tsRNAs are a kind of small non-coding single-stranded RNA, which play an important role in many kinds of tumours. Previous studies have identified that tRF-31-U5YKFN8DYDZDD can be used as a novel tumour biomarker for the diagnosis and prognosis of gastric cancer (GC). In this study, we further explored the regulatory effect of tRF-31-U5YKFN8DYDZDD on tumour biological function in GC cells and related regulatory mechanisms. Inhibition of tRF-31-U5YKFN8DYDZDD can inhibit the proliferation, invasion, migration and angiogenesis of GC cells, while overexpression of tRF-31-U5YKFN8DYDZDD has the opposite effect. BMPER was shown to be a direct target of tRF-31-U5YKFN8DYDZDD in GC. Furthermore, the cytological effects of stable overexpression of BMPER were similar to the inhibition of tRF-31-U5YKFN8DYDZDD. And the attenuation of BMPER expression rescued the tRF-mediated promotion of GC cells. WB showed that up-regulation of tRF-31-U5YKFN8DYDZDD could increase the phosphorylation level of ERK and Smad1/5, and promote the expression of epithelial mesenchymal transition (EMT) and matrix metalloproteinases. Animal experiments in vivo show that down-regulation of tRF-31-U5YKFN8DYDZDD can effectively inhibit tumour growth. These data suggest that tRF-31-U5YKFN8DYDZDD is a new tumour-promoting factor and may be a potential new therapeutic target for GC.

Osimertinib is standard therapy for EGFR-mutant lung cancer, and HER2 amplification is a known resistance mechanism. We report HER2-positive breast cancer showing durable regression during osimertinib monotherapy for synchronous EGFR-mutated lung adenocarcinoma. Post-treatment analysis showed marked regression, HER2 amplification, no detectable EGFR, HER2, or PIK3CA mutations, ER/PgR negativity, preserved PTEN, and reduced proliferative activity. These findings suggest ERBB pathway inhibition may have contributed, although the mechanism remains speculative without functional validation. This hypothesis-generating observation describes unexpected breast cancer regression during osimertinib treatment.

Gliomas are the most lethal malignant tumors of the central nervous system, and their treatment continues to face serious challenges. Increasing evidence suggests that lactylation is strongly associated with tumorigenesis and progression. However, studies of lactylation in gliomas are rare. In this study, we screened for lactylation-related genes in glioblastoma affecting patient prognosis based on TCGA and GEO databases and constructed a prediction model for lactylation-related genes using various machine learning methods. In addition, the researchers have also performed tumor somatic mutation difference analysis, drug sensitivity analysis, and single-cell analysis. We developed a prognostic model for lactylation-related genes and validated its predictive power. Further analysis revealed differences in tumor somatic mutations between the high- and low-risk groups. We screened 50 drugs using drug sensitivity analysis, and at the single-cell level, we demonstrated the expression of characterized genes in glioblastoma. Our findings suggest that the lactylation-related gene prediction model can serve as a reliable tool for predicting the prognosis of patients with glioma.

Pharmacogenetics plays an increasingly important role in oncology by supporting individualized therapeutic strategies based on patient genetic profiles. ClinicalTrials.gov provides a valuable platform for evaluating global trends in pharmacogenetic anticancer clinical trials. This systematic review aimed to evaluate pharmacogenetic-focused anticancer clinical trials registered on ClinicalTrials.gov and summarize trends in study design, cancer types, targeted biomarkers, therapeutic interventions, and geographic distribution.

The androgen receptor (AR) is an emerging biomarker in breast cancer (BC), yet its prognostic relevance in patients undergoing neoadjuvant chemotherapy (NACT) remains inconclusive. While most studies focus on Western populations, data from the Caucasus are scarce. Given regional differences in tumor biology and treatment access, evaluating AR's clinical significance in these populations is crucial. This study aimed to investigate the association between AR expression, pathological response, and disease-free survival (DFS) in stage II to III BC patients receiving NACT. A retrospective cohort study was conducted on 132 female BC patients treated with NACT at Bonadea Hospital. AR expression patterns were categorized as negative, weak, moderate, or strong. Statistical analyses included chi-square tests, Kaplan-Meier survival analysis, and univariate Cox regression to evaluate associations between AR expression and clinicopathological features, pathological complete response (pCR), and DFS. The median age at diagnosis was 47 years. Invasive ductal carcinoma accounted for 91.1% of cases, with 50.0% of tumors graded as II or III. pCR was achieved in 28.6% of patients. Moderate-to-strong AR expression was observed in 15% of cases and was associated with a significantly lower Ki-67 index (P = .047). Although dichotomized AR expression did not significantly predict DFS (hazard ratio [HR]: 0.35, 95% confidence interval [CI]: 0.08-1.47, P = .151), Cox regression using all 4 AR categories revealed a significant reduction in recurrence risk (HR = 0.384, 95% CI: 0.160-0.918, P = .031). In multivariable model adjusting for estrogen receptor, human epidermal growth factor receptor 2, and Ki-67 expression, moderate-to-strong AR expression remained the most influential prognostic factor, although the association did not reach statistical significance (adjusted HR: 0.31, 95% CI: 0.07-1.41, P = .129; concordance index = 0.83). Among patients with residual disease (non-pCR), moderate-to-strong AR expression was associated with a higher 2-year DFS rate (90.9% vs 46.4%, P = .089). Sensitivity analysis demonstrated no evidence of selection bias related to AR data availability, as 2-year DFS was comparable between patients with and without available AR assessment (76.1% vs 71.6%, log-rank P = .975). Moderate-to-strong AR expression may be associated with improved DFS in patients treated with NACT, especially in those with incomplete pathological response. These findings highlight the potential of AR as a prognostic biomarker and therapeutic target, warranting validation in prospective studies.

Colorectal cancer (CRC) exhibits marked molecular and microenvironmental heterogeneity, and the TNM staging system alone does not fully explain differences in prognosis or treatment response. Neutrophil extracellular traps (NETs) and oxidative stress participate in inflammatory remodeling, immune regulation, and tumor progression in CRC. However, transcriptomic studies integrating NET-related programs with oxidative stress-associated signaling for molecular stratification and prognostic modeling remain limited. Transcriptome and clinical data from TCGA-COADREAD (training set, 449 tumor and 39 normal samples) and GSE39582 (validation set, 566 tumor and 19 non-tumor samples) were analyzed. Candidate NET-related and oxidative stress-associated genes were collected from GeneCards, Gene Ontology, and published NETosis signatures, and were further restricted by differential expression analysis (|log2FC| > 0.585, adjusted P < .05) and univariate Cox analysis (P < .05). Consensus clustering, survival analysis, immune infiltration analysis, mutation analysis, and oncoPredict-based drug sensitivity modeling were performed. A multigene prognostic model was constructed using LASSO-Cox regression and evaluated in both cohorts. Two molecular subtypes were identified. C1 showed a better prognosis, higher tumor mutational burden, higher immune checkpoint and immunogenicity scores, and an immune-active phenotype. C2 showed poorer survival, enrichment of NET formation and inflammatory pathways, lower relative neutrophil infiltration by CIBERSORT, and predicted sensitivity to JAK, Wnt/β-catenin, proteasome, and cell-cycle checkpoint inhibitors. The prognostic model separated high- and low-risk patients in both the training and validation cohorts, with 1-, 3-, and 5-year area under the curves of 0.698, 0.711, and 0.645 in TCGA-COADREAD and 0.542, 0.578, and 0.569 in GSE39582. This study proposes NET-related CRC molecular subtypes with oxidative stress-associated biological context and develops a transcriptomic risk model with external validation. The findings suggest subtype-specific immune and therapeutic features, while prospective clinical and functional validation is required before clinical translation.

The highly heterogeneous properties and dynamic immunogenic features of osteosarcoma (OS) lead to limited therapeutic efficacy. The oncogenetic mechanisms of OS await further explanation, and new insights into therapeutic targets for OS remain urgent. We used single-cell RNA-sequencing data to identify cell type-specific differentially expressed genes between normal human osteocytes and OS cells. Subsequently, we analyzed the differential expression of leptin receptor (LEPR) in OS cell lines through in vitro experiments. We assessed the prognostic potential of LEPR and immune cell infiltration status associated with differential LEPR expression in OS patients. By integrating protein quantitative trait loci and methylation quantitative trait loci datasets with OS genome-wide association studies summary statistics, we performed Mendelian randomization analysis to elucidate the causal effect of LEPR on OS risk and identify cytosine-phosphate-guanine sites that may influence LEPR expression and OS risk. We observed lower LEPR expression in OS cell clusters compared to normal osteocyte clusters. RT-qPCR results indicated that the LEPR gene showed consistent differential expression in OS cells. Survival analysis revealed that lower LEPR expression was associated with worse survival outcomes in OS patients. OS patients with high LEPR expression tended to exhibit more immune cell infiltration. Mendelian randomization analysis indicated that genetically-determined LEPR was inversely associated with OS risk. We identified 8 methylation cytosine-phosphate-guanine sites within the LEPR promoter region causally associated with both lower LEPR protein expression and higher OS risk. Our integrated multi-omics analyses supported a causal association between LEPR expression and OS risk, suggesting that LEPR may offer protective benefits for OS patients. The relationship between LEPR and OS risk highlights the potential of LEPR as an early prognostic biomarker and may provide new insights into the etiology of OS.

Although several biomarkers can predict human papillomavirus (HPV) status in head and neck squamous cell carcinoma (HNSC), their accuracy is low. We assessed the HPV status of HNSC using a Scissor algorithm in conjunction with bulk sequencing and single-cell sequencing data. A Scissor scoring system was constructed for predicting HPV status using the gene set variation analysis algorithm. The biological functions and signaling pathways influencing the Scissor scoring model were described using correlation analysis and functional annotation. We used the Scissor algorithm to identify 1070 HPV-positive cells in single-cell sequencing data based on HPV status and then screened out genes that were significantly expressed in the Scissor-positive cells. We then constructed a Scissor score model using the 43 genes specifically expressed in Scissor-positive cells. The model had high accuracy in predicting HPV status in HNSC patients (area under curve in the discovery set: 86.6%, area under curve in the validation set: 96.1% and 79.8%). We also found that patients with higher Scissor scores had better outcomes, higher tumor mutational burden and tumor neoantigen burden, higher levels of immune checkpoint infiltration, and more cytotoxic T cell infiltration, and better outcomes with radiotherapy and immunotherapy. We constructed a predictive model that can accurately predict HPV status in patients with HNSC with greater accuracy than most current biomarkers.

The adrenomedullin receptor signaling pathway plays a crucial role in tumor progression, yet its comprehensive implication in hepatocellular carcinoma (HCC) remains underexplored. This study aimed to develop and validate a multi-gene prognostic signature based on adrenomedullin receptor signaling-related genes (ARGs) and to elucidate its associations with clinicopathological features, immune microenvironment, drug sensitivity, and somatic mutations in HCC. Transcriptomic and clinical data of HCC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Pan-cancer analysis was performed to evaluate the expression and prognostic value of ARGs. A prognostic model was constructed using 116 machine learning algorithm combinations and evaluated by C-index and receiver operating characteristic analysis. The risk score (RS) derived from the model was further correlated with clinical characteristics, immune cell infiltration, drug sensitivity, somatic mutations, and pathway activities. A nomogram was established for clinical applicability. Single-cell RNA sequencing data were analyzed to delineate the cellular heterogeneity of ARG expression within the tumor microenvironment. ADM and RAMP3 were significantly associated with HCC prognosis. The optimal model, built with Random Survival Forest, demonstrated robust predictive performance in both the Cancer Genome Atlas (HR = 9.51, P < .001) and GSE14520 (HR = 2.24, 95% CI: 1.46-3.43, P < .001) cohorts. High-risk patients exhibited advanced T stage, higher histological grade, and poorer overall survival. Computational immune profiling suggested decreased immune cell infiltration and altered immune checkpoint gene expression in the high-risk group. In silico drug sensitivity analysis predicted increased susceptibility to certain targeted agents (e.g., erlotinib) in high-risk patients, though these findings are hypothesis-generating and require experimental validation. Furthermore, RS was weakly correlated with tumor mutational burden (R = 0.16, P = .004), though TP53 mutation frequency did not differ significantly between risk groups after covariate adjustment. The nomogram integrating RS demonstrated favorable predictive accuracy for 1-, 3-, and 5-year survival. Single-cell analysis revealed predominant expression of ARGs in tumor endothelial cells. We developed and validated an ARG-based prognostic signature that effectively stratifies HCC patients into distinct risk subgroups. This model may serve as a hypothesis-generating framework for individualized prognosis prediction and for identifying potential therapeutic vulnerabilities in HCC that warrant prospective investigation.

Long-term survival of extensive-stage small-cell lung cancer (ES-SCLC) remains rare, with most patients experiencing disease progression during maintenance therapy. Poly (ADP-ribose) polymerase (PARP) inhibitors have the potential to confer antitumor activity, modify tumor immunogenicity, and sensitize tumors to anti-programmed cell death protein 1/programmed death-ligand 1 therapy. We conducted this phase 2 trial to investigate the efficacy and safety of durvalumab plus olaparib as maintenance therapy in patients with ES-SCLC.

Chemokine (C-C motif) ligand 26 (CCL26), also known as eotaxin-3, is an immune-regulatory chemokine involved in inflammatory responses and immune cell recruitment. Emerging evidence suggests its expression is dysregulated across multiple malignancies, yet comprehensive pan-cancer evaluations remain limited.

The phase 3 RASolute 302 trial demonstrated significant overall and progression-free survival benefits with daraxonrasib over investigator's choice chemotherapy in patients with metastatic pancreatic ductal adenocarcinoma harboring RAS G12 mutations after progression on prior chemotherapy.1 The benefit in patients with non-G12 RAS mutations or RAS wild-type tumors remains unclear.

The genetic architecture of polycystic ovary syndrome (PCOS) has not been fully elucidated. Translating genome-wide association study (GWAS) loci into functional effector genes represents a key challenge for elucidating disease mechanisms and advancing targeted therapeutic strategies. In this study, the FinnGen R12 dataset was utilized in conjunction with the Genotype-Tissue Expression Project (GTEx) v8 eQTL dataset to conduct cross-tissue transcriptome-wide association studies (TWAS). External validation was performed in two independent datasets, comprising a PCOS meta-analysis of Rotterdam- and NIH-criteria cohorts and UK Biobank summary statistics. To prioritize high-confidence candidate genes, we integrated a cross-tissue (UTMOST) TWAS and tissue-specific (FUSION) TWAS with a gene-based association test (MAGMA). Furthermore, causal inference analyses including Mendelian randomization (MR), colocalization analysis, summary data-based MR, and the heterogeneity in dependent instrument (HEIDI) test were carried out on candidate genes. GeneMANIA, gene-chemical-disease analysis, and phenome-wide association study (PheWAS) were further performed on candidate genes. Finally, we explored expression profiles of core candidate genes using transcriptomic datasets from the Gene Expression Omnibus (GEO) database. We prioritized 15 candidate genes potentially associated with PCOS. External analyses across independent datasets showed limited consistency, which may reflect differences in statistical power and cohort heterogeneity. MCM6 was prioritized by all three approaches, whereas the remaining 14 genes were supported by two approaches. Among the 15 candidate genes, six genes were demonstrated to have causal associations with PCOS risk. GeneMANIA network analysis further revealed that these candidate genes were involved in key biological functions, including DNA replication and regulation, DNA repair and recombination, protein-DNA complex, cell cycle regulation, nucleic acid-enzyme activities, DNA structure and chromosomal regions, reproductive system development, and sex differentiation. Gene-chemical-disease analysis suggested that the prioritized genes may mediate crosstalk between PCOS genetic risk and environmental/metabolic factors. PheWAS analysis highlighted the pleiotropic roles of PCOS candidate genes, which contributed to both PCOS susceptibility and a spectrum of PCOS-related traits including metabolic and cardiovascular phenotypes. Finally, transcriptomic data showed downregulated MCM6 expression in granulosa cells of PCOS patients. Our study established a multipronged pipeline for PCOS gene prioritization and provided novel insights into the genetic architecture of PCOS.

Neuroblastoma (NB) is a common pediatric tumor that exhibits significant clinical heterogeneity and substantial prognostic variability. However, there is still a lack of effective prognostic tools. It has been found that the differentiation of myeloid cells in the tumor microenvironment (TME) has a profound influence on tumor progression and immune regulation in neuroblastoma. But the correlation between NB and myeloid cell differentiation-related genes (MCDRGs) is still unclear. This study integrated bulk and single-cell RNA sequencing data to identify key prognostic genes associated with myeloid cell differentiation, construct and validate a risk prediction model, and thoroughly investigate the molecular mechanisms through which these genes regulate the TME and influence NB prognosis. Using bioinformatic approaches, we retrieved NB-related transcriptomic data from public databases, identified differentially expressed myeloid cell differentiation-related genes (MCDRGs), and constructed a prognostic risk model using Cox regression and machine-learning algorithms. Functional enrichment analysis, TME characterization, immune checkpoint profiling, drug sensitivity analysis, and somatic mutation analysis were performed between the high-risk group (HRG) and low-risk group (LRG). Single-cell RNA sequencing data were used to dissect the cell type-specific expression patterns of prognostic genes, which were further experimentally validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR). Five key prognostic genes (FAXDC2, GP1BB, TRIB1, ETV2, and H4C12) were identified from 187 candidate genes, and a robust risk prediction model was constructed. Significant differences were observed between the HRG and LRG in terms of TME scores, immune cell infiltration, and immune checkpoint expression. Ribosome biogenesis and cell cycle-related pathways were enriched in the HRG, which displayed higher sensitivity to entinostat and sapitinib. Single-cell analysis further highlighted fibroblasts and myeloid cells as key cell subsets in which prognostic genes exhibited dynamic expression patterns. RT-qPCR further validated these gene expression trends. The prognostic model based on MCDRGs effectively predicts survival outcomes in patients with NB. This model systematically reveals the comprehensive mechanisms by which MCDRGs influence prognosis through regulating the TME, cell-cell interactions, and therapeutic responses. These findings provide a new theoretical basis and potential targets for precise risk stratification and individualized treatment strategies in NB.

Rhabdoid tumors (RTs) are highly aggressive cancers driven by biallelic mutation of SMARCB1, a core subunit of the BAF (SWI/SNF) complex. We found that SMARCB1-deficient tumors have a defect in the microhomology-mediated end joining (MMEJ) pathway, and SMARCB1 is essential for maintaining the protein level of the core MMEJ protein, DNA Polymerase theta (PolΘ). Mechanistically, SMARCB1 facilitates the nuclear export of the POLQ mRNA through its interaction with the nuclear pore complex. Interestingly, loss of MMEJ in RT cells leads to a compensatory activation of, and a hyper-dependence on, the Fanconi Anemia (FA)/BRCA pathway. Notably, degraders of RBM39, a splicing modulator, show strong antitumor efficacy in RT models in vitro and in vivo by disrupting FA/BRCA pathway. SMARCB1 and other cBAF/pBAF components are important for maintenance of MMEJ activity and PolΘ protein level, suggesting that BAF-deficient cancers more broadly may be treated by targeted inhibition of the FA/BRCA pathway.

Systemic inhibition of interleukin-1b (IL-1b) has been shown to reduce the incidence of lung cancer in patients in years after treatment, but knowledge gaps surrounding its activation and role in the tumor microenvironment hinder cancer interception. We developed activity-based technologies to probe inflammation in early lung cancer and identified a translational target candidate. Probes sensitive to IL-1b-activating proteases were designed and applied to a murine model of inflammatory lung cancer, Kras/Trp53-mutant mice with SIINFEKL expression (KPS). Our nanosensors revealed elevated caspase-1 expression and activity in tumors, highlighting the importance of caspase-1 processing IL-1b during cancer development. We conducted a preclinical combination therapy trial by administering IL-1b blockade and caspase-1 inhibition. We observed significant reduction in lung cancer, including complete ablation of tumor incidence in nearly 20% of KPS mice. Our approach to understanding the interplay of protease activity and cytokine activation supports new strategies to mitigate inflammation and intercept lung cancer progression.

Lineage-determining transcription factors (TFs), such as p63 in epithelial tissues, establish normal tissue identity but can also drive carcinogenesis. The epigenetic basis for these divergent effects, however, is unclear. Here, we show that p63 reorganizes three-dimensional (3D) chromatin topology in squamous cell carcinoma (SCC) through cooperation with the tumor-selective partner TF, FOXK1. Multi-omic profiling across normal and malignant states reveals that p63 enhances connectivity between cis-regulatory elements near oncogenes and promotes a secondary enhancer looping architecture, in which p63 and FOXK1-enriched enhancers indirectly contact target promoters via FOXK1-associated intermediate anchors lacking p63 binding. FOXK1 mediates p63-dependent chromatin looping, as FOXK1 loss selectively weakens p63-dependent loops and reduces transcription of associated target genes. The p63-FOXK1 interaction is observed in human tumor samples but not in normal tissues. Together, our findings define an epigenetic mechanism by which a lineage master TF and tumor-restricted partner TF reshape 3D chromatin structure to drive oncogenic transcription, with implications for tumor cell identity and therapy.

Tumor-associated macrophage (TAM) infiltration is a critical characteristic of triple-negative breast cancer (TNBC) related to drug resistance and poor prognosis. Integrating macrophages into TNBC spheroids is crucial to improve the accuracy of 3D in vitro models that mimic the complexity of the tumor microenvironment (TME) and assess treatment response. However, this remains challenging since the reciprocal effects of these two cell types on each other are not fully understood. In this study, we used the TNBC cell line, MDA-MB-231, and polarized M1-like or M2-like macrophages derived from THP-1 monocytes to establish 3D co-culture spheroids to examine bidirectional interactions between these cells and responses to chemotherapy. Drug efficacy, epithelial-mesenchymal transition (EMT) in cancer cells, macrophage phenotypes, and RNA sequencing, including pathway enrichment analysis, were performed in 3D spheroids. CIBERSORTx deconvolution of RNA sequencing results facilitated the separation of cell types within mixtures to estimate their corresponding cell fractions. We observed that M2 macrophages increased the viability of MDA-MB-231 cells in 3D spheroids, while both M1 and M2 macrophages increased the chemosensitivity of 3D spheroids to doxorubicin and paclitaxel. Interestingly, instead of maintaining their phenotypes, both M1 and M2 macrophages lost some polarization and formed a mixed M1-M2 phenotype when co-cultured with MDA-MB-231 cells in 3D spheroids, a phenomenon further supported by RNA-seq deconvolution analysis. However, the fraction of M1-like macrophages shifting to M2-like was much lower than the fraction of M2-like macrophages shifting to M1-like in the 3D co-cultures. Compared with 2D cultures, an expected mesenchymal transition, numerous differentially expressed genes (DEGs) and various pathways, including both tumor-promoting and tumor-suppressing genes, were observed in 3D spheroid MDA-MB-231 cells. However, both M1- and M2-like macrophages induced only partial EMT phenotype changes of cancer cells in co-cultures. Furthermore, a coexistence of pro-inflammatory and anti-inflammatory DEGs was observed in both M1 and M2-like co-cultured cancer spheroids. In conclusion, our findings present an effective 3D co-culture system of breast cancer cells and integrated macrophages for studying dynamic cellular phenotype changes and reciprocal interactions in a heterogeneous environment to mimic aspects of the TME and enhance the accuracy of preclinical in vitro treatment response studies.

The study of genetic diversity has a rich history, as it serves as the foundation upon which evolutionary processes act. Numerous methods have been developed to quantify genetic variation within and among populations. While some methods focus solely on allele frequencies, others assess branching patterns within phylogenetic clades without considering genetic distances. Here we introduce the Proportional Diversity Likelihood Ratio Statistic (PLR), a novel method that integrates genetic distances with phylogenetic tree structure to provide a comprehensive measure of genetic diversity. This method evaluates the likelihood of observed genetic data under models with constrained and unconstrained diversity, offering a robust statistical framework for testing hypotheses about genetic differentiation. The PLR approach is broadly applicable, from assessing immune repertoire diversity, such as B-cell diversity before and after vaccination, to evaluating tumor genetic diversity before and after treatment. By combining genetic distance and tree topology, PLR enables deeper insights into evolutionary dynamics and population structure. We demonstrate the use of this approach on SARS-CoV-2 sequences from two different time frames and found that diversity maybe waning. Moreover, we applied PRL to chronic lymphocytic leukemia (CLL) data, revealing that with temporally order sequences from with treatment we do not see a significant drop in diversity.

Uveal melanoma is the most common primary malignant intraocular tumor in adults. A major prognostic factor is the cytogenetic profile of the tumor, particularly the presence of monosomy of chromosome 3, which is associated with an increased risk of developing distant metastases. The aim of this study was to determine whether there is a relationship between the metabolic activity of the tumor assessed by SUVmax (maximum standardized uptake value) in PET/CT or PET/MRI and the presence of monosomy 3 in the tumor cytogenetic profile.