Back

Scribble knockdown induced metastasis tracking, identification of its associated novel molecular candidates through Proteome and in silico studies

Srikrishna, S.; Singh, J.

2024-04-13 cancer biology
10.1101/2024.04.09.588727 bioRxiv
Show abstract

Metastasis is the primary cause of cancer associated death globally. Loss of function of Scribble, a cell polarity regulator/tumor suppressor gene, is associated with many forms of human cancer but its role in cell proliferation and metastasis remains unknown. We generated metastatic cancer in Drosophila using UAS-GAL4 system, through knockdown of Scribble in the wing imaginal discs and tracked metastasis events from 0hr early pupae to 84hrs late pupae using fluorescence microscope. Here, we report, for the first time, that knockdown of Scribble alone could lead to the development of primary tumor in the wing imaginal discs, which is capable of establishing metastasis, leading to secondary tumor formation, eventually resulting in absolute pupal lethality. Further, we checked a metastasis biomarker, MMP1 levels during pre-and post-metastatic phases in Drosophila pupae using qRT-PCR and Western blot analysis. In addition, we analyzed the proteome of Scribble knockdown induced tumor-bearing pupae by 2-D gel electrophoresis followed by MALDI-TOF MS to identify novel proteins involved in the process of tumorigenesis and metastasis. We identified six differentially expressed proteins, Obp 99b, Fer2LCH,CG13492, Hsp23, Ubiquitin and Colt in Scrib knockdown pupae compared to wild-type and validated their expression at the transcriptional level using qRT-PCR. In-silico studies show these novel protein interaction with Scrib. Thus, our results suggested that loss of Scrib alone causes metastasis, without the need for cooperative interaction with oncogenic Ras. The newly identified Fer2LCH (ferritin) and colt proteins could be important candidates for therapeutic target against Scrib associated cell proliferation and metastasis.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.