst output by approximately 99% and maintained body weight gain. Four generations of schizogony were observed, and the endogenous development mainly occurred in the jejunum and ileum of rabbits. E. kongi was most sensitive to sulfachloropyrazine sodium, followed by decoquinate; it is resistant to diclazuril. Both decoquinate and sulfachloropyrazine sodium may be effective in the control of E. kongi infection.

UNASSIGNED: Patient-derived xenograft (PDX) models have shown a great efficiency in preclinical and translational applications. Gastrointestinal (GI) tumors have a strong heterogeneity, and the engraftment rate of PDX models remarkably vary. However, the clinicopathological and molecular characteristics affecting the engraftment rate still remain elusive.
UNASSIGNED: A total of 312 fresh tumor tissue samples from patients with GI cancer were implanted into immunodeficient mice. The median follow-up time of patients was 37 months. Patients\' characteristics were compared in terms of PDX growth and overall survival. PDX models of 3-6 generations were used for drug evaluation.
UNASSIGNED: In total, 171 (54.8%, 171/312) PDX models were established, including 85 PDX models of colorectal cancer, 21 PDX models of esophageal cancer, and 65 PDX models of gastric cancer. Other than tumor site, histology, differentiation degree, and serum alpha-fetoprotein (AFP) level, no significant differences were found between transplantation of xenografts and patients\' characteristics. For patients who had undergone neoadjuvant therapy, the incidence of tumor formation was higher in those with progressive disease (PD) or stable disease (SD). In gastric cancer, the results showed a higher transplantation rate in deficient mismatch repair (dMMR) tumors, and Ki-67 could be an important factor affecting the engraftment rate. The gene mutation status of RAS and BRAF, two important molecular markers in colorectal cancer, showed a high degree of consistency between patients\' tumors and PDXs. However, no significant effects of these two mutations on PDX engraftment rate were observed. More importantly, in this study although KRAS mutations were detected in two clinical cases, evident tumor inhibition was still observed after cetuximab treatment in both PDX models and patients.
UNASSIGNED: A large-scale PDX model including 171 cases was successfully established for GI tumors in our center. The relationship between clinicopathological and molecular features and engraftment rates were clarified. Furthermore, this resource provides us with profound insights into tumor heterogeneity, making these models valuable for PDX-guided treatment decisions, and offering the PDX model as a great tool for personalized treatment and translation research.

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

Anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors (TKIs) crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib are approved for advanced non-small-cell lung cancer (NSCLC) with ALK rearrangement. However, the mechanisms of resistance remain largely unclear.
This prospective multicenter study analyzed cell-free DNA (cfDNA) and/or cancer tissues of patients with NSCLC after progression on ALK TKI(s), using targeted next-generation sequencing. Patients\' clinicopathologic characteristics and treatment outcomes were analyzed.
Overall, 88 patients were enrolled; 31 cancer tissues and 90 cfDNA samples were analyzed. Five (16%) ALK mutations (L1196M ×2, I1171T, D1203N, G1269A/F1174L) and 3 possible bypass mutations (NRAS G12V, EGFR R108K, PIK3CA E545K) were found in 32 crizotinib-resistant cancers. Four (22%) ALK mutations (G1128A, G1202R, G1269A, I1171T/E1210K) and 3 possible bypass mutations (KIT D820E, MET E1012∗, EGFR P265_C291del) were found in 18 ceritinib-resistant cancers. Four (17%) ALK mutations (G1202R ×2, W1295C, G1202R/L1196M) and 1 possible bypass mutation (EGFR P753S) were found in 24 alectinib-resistant cancers. Two (11%) ALK mutations (G1202R/G1269A ×2) and 2 possible bypass mutations (BRAF V600E, MET D1246N) were found in 18 lorlatinib-resistant cancers. In patients with simultaneous paired tissue and cfDNA samples (n = 20), mutations were identified in 9 (45%) and 6 (30%) cases, respectively; the concordance rate was 45%.
The mechanisms of ALK TKI resistance were heterogeneous; ALK mutations were found in less than one-third of patients. Compound ALK mutations, which may confer lorlatinib resistance, may occur in crizotinib, ceritinib, and alectinib-resistant lung cancers.

The most important health benefit of selenium (Se) is in the prevention and control of cancer. Glutathione peroxidases (GPXs) and thioredoxin reductases (TXNRDs) are selenoenzymes that are thought to play a role in oxidative stress. The differential expression of genes of the TXNRD and GPX families is closely related to carcinogenesis and the occurrence of cancer. This study comprehensively analyzed the expression profiles of seven genes in the TXNRD and GPX families, in terms of their correlations with patient survival and immune-cell subtypes, tumor microenvironment, and drug sensitivity.
The expression profiles of genes in the TXNRD and GPX families differ between different types of cancer, and also between and within individual cancer cases. The expression levels of the seven analyzed genes are related to the overall survival of patients. The TXNRD1 and TXNRD3 genes are mainly related to poor prognoses, while other genes are related to good or poor prognoses depending on the type of cancer. All of the genes were found to be correlated to varying degrees with immune-cell subtypes, level of mechanistic cell infiltration, and tumor cell stemness. The TXNRD1, GPX1, and GPX2 genes may exert dual effects in tumor mutagenesis and development, while the TXNRD1, GPX1, GPX2, and GPX3 genes were found to be related to drug sensitivity or the formation of drug resistance.
The results will greatly help in identifying the association between genes and tumorigenesis, especially in the immune response, tumor microenvironment, and drug resistance, and very important when attempting to identify new therapeutic targets.