What question did this study set out to answer?

The aim is to identify key genes associated with progesterone resistance in endometrial cancer using CRISPR technology.

March 5, 2026Open Access

Construction and initial validation of key gene network for progesterone resistance in endometrial cancer based on genome-wide CRISPR screening

Puntos clave

The aim is to identify key genes associated with progesterone resistance in endometrial cancer using CRISPR technology.
Established a gene library using CRISPR/Cas9 in Ishikawa cell line.
Conducted high-throughput sequencing after exposure to medroxyprogesterone.
Performed differential gene expression and enrichment analyses using MAGeCK RRA and MLE algorithms.
Validated top genes in progestin-resistant and sensitive cell models through CCK-8 assays.
Validated findings in vivo using xenograft mouse models.
Identified 5 genes that promote progestin resistance and 20 genes that inhibit resistance.
Enrichment analysis linked identified genes to DNA and RNA synthesis and metabolism.
332 negative selection genes and 3438 positive selection genes were identified.
High-quality gene library met sequencing standards.

Resumen

Endometrial carcinoma, a prevailing malignancy of the female reproductive system, exhibits escalating incidence and a trend towards early onset. Hormone therapy serves as a primary choice for fertility preservation and is also considered for advanced and recurrent cases. However, a considerable number of patients fail to respond favorably to progestin treatments. We employed CRISPR/Cas9 technology to establish a comprehensive human genome library in the Ishikawa cell line. Subsequent exposure to medroxyprogesterone was followed by high-throughput sequencing, and differential gene expression and enrichment analyses were conducted using Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout (MAGeCK) Robust Rank Aggregation (RRA) and MAGeCK Maximum-Likelihood Estimation (MLE) algorithms. An iterative data intersection approach was employed, utilizing sequenced data from progestin-resistant cell lines, to identify pivotal genes associated with progestin resistance. The top 10 identified genes were functionally validated in our previously established progestin-resistant cell model through Cell Counting Kit-8 (CCK-8) assays and apoptosis detection. The progestin-resistant gene NNMT and the progestin-sensitive gene SOX17 were validated in vivo in xenograft mouse models. The constructed library exhibited high quality, meeting sequencing standards. Employing RRA and MLE algorithms, we identified 332 and 829 negative selection genes, as well as 3438 and 5098 positive selection genes. Enrichment analysis implicated pathways linked to DNA and RNA synthesis, metabolism, and related processes. After multiple data intersections, we identified a total of 5 genes promoting progestin resistance and 20 genes inhibiting resistance, with functional experiments confirming their roles. Employing CRISPR/Cas9 technology enables the construction of a relatively reliable network of pivotal genes associated with progestin resistance in endometrial carcinoma. Processes involving DNA and RNA synthesis, metabolism, and related mechanisms appear to significantly impact the progestin sensitivity of endometrial carcinoma.

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