Secretory leukocyte protease inhibitor (SLPI) in cancer pathophysiology: Mechanisms of action and clinical implications

Zhang, Xiaohua; Liu, Shan Shan; Ma, Jinjin; Qu, Wei

doi:10.1016/j.prp.2023.154633

Cited by 3 publications

(1 citation statement)

References 138 publications

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“…Secondly, the HER2-low signature includes genes that are outside the ER transcriptional network, previously described in breast cancer or other malignancies, and with a potential as molecular targets. Among these, SLPI (Secretory Leukocyte Protease Inhibitor) is involved in inflammation inhibition, immune response modulation, and cell proliferation promotion 31 , and is found to be elevated in triple-negative breast cancer patients with poor prognosis 32 . A2M (Alpha-2-Macroglobulin) has been shown to affect adhesion, migration, and growth by inhibiting signaling pathways such as PI3K/AKT and SMAD, while also increasing PTEN levels by down-regulating miR-21 33 .…”

Section: Discussionmentioning

confidence: 99%

A gene expression-based classifier for HER2-low breast cancer

Di Cosimo,

Pizzamiglio,

Ciniselli

et al. 2024

Sci Rep

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In clinical trials evaluating antibody-conjugated drugs (ADCs), HER2-low breast cancer is defined through protein immunohistochemistry scoring (IHC) 1+ or 2+ without gene amplification. However, in daily practice, the accuracy of IHC is compromised by inter-observer variability. Herein, we aimed to identify HER2-low breast cancer primary tumors by leveraging gene expression profiling. A discovery approach was applied to gene expression profile of institutional INT1 (n = 125) and INT2 (n = 84) datasets. We identified differentially expressed genes (DEGs) in each specific HER2 IHC category 0, 1+, 2+ and 3+. Principal Component Analysis was used to generate a HER2-low signature whose performance was evaluated in the independent INT3 (n = 95), and in the publicly available TCGA and GSE81538 datasets. The association between the HER2-low signature and HER2 IHC categories was evaluated by Kruskal–Wallis test with post hoc pair-wise comparisons. The HER2-low signature discriminatory capability was assessed by estimating the area under the receiver operating characteristic curve (AUC). Gene Ontology and KEGG analyses were performed to evaluate the HER2-low signature genes functional enrichment. A HER2-low signature was computed based on HER2 IHC category-specific DEGs. The twenty genes included in the signature were significantly enriched with lipid and steroid metabolism pathways, peptidase regulation, and humoral immune response. The HER2-low signature values showed a bell-shaped distribution across IHC categories (low values in 0 and 3+; high values in 1+ and 2+), effectively distinguishing HER2-low from 0 (p < 0.001) to 3+ (p < 0.001). Notably, the signature values were higher in tumors scored with 1+ as compared to 0. The HER2-low signature association with IHC categories and its bell-shaped distribution was confirmed in the independent INT3, TCGA and GSE81538 datasets. In the combined INT1 and INT3 datasets, the HER2-low signature achieved an AUC value of 0.74 (95% confidence interval, CI 0.67–0.81) in distinguishing HER2-low vs. the other categories, outperforming the individual ERBB2 mRNA AUC value of 0.52 (95% CI 0.43–0.60). These results represent a proof-of-concept for an observer-independent gene-expression-based classifier of HER2-low status. The herein identified 20-gene signature shows promise in distinguishing between HER2 0 and HER2-low expressing tumors, including those scored as 1+ at IHC, and in developing a selection approach for ADCs candidates.

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Section: Discussionmentioning

confidence: 99%

A gene expression-based classifier for HER2-low breast cancer

Di Cosimo,

Pizzamiglio,

Ciniselli

et al. 2024

Sci Rep

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The secretory leukocyte protease inhibitor (SLPI) in pathophysiology of non-communicable diseases: Evidence from experimental studies to clinical applications

Mongkolpathumrat,

Pikwong,

Phutiyothin

et al. 2024

Heliyon

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Screening of genes characteristic of pancreatic cancer by LASSO regression combined with support vector machine and recursive feature elimination, and immune correlation analysis

Zeng,

Chen

2024

J Int Med Res

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Background Pancreatic cancer is a malignant tumor of the digestive tract that shows increased mortality, recurrence, and morbidity year on year. Methods Differentially expressed genes between pancreatic cancer and healthy tissues were first analyzed from four datasets within the Gene Expression Omnibus (GEO). Gene ontology, disease ontology, and gene set enrichment analysis of differentially expressed genes were performed, and genes identified as characteristic of pancreatic cancer were screened using LASSO regression combined with support vector machine and recursive feature elimination (SVM–RFE). Differential analysis and receiver operating characteristic curve analysis were performed on the identified eigengenes, and validation was carried out using another dataset from the GEO database. Differences and correlations between characteristic pancreatic cancer genes and immune cells were analyzed. Results A total of 90 differentially expressed genes were identified by screening, and six genes characteristic of pancreatic cancer were obtained by taking the intersection of two characteristic genes identified by machine learning. Immunoassays yielded multiple immune cells associated with pancreatic cancer signature genes. Conclusion The six characteristic genes screened by a combination of LASSO regression and SVM–RFE are potential new biomarkers for the early diagnosis and prognosis of pancreatic cancer, and could be a novel therapeutic target.

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Secretory leukocyte protease inhibitor (SLPI) in cancer pathophysiology: Mechanisms of action and clinical implications

Cited by 3 publications

References 138 publications

A gene expression-based classifier for HER2-low breast cancer

A gene expression-based classifier for HER2-low breast cancer

The secretory leukocyte protease inhibitor (SLPI) in pathophysiology of non-communicable diseases: Evidence from experimental studies to clinical applications

Screening of genes characteristic of pancreatic cancer by LASSO regression combined with support vector machine and recursive feature elimination, and immune correlation analysis

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