Histone Methylation in Nickel-Smelting Industrial Workers

<div><p>Background</p><p>Nickel is an essential trace metal naturally found in the environment. It is also common in occupational settings, where it associates with various levels of both occupational and nonoccupational exposure <i>In vitro</i> studies have shown that nickel exposure can lead to intracellular accumulation of Ni²⁺, which has been associated with global decreases in DNA methylation, increases in chromatin condensation, reductions in H3K9me2, and elevated levels of H3K4me3. Histone modifications play an important role in modulating chromatin structure and gene expression. For example, tri-methylation of histone H3k4 has been found to be associated with transcriptional activation, and tri-methylation of H3k27 has been found to be associated with transcriptional repression. Aberrant histone modifications have been found to be associated with various human diseases, including cancer. The purpose of this work was to identify biomarkers for populations with occupational nickel exposure and to examine the relationship between histone methylation and nickel exposure. This may provide a scientific indicator of early health impairment and facilitate exploration of the molecular mechanism underlying cancer pathogenesis.</p><p>Methods</p><p>One hundred and forty subjects with occupational exposure to Ni and 140 referents were recruited. H3K4 and H3K27 trimethylation levels were measured in subjects’ blood cells.</p><p>Results</p><p>H3K4me3 levels were found to be higher in nickel smelting workers (47.24±20.85) than in office workers (22.65±8.81; <i>P</i> = 0.000), while the opposite was found for levels of H3K27me3(nickel smelting workers, 13.88± 4.23; office workers, 20.67± 5.96; <i>P</i> = 0.000). H3K4me3 was positively (r = 0.267, <i>P</i> = 0.001) and H3K27 was negatively (r = -0.684, <i>P</i> = 0.000) associated with age and length of service in smelting workers.</p><p>Conclusion</p><p>This study indicated that occupational exposure to Ni is associated with alterations in levels of histone modification.</p></div

H3K4me3 in nickel smelting and office workers with different lengths of service (nmol/mg prot).

<p>Note: *<b><i>P</i></b> < 0.05 was here considered statistically significant.</p><p>H3K4me3 in nickel smelting and office workers with different lengths of service (nmol/mg prot).</p

Trend of H3K27me3 with increasing working years.

<p>The trend of H3k27me3 in two group with incressing working years, the line connected with red balls express the smelting workers, and with green triangles express the office workers.</p

H3K27me3 in nickel smelting and office workers with different lengths of service (nmol/mg prot).

<p>Note: *<b><i>P</i></b> <0.05, statistically significant.</p><p>H3K27me3 in nickel smelting and office workers with different lengths of service (nmol/mg prot).</p

Sampling distribution of high nickel exposure in groups with different lengths of service.

<p>Sampling distribution of high nickel exposure in groups with different lengths of service.</p

H3K27me3 in smelting and office workers in different working years.

<p>The level of H3k27me3 in every experimental subject, the red balls express the smelting workers, and green triangles express the office workers.</p

H3K4me3 in smelting and office workers with different working years.

<p>The level of H3k4me3 in every experimental subject, the red balls express the smelting workers, and green triangles express the office workers.</p

Trend of H3K4me3 with increasing working years.

<p>The trend of H3k4me3 in two group with incressing working years, the line connected with red balls express the smelting workers, and with green triangles express the office workers.</p

Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.

Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960 ± 0.01, and 0.771 ± 0.024 to 0.904 ± 0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the "digital grade" cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value < 0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology