Anticancer Effect of a Single-Chain Variable Fragment Against Pro-Matrix Metalloproteinase-7 in Colon Cancer

Shinhye Min,Bohee Jang,Ji-Hye Yun, Hyeonju Yang,Jee Young Sung, Ga-Eun Lim,Yong-Nyun Kim, Weontae Lee,Eok-Soo Oh

MATRIX BIOLOGY（2025）

Ewha Womans Univ | Inst for Basic Sci Korea | Epinogen Ltd

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Abstract

Disrupting the interaction between matrix metalloproteinase-7 (MMP-7) and syndecan-2 (SDC-2) can yield anticancer effects in colon cancer cells. Here, a single-chain variable fragment (scFv) targeting the pro-domain of MMP-7 was generated as a potential candidate anticancer agent. Among the generated scFvs, those designated 1B7 and 1C3 showed the strongest abilities to inhibit the ability of MMP-7 pro-domain to directly interact with SDC-2 in vitro and decrease the cancer activities of human HT29 colon adenocarcinoma cells. Consistently, 1B7 and 1C3 inhibited the cell-surface localization of pro-MMP-7, reduced the gelatinolytic activity of MMP-7, and suppressed the cancer activities of metastatic HCT116 human colon carcinoma cells. Notably, 1B7 inhibited the primary tumor growth and lung metastasis of CT26 mouse colon cancer cells in a mouse model. Compared to 1B7, the 1B7-Fc fusion antibody showed better anti-tumorigenic activity against HCT116 cells in culture and a syngeneic mouse model. Together, these data suggest that 1B7-Fc exerts anticancer effects by interfering with the interaction of MMP-7 and SDC-2 and could be a promising therapeutic antibody for colon cancer.

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Key words

Matrix metalloproteinase-7,Single-chain variable fragment,Colon cancer,Syndecan-2

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
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Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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