Prognostic value of baseline genetic features and newly identified <i>TP53</i> mutations in advanced breast cancer

Zhang, Lanxin; Sun, Shutao; Zhao, Xiaotian; Li, Jingwen; Xu, Yang; Xu, Lingzhi; Song, Chen; Li, Na; Yu, Jie; Zhao, Shanshan; Yu, Pengfei; Fang, Fengqi; Xie, Jiping; Ji, Xuening; Yu, Rong; Ou, Qiuxiang; Zhao, Zuowei; Li, Man

doi:10.1002/1878-0261.13297

Cited by 4 publications

(6 citation statements)

References 52 publications

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“…This presumption is based on the understanding that overexpression or underexpression of certain genes can precipitate disease states ( Kastora et al 2022 ). Moreover, it has been demonstrated that some genes serve as biomarkers for specific diseases ( Tripathi et al 2011 , Zhang et al 2022 ), further supporting the notion that spatial proximity in tissue samples correlates with gene expression patterns related to disease conditions. Therefore, we incorporated the spatial relationships between the central patch and its eight neighboring patches (arranged in a 3 × 3 grid) into a loss function.…”

Section: Methodsmentioning

confidence: 58%

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data

Chen,

Lin,

Wang

et al. 2024

Bioinformatics

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Motivation Tissue context and molecular profiling are commonly used measures in understanding normal development and disease pathology. In recent years, the development of spatial molecular profiling technologies (e.g. spatial resolved transcriptomics) has enabled the exploration of quantitative links between tissue morphology and gene expression. However, these technologies remain expensive and time-consuming, with subsequent analyses necessitating high-throughput pathological annotations. On the other hand, existing computational tools are limited to predicting only a few dozen to several hundred genes, and the majority of the methods are designed for bulk RNA-seq. Results In this context, we propose HE2Gene, the first multi-task learning-based method capable of predicting tens of thousands of spot-level gene expressions along with pathological annotations from H&E-stained images. Experimental results demonstrate that HE2Gene is comparable to state-of-the-art methods and generalizes well on an external dataset without the need for re-training. Moreover, HE2Gene preserves the annotated spatial domains and has the potential to identify biomarkers. This capability facilitates cancer diagnosis and broadens its applicability to investigate gene-disease associations. Availability and implementation The source code and data information has been deposited at https://github.com/Microbiods/HE2Gene.

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Section: Methodsmentioning

confidence: 58%

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data

Chen,

Lin,

Wang

et al. 2024

Bioinformatics

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“…Our study revealed that gene mutations, such as TP53, occur commonly in both the cluster2 subtype of TNBC patients with better survival rates and the cluster3 subtype with the poorest survival rates. The TP53 gene mutation has been reported as a significant biomarker for tumorigenesis 35 . Furthermore, it is worth noting that the cluster2 subtype not only exhibited a higher frequency of mutation types and mutation rates but also presented unique ONP mutations.…”

Section: Discussionmentioning

confidence: 95%

“…The TP53 gene mutation has been reported as a significant biomarker for tumorigenesis. 35 Furthermore, it is worth noting that the cluster2 subtype not only exhibited a higher frequency of mutation types and mutation rates but also presented unique ONP mutations. Previous studies have suggested that the deletion or mutation of ONPs can promote the overexpression of neighboring genes.…”

Section: The Sensitivity Of Cluster2 and Cluster3 To Drugs Is Differentmentioning

confidence: 98%

Identification of Subtypes in Triple-negative Breast Cancer Based on Shared Genes Between Immunity and Cancer Stemness

Lv,

Lan,

Guo

2024

Journal of Immunotherapy

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The correlation between triple-negative breast cancer (TNBC) and genes related to immunity and cancer stemness, particularly shared genes, remains unclear. This study aimed to investigate the correlation of immunity and cancer stemness with the molecular subtyping and survival rates in TNBC using bioinformatics approaches. Differential gene analysis was conducted to identify TNBC-associated differentially expressed genes (DEGs). Cancer stem cell (CSC)-related genes were obtained using weighted gene coexpression network analysis. Immune-related gene sets were retrieved from the literature. Venn analysis was performed to identify the shared DEGs between immunity and cancer stemness in TNBC. Cluster analysis and survival analysis based on the expression of these genes were conducted to identify TNBC subtypes with significant survival differences. A total of 5259 TNBC-associated DEGs, 2214 CSC-related genes, 1793 immune-related genes, and 44 shared DEGs between immunity and cancer stemness were obtained. Among them, 3 shared DEGs were closely associated with TNBC survival rates (P<0.05). Cluster and survival analyses revealed that among 3 subtypes, cluster2 exhibited the best survival rate, and cluster3 showed the worst survival rate (P<0.05). Dendritic cells were highly infiltrated in cluster2, while plasma cells and resting mast cells were highly infiltrated in cluster3 (P<0.05). Genes shared by immunity and cancer stemness were capable of classifying TNBC samples. TNBC patients of different subtypes exhibited significant differences in immune profiles, genetic mutations, and drug sensitivity. These findings could provide new insights into the pathogenesis of TNBC, the immune microenvironment, and the selection of therapeutic targets for drug treatment.

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“…Although nuclear β-catenin is commonly used for SPN diagnosis, CD10, cyclin D1, vimentin, progesterone receptor, glutamine synthase, TFE3, SOX11, LEF1, FUS, WIF-1, CD56, CD138, CD200, CD99, and E-cadherin have also been explored as diagnostic markers. 6,10,[12][13][14][15][16][17] However, their sensitivity and specificity when used alone vary. Notably, some SPN cases exhibit negative staining "−" as one group and others as another group for comparative analysis; P(−/+) means to treat "−" and "+" as one group and others as another group for comparative analysis; P(−/+/++) means to treat "+++" as one group and others as another group for comparative analysis.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas

Liu,

et al. 2024

American Journal of Surgical Pathology

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The diagnosis of solid pseudopapillary neoplasm of the pancreas (SPN) can be challenging due to potential confusion with other pancreatic neoplasms, particularly pancreatic neuroendocrine tumors (NETs), using current pathological diagnostic markers. We conducted a comprehensive analysis of bulk RNA sequencing data from SPNs, NETs, and normal pancreas, followed by experimental validation. This analysis revealed an increased accumulation of peroxisomes in SPNs. Moreover, we observed significant upregulation of the peroxisome marker ABCD1 in both primary and metastatic SPN samples compared with normal pancreas and NETs. To further investigate the potential utility of ABCD1 as a diagnostic marker for SPN via immunohistochemistry staining, we conducted verification in a large-scale patient cohort with pancreatic tumors, including 127 SPN (111 primary, 16 metastatic samples), 108 NET (98 nonfunctional pancreatic neuroendocrine tumor, NF-NET, and 10 functional pancreatic neuroendocrine tumor, F-NET), 9 acinar cell carcinoma (ACC), 3 pancreatoblastoma (PB), 54 pancreatic ductal adenocarcinoma (PDAC), 20 pancreatic serous cystadenoma (SCA), 19 pancreatic mucinous cystadenoma (MCA), 12 pancreatic ductal intraepithelial neoplasia (PanIN) and 5 intraductal papillary mucinous neoplasm (IPMN) samples. Our results indicate that ABCD1 holds promise as an easily applicable diagnostic marker with exceptional efficacy (AUC=0.999, sensitivity=99.10%, specificity=100%) for differentiating SPN from NET and other pancreatic neoplasms through immunohistochemical staining.

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Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Cited by 4 publications

References 52 publications

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data

Identification of Subtypes in Triple-negative Breast Cancer Based on Shared Genes Between Immunity and Cancer Stemness

ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas

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