Identification of two-dimensional copper signatures in human blood for bladder cancer with machine learning

Metal-detection based techniques and their applications in metallobiology

Metals are essential for human health and play a crucial role in numerous biological processes and pathways. Gaining a deeper insight into these biological events will facilitate novel strategies for disease prevention, early detection, and personalized treatment. In recent years, there has been significant progress in the development of metal-detection based techniques from single cell metallome and proteome profiling to multiplex imaging, which greatly enhance our comprehension of the intricate roles played by metals in complex biological systems. This perspective summarizes the recent progress in advanced metal-detection based techniques and highlights successful applications in elucidating the roles of metals in biology and medicine. Technologies including machine learning that couple with single-cell analysis such as mass cytometry and their application in metallobiology, cancer biology and immunology are also emphasized. Finally, we provide insights into future prospects and challenges involved in metal-detection based techniques, with the aim of inspiring further methodological advancements and applications that are accessible to chemists, biologists, and clinicians.

Correlation between S100A7 and immune characteristics, methylation, tumor stemness and tumor heterogeneity in pan-cancer and its role in chemotherapy resistance in breast cancer

OBJECTIVE

To explore the relationships between S100A7 and the immune characteristics, tumor heterogeneity, and tumor stemness pan-cancer as well as the effect of S100A7 on chemotherapy sensitivity in breast cancer.

METHODS

TCGA-BRCA and TCGA-PANCANCER RNA-seq data and clinical follow-up survival data were collected from the University of California Santa Cruz database. Survival analyses were performed to explore the relationship between S100A7 expression and pan-cancer prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and Gene Set Enrichment Analysis (GSEA) were used to identify the potential pathways related to the differentially expressed genes in breast cancer. Spearman's and Wilcoxon's tests were used to investigate the relationships between S100A7 expression and immune characteristics, methylation, tumor heterogeneity, and tumor stemness. The potential functions of S100A7 and its influence on chemotherapy sensitivity in breast cancer were elucidated using reverse transcription-quantitative PCR, Cell Counting Kit-8 (CCK-8) assay, Transwell assay, and wound healing assay.

RESULTS

S100A7 was highly expressed in most types of tumors and was associated with poor prognosis. S100A7 was closely associated with immunomodulators, immune checkpoint and immune cell infiltration. Further, S100A7 was related to tumor mutational burden, tumor heterogeneity, methylation and tumor stemness in breast cancer. High S100A7 expression was associated with the invasiveness, migration, proliferation and chemotherapy resistance of breast cancer cells in vitro experiments.

CONCLUSION

High S100A7 expression was related with poor prognosis and chemotherapy resistance in breast cancer, making it a potential immune and chemotherapy resistance biomarker.

Metal-binding amino acid ligands commonly found in metalloproteins differentially fractionate copper isotopes

Copper (Cu) is a cofactor in numerous key proteins and, thus, an essential element for life. In biological systems, Cu isotope abundances shift with metabolic and homeostatic state. However, the mechanisms underpinning these isotopic shifts remain poorly understood, hampering use of Cu isotopes as biomarkers. Computational predictions suggest that isotope fractionation occurs when proteins bind Cu, with the magnitude of this effect dependent on the identity and arrangement of the coordinating amino acids. This study sought to constrain equilibrium isotope fractionation values for Cu bound by common amino acids at protein metal-binding sites. Free and bound metal ions were separated via Donnan dialysis using a cation-permeable membrane. Isotope ratios of pre- and post-dialysis solutions were measured by MC-ICP-MS following purification. Sulfur ligands (cysteine) preferentially bound the light isotope (63Cu) relative to water (Δ65Cucomplex-free = - 0.48 ± 0.18‰) while oxygen ligands favored the heavy isotope (65Cu; + 0.26 ± 0.04‰ for glutamate and + 0.16 ± 0.10‰ for aspartate). Binding by nitrogen ligands (histidine) imparted no isotope effect (- 0.01 ± 0.04‰). This experimental work unequivocally demonstrates that amino acids differentially fractionate Cu isotopes and supports the hypothesis that metalloprotein biosynthesis affects the distribution of transition metal isotopes in biological systems.

Cuproptosis: Harnessing Transition Metal for Cancer Therapy

Transition metal elements, such as copper, play diverse and pivotal roles in oncology. They act as constituents of metalloenzymes involved in cellular metabolism, function as signaling molecules to regulate the proliferation and metastasis of tumors, and are integral components of metal-based anticancer drugs. Notably, recent research reveals that excessive copper can also modulate the occurrence of programmed cell death (PCD), known as cuprotosis, in cancer cells. This modulation occurs through the disruption of tumor cell metabolism and the induction of proteotoxic stress. This discovery uncovers a mode of interaction between transition metals and proteins, emphasizing the intricate link between copper homeostasis and tumor metabolism. Moreover, they provide innovative therapeutic strategies for the precise diagnosis and treatment of malignant tumors. At the crossroads of chemistry and oncology, we undertake a comprehensive review of copper homeostasis in tumors, elucidating the molecular mechanisms underpinning cuproptosis. Additionally, we summarize current nanotherapeutic approaches that target cuproptosis and provide an overview of the available laboratory and clinical methods for monitoring this process. In the context of emerging concepts, challenges, and opportunities, we emphasize the significant potential of nanotechnology in the advancement of this field.

Machine learning-aided metallomic profiling in serum and urine of thyroid cancer patients and its environmental implications

The incidence rate of thyroid cancer has been growing worldwide. Thyroid health is closely related with multiple trace metals, and the nutrients are essential in maintaining thyroid function while the contaminants can disturb thyroid morphology and homeostasis. In this study, we conducted metallomic analysis in thyroid cancer patients (n = 40) and control subjects (n = 40) recruited in Shenzhen, China with a high incidence of thyroid cancer. We found significant alterations in serumal and urinary metallomic profiling (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, I, Ba, Tl, and Pb) and elemental correlative patterns between thyroid cancer patients and controls. Additionally, we also measured the serum Cu isotopic composition and found a multifaceted disturbance in Cu metabolism in thyroid disease patients. Based on the metallome variations, we built and assessed the thyroid cancer-predictive performance of seven machine learning algorithms. Among them, the Random Forest model performed the best with the accuracy of 1.000, 0.858, and 0.813 on the training, 5-fold cross-validation, and test set, respectively. The high performance of machine learning has demonstrated the great promise of metallomic analysis in the identification of thyroid cancer. Then, the Shapley Additive exPlanations approach was used to further interpret the variable contributions of the model and it showed that serum Pb contributed the most in the identification process. To the best of our knowledge, this is the first study that combines machine learning and metallome data for cancer identification, and it supports the indication of environmental heavy metal-related thyroid cancer etiology.

Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells

Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δ⁶⁵Cu) with autistic serum. SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δ⁶⁵Cu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.

Natural copper isotopic abnormity in maternal serum at early pregnancy associated to risk of spontaneous preterm birth

Spontaneous preterm birth (SPB) has drawn public attention due to its increasing incidence and adverse effects on fetal growth. Effect of copper (Cu) imbalance in maternal bodies on the risk of SPB remains a subject of debate, and the related mechanisms are still unraveled. Here we applied natural stable copper isotopes to explore the underlying association and mechanism of copper imbalance with SPB using a nested case-control study. We collected maternal sera at the early pregnancy stage and then measured their copper isotopic ratio (⁶⁵Cu/⁶³Cu, expressed as δ⁶⁵Cu) as well as physiological and biochemical indexes from women with and without delivering SPB. We found that SPB cases had no significant difference in serum copper level from their controls, but their serum copper was significantly isotopically heavier than the controls (δ⁶⁵Cu value = 0.15 ± 0.34 ‰ versus -0.15 ± 0.17 ‰, P = 0.0149). Compared with the controls with lower δ⁶⁵Cu values, the crude odds ratio (OR) associated with SPB risk increased to 4.00 (95 % confidence interval (CI): 1.37-11.70) and the adjusted OR reached up to 11.35 (95 % CI: 1.35-95.60). Furthermore, via the copper isotopic fractionation, we revealed that dietary intake and blood ceruloplasmin may play more important roles than blood lipids and mother-to-child transmission in the copper imbalance associated with SPB. Further studies will be needed to understand the mechanisms of isotope fractionation related to reproductive health.

Multi-omics analysis of the oncogenic value of copper Metabolism-Related protein COMMD2 in human cancers

BACKGROUND

The copper metabolism MURR1 domain (COMMD) protein family is involved in tumorigenicity of malignant tumors. However, as the member of COMMD, the role of COMMD2 in human tumors remains unknown.

METHODS

We used The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), Human Protein Atlas (HPA) database, Cancer Cell Line Encyclopedia (CCLE) platform, univariate Cox regression analysis, Kaplan-Meier curve, cBioPortal, UALCAN database, Sangerbox online platform, GSCA database gene set enrichment analysis (GSEA), and GeneMANIA to analyze the expression of COMMD2, its prognostic values, genomic alteration patterns, and the correlation with tumor stemness, tumor mutational burden (TMB), microsatellite instability (MSI), and immune infiltrates, drug sensitivity, and gene function enrichment in pan-cancer. qRT-PCR, CCK-8, EdU, wound healing, and transwell migration assays were performed to confirm the function of COMMD2.

RESULTS

COMMD2 was strongly expressed in most cancer types. Elevated COMMD2 expression affects the prognosis, clinicopathological stage, and molecular or immune subtypes of various tumors. Moreover, promoter hypomethylation and mutations in the COMMD2 gene may be associated with its high expression and poor survival. Additionally, we discovered that COMMD2 expression was linked to tumor stemness, TMB, MSI, immune cell infiltration, immune-checkpoint inhibitors, and drug sensitivity in pan-cancer. Furthermore, the COMMD2 gene co-expression network is constructed with GSEA analysis, displaying significant interaction of COMMD2 with E2F targets, G2-M checkpoint, and mitotic spindle in bladder cancer (BLCA). Finally, RNA interference data showed suppression of COMMD2 prevented proliferation and migration of BLCA and uterine corpus endometrial carcinoma (UCEC) cells.

CONCLUSION

Our findings shed light on the COMMD2 functions in human cancers and demonstrate that it is a promising prognostic biomarker and therapeutic target in pan-cancer.