BACKGROUND: Cuproptosis, a novel form of cell death mediated by protein lipoylation, is intricately linked to mitochondrial metabolism. However, the clinical association of cuproptosis- related genes (CRGs) in thyroid cancer remains unclear. In this study, we performed a systematic investigation on the differential expression and genetic alterations of CRGs in papillary thyroid cancer (PTC) and constructed a CRG signature to predict the prognosis of PTC patients.
METHODS: We integrated the data of The Cancer Genome Atlas (TCGA) database and analyzed the expression of 10 CRGs in PTC. CRG signature was constructed using univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression. In addition, the signature-related molecular features were validated by a combination of functional enrichment, Cox regression, and immune infiltration analysis. Independent validation cohort and quantitative real-time polymerase chain reaction (qRT-PCR) were used to validate the expression of differentially expressed CRG (CDKN2A).
RESULTS: Thyroid cancer patients could be divided into two subtypes (high and low CRG score groups). We found that the overall survival (OS) of patients was lower in the high CRG score group (HCSG) than in the low CRG score group (LCSG) (P < 0.001). The area under the curve (AUC) values for 3 years, 5 years, and 8 years were 0.872, 0.941, and 0.976, respectively. Cox regression analysis indicated that the CRG score could serve as an independent prognostic indicator for PTC. Functional enrichment analysis indicated that the CRG prognostic signature was also associated with the tumor immune microenvironment. In HCSG, the immune suppression cell score was significantly higher than in LCSG. In addition, we identified the expression of CRG (CDKN2A) by qRT-PCR, and the results aligned with the TCGA database.
CONCLUSIONS: Our CRG signature demonstrates excellent predictive capabilities for the prognosis of PTC patients. CRGs may play an important role in tumorigenesis and could be used to predict the immunotherapy efficacy of PTC.

Ovarian cancer is one of the most common cancers with a high mortality rate among females worldwide. The understanding of the pathogenesis of the disease is highly important to provide personalized therapy to the patients. Ovarian cancer is as heterogeneous as colon and breast cancer which makes it difficult to treat. The development of gene signature is the only hope in providing targeted therapy to improve the survival of ovarian cancer patients. Malignant epithelial carcinomas are the most common cancers of the ovary with different histological and molecular subtypes and clinical behavior. The development of precursor lesions of ovarian carcinoma in the tubes and endometrium has provided a new dimension to the origin of ovarian cancers. The clinical utility of various gene signatures may not be logical unless validated. Validated gene signatures can aid the clinician in deciding the appropriate line of treatment.

UNASSIGNED: Mycosis fungoides (MF) is the most common subtype of cutaneous T-cell lymphoma (CTCL). Comprehensive analysis of MF cells in situ and ex vivo is complicated by the fact that is challenging to distinguish malignant from reactive T cells with certainty.
UNASSIGNED: To overcome this limitation, we performed combined single-cell RNA (scRNAseq) and T-cell receptor TCR sequencing (scTCRseq) of skin lesions of cutaneous MF lesions from 12 patients. A sufficient quantity of living T cells was obtained from 9 patients, but 2 had to be excluded due to unclear diagnoses (coexisting CLL or revision to a fixed toxic drug eruption).
UNASSIGNED: From the remaining patients we established single-cell mRNA expression profiles and the corresponding TCR repertoire of 18,630 T cells. TCR clonality unequivocally identified 13,592 malignant T cells. Reactive T cells of all patients clustered together, while malignant cells of each patient formed a unique cluster expressing genes typical of naive/memory, such as CD27, CCR7 and IL7R, or cytotoxic T cells, e.g., GZMA, NKG7 and GNLY. Genes encoding classic CTCL markers were not detected in all clusters, consistent with the fact that mRNA expression does not correlate linearly with protein expression. Nevertheless, we successfully pinpointed distinctive gene signatures differentiating reactive malignant from malignant T cells: keratins (KRT81, KRT86), galectins (LGALS1, LGALS3) and S100 genes (S100A4, S100A6) being overexpressed in malignant cells.
UNASSIGNED: Combined scRNAseq and scTCRseq not only allows unambiguous identification of MF cells, but also revealed marked heterogeneity between and within patients with unexpected functional phenotypes. While the correlation between mRNA and protein abundance was limited with respect to established MF markers, we were able to identify a single-cell gene expression signature that distinguishes malignant from reactive T cells.

UNASSIGNED: This research aims to establish a copper homeostasis-related gene signature for predicting the prognosis of epithelial ovarian cancer and to investigate its underlying mechanisms.
UNASSIGNED: We mainly constructed the copper homeostasis-related gene signature by LASSO regression analysis. Then multiple methods were used to evaluate the independent predictive ability of the model and explored the mechanisms.
UNASSIGNED: The 15-copper homeostasis-related gene (15-CHRG) signature was successfully established. Utilizing an optimal cut-off value of 0.35, we divided the training dataset into high-risk and low-risk subgroups. Kaplan-Meier analysis revealed that survival times for the high-risk subgroup were significantly shorter than those in the low-risk group (P < .05). Additionally, the Area Under the Curve (AUC) of the 15-CHRG signature achieved 0.822 at 1 year, 0.762 at 3 years, and 0.696 at 5 years in the training set. COX regression analysis confirmed the 15-CHRG signature as both accurate and independent. Gene set enrichment (GSEA), Kyoto Encyclopedia of Gene and Genome (KEGG) and Gene Ontology (GO) analysis showed that there were significant differences in apoptosis, p53 pathway, protein synthesis, hydrolase and transport-related pathways between high-risk group and low-risk group. In tumor immune cell (TIC) analysis, the increased expression of resting mast cells was positively correlated with the risk score.
UNASSIGNED: Consequently, the 15-CHRG signature shows significant potential as a method for accurately predicting clinical outcomes and treatment responses in patients with epithelial ovarian cancer.

UNASSIGNED: Drug resistance is the primary obstacle to advanced tumor therapy and the key risk factor for tumor recurrence and death. 5-Fluorouracil (5-FU) chemotherapy is the most common chemotherapy for individuals with colorectal cancer, despite numerous options.
UNASSIGNED: The Gene Expression Omnibus database was utilized to extract expression profile data of HCT-8 human colorectal cancer wild-type cells and their 5-FU-induced drug resistance cell line. These data were used to identify 5-FU resistance-related differentially expressed genes (5FRRDEGs), which intersected with the colorectal adenocarcinoma (COAD) transcriptome data provided by the Cancer Genome Atlas Program database. A prognostic signature containing five 5FRRDEGs (GOLGA8A, KLC3, TIGD1, NBPF1, and SERPINE1) was established after conducting a Cox regression analysis. We conducted nomogram development, drug sensitivity analysis, tumor immune microenvironment analysis, and mutation analysis to assess the therapeutic value of the prognostic qualities.
UNASSIGNED: We identified 166 5FRRDEGs in patients with COAD. Subsequently, we created a prognostic model consisting of five 5FRRDEGs using Cox regression analysis. The patients with COAD were divided into different risk groups by risk score; the high-risk group demonstrated a worse prognosis than the low-risk group.
UNASSIGNED: In summary, the 5FRRDEG-based prognostic model is an effective tool for targeted therapy and chemotherapy in patients with COAD. It can accurately predict the survival prognosis of these patients as well as to provide the direction for exploring the resistance mechanism underlying COAD.

UNASSIGNED: The aims of this study were to screen for phagocytosis regulator-related genes in tissue samples from children with medulloblastoma (MB) and to construct a prognostic model based on those genes.
UNASSIGNED: Differentially expressed genes between the MB and control groups were identified using the GSE50161 dataset from the Gene Expression Omnibus database. Prognosis-related phagocytosis regulator genes were selected from the GSE85217 dataset. Intersecting genes of the two datasets (differentially expressed prognosis-related phagocytosis regulator genes) were submitted to unsupervised cluster analysis to identify disease subtypes, after which the association between the subtypes and the immune microenvironment was analyzed. A prognostic risk score model was constructed, and functional, immune-related, and drug sensitivity analyses were performed.
UNASSIGNED: In total, 23 differentially expressed prognosis-related phagocytosis regulator genes were identified, from which two disease subtypes (clusters 1 and 2) were classified. The prognoses of the patients in cluster 2 were significantly worse than those of the patients in cluster 1. The immune microenvironment differed significantly between the two subtypes. Finally, 10 genes (FAM81A, EZR, NDUFB9, RCOR1, FOXO4, NHLRC2, KIF23, PTPN6, SMAGP, and MED13) were selected to establish the prognostic risk score model. The prognosis in the low-risk group was better than that in the high-risk group. The model genes NDUFB9 and PTPN6 were positively correlated with M2 macrophages.
UNASSIGNED: Ten key phagocytosis regulator genes were screened to construct a prognostic model for MB. These genes may serve as key biomarkers for predicting the prognosis of patients with this type of brain cancer.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer, characterized by a high morbidity rate. Long non-coding RNAs (lncRNAs) play an important role in regulating various cellular processes and diseases, including cancer. However, their specific roles and mechanisms in HCC are not fully understood. This study used a multi-cohort design to investigate necroptosis-related lncRNAs (NRLs) in patients with HCC. We curated a list of 1095 NRLs and 838 genes showing differential expression between tumor and normal tissues. Among them, we found 105 NRLs closely associated with the prognosis of HCC patients. The 10 lncRNAs (AC100803.3, AC027237.2, AL158166.1, LINC02870, AC026412.3, LINC02159, AC027097.1, AC139887.4, AC007405.1, AL023583.1) generated by LASSO-Cox regression analysis were used to create a prognostic risk model for HCC and group patients into groups based on risk. The KEGG analysis revealed distinct pathway enrichments in high-risk (H-R) and low-risk (L-R) subgroups. According to GO analysis, this study identified 230 differentially expressed genes (DEGs) that were significantly enriched in specific biological processes. Comparison of immune checkpoint-related genes (MCPGs) between H-R and L-R patients revealed significant differences. Moreover, we established a correlation between the risk scores of patients with liver cancer and their sensitivity to 16 chemotherapeutic agents. Employing protein-protein interaction (PPI) analysis, we identified 10 hub genes that potentially regulate the molecular networks involved in HCC development. This study is a pioneering effort to investigate the roles of NRLs in HCC. It opens a new avenue for potential targeted therapies and provides insights into the molecular mechanisms of HCC.

Background: This study aimed to investigate the comprehensive expression profile of cancer stem cell (CSC)-related genes and construct a prognostic signature for overall survival (OS) prediction in high-risk Wilms\' tumor (WT). Materials and methods: Gene expression and survival data from 120 high-risk WT cases in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-WT were used. Results: In total, 229 CSC-related genes were found to be significantly dysregulated in WT compared to tumor-adjacent normal tissues, among which 34 were associated with OS. Using LASSO regression, a 22-gene signature was developed, which exhibited excellent performance in 3-, 5-, and 10-year OS predictions (AUC > 0.86). The high-risk score group showed markedly poorer OS compared to the low-risk score group (median separation, HR = 6.41, 95% CI: 3.18-12.92, p = 3.2e - 9). The 22-gene signature was an independent prognostic factor for OS (HR = 5.086, 95% CI: 3.019-8.568, p < 0.001). Conclusion: This study identified a robust prognostic signature that can effectively support OS prediction.

Gene expression profiling technologies have revolutionized cell biology, enabling researchers to identify gene signatures linked to various biological attributes of melanomas, such as pigmentation status, differentiation state, proliferative versus invasive capacity, and disease progression. Although the discovery of gene signatures has significantly enhanced our understanding of melanocytic phenotypes, reconciling the numerous signatures reported across independent studies and different profiling platforms remains a challenge. Current methods for classifying melanocytic gene signatures depend on exact gene overlap and comparison with unstandardized baseline transcriptomes. In this study, we aimed to categorize published gene signatures into clusters based on their similar patterns of expression across clinical cutaneous melanoma specimens. We analyzed nearly 800 melanoma samples from six gene expression repositories and developed a classification framework for gene signatures that is resilient against biases in gene identification across profiling platforms and inconsistencies in baseline standards. Using 39 frequently cited published gene signatures, our analysis revealed seven principal classes of gene signatures that correlate with previously identified phenotypes: Differentiated, Mitotic/MYC, AXL, Amelanotic, Neuro, Hypometabolic, and Invasive. Each class is consistent with the phenotypes that the constituent gene signatures represent, and our classification method does not rely on overlapping genes between signatures. To facilitate broader application, we created WIMMS (what is my melanocytic signature, available at https://wimms.tanlab.org/), a user-friendly web application. WIMMS allows users to categorize any gene signature, determining its relationship to predominantly cited signatures and its representation within the seven principal classes.

BACKGROUND: Deeper insights into ERBB2-driven cancers are essential to develop new treatment approaches for ERBB2+ breast cancers (BCs). We employed the Collaborative Cross (CC) mouse model to unearth genetic factors underpinning Erbb2-driven mammary tumour development and metastasis.
METHODS: 732 F1 hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains were monitored for mammary tumour phenotypes. GWAS pinpointed SNPs that influence various tumour phenotypes. Multivariate analyses and models were used to construct the polygenic score and to develop a mouse tumour susceptibility gene signature (mTSGS), where the corresponding human ortholog was identified and designated as hTSGS. The importance and clinical value of hTSGS in human BC was evaluated using public datasets, encompassing TCGA, METABRIC, GSE96058, and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validated in vivo using genetically diverse MMTV-Erbb2 mice.
RESULTS: Distinct variances in tumour onset, multiplicity, and metastatic patterns were observed in F1-hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified specific SNPs significantly associated with tumour onset, multiplicity, lung metastasis, and liver metastasis. Multivariate analyses flagged SNPs in 20 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b, Rer1, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a, Oxr1, Plxna2, and Tbc1d31) independently tied to various tumour characteristics, designated as a mTSGS. hTSGS scores (hTSGSS) based on their transcriptional level showed prognostic values, superseding clinical factors and PAM50 subtype across multiple human BC cohorts, and predicted pathological complete response independent of and superior to MammaPrint score in I-SPY2 study. The power of mTSGS score for predicting chemotherapy response was further validated in an in vivo mouse MMTV-Erbb2 model, showing that, like findings in human patients, mouse tumours with low mTSGS scores were most likely to respond to treatment.
CONCLUSIONS: Our investigation has unveiled many new genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that hTSGS holds promise as a biomarker for refining treatment strategies for patients with BC.
BACKGROUND: The U.S. Department of Defense (DoD) Breast Cancer Research Program (BCRP) (BC190820), United States; MCIN/AEI/10.13039/501100011039 (PID2020-118527RB-I00, PDC2021-121735-I00), the \"European Union Next Generation EU/PRTR,\" the Regional Government of Castile and León (CSI144P20), European Union.