Targeting PAX8 sensitizes ovarian cancer cells to ferroptosis by inhibiting glutathione synthesis.

Ovarian cancer is a malignant tumor originating from the ovary, characterized by its high mortality rate and propensity for recurrence. In some patients, especially those with recurrent cancer, conventional treatments such as surgical resection or standard chemotherapy yield suboptimal results. Consequently, there is an urgent need for novel anti-cancer therapeutic strategies. Ferroptosis is a distinct form of cell death separate from apoptosis. Ferroptosis inducers have demonstrated promising potential in the treatment of ovarian cancer, with evidence indicating their ability to enhance ovarian cancer cell sensitivity to cisplatin. However, resistance of cancer cells to ferroptosis still remains an inevitable challenge. Here, we analyzed genome-scale CRISPR-Cas9 loss-of function screens and identified PAX8 as a ferroptosis resistance protein in ovarian cancer. We identified PAX8 as a susceptibility gene in GPX4-dependent ovarian cancer. Depletion of PAX8 rendered GPX4-dependent ovarian cancer cells significantly more sensitive to GPX4 inhibitors. Additionally, we found that PAX8 inhibited ferroptosis in ovarian cancer cells. Combined treatment with a PAX8 inhibitor and RSL3 suppressed ovarian cancer cell growth, induced ferroptosis, and was validated in a xenograft mouse model. Further exploration of the molecular mechanisms underlying PAX8 inhibition of ferroptosis mutations revealed upregulation of glutamate-cysteine ligase catalytic subunit (GCLC) expression. GCLC mediated the ferroptosis resistance induced by PAX8 in ovarian cancer. In conclusion, our study underscores the pivotal role of PAX8 as a therapeutic target in GPX4-dependent ovarian cancer. The combination of PAX8 inhibitors such as losartan and captopril with ferroptosis inducers represents a promising new approach for ovarian cancer therapy.

Development and validation of ferroptosis-related lncRNAs signature for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) with high heterogeneity is one of the most frequent malignant tumors throughout the world. However, there is no research to establish a ferroptosis-related lncRNAs (FRlncRNAs) signature for the patients with HCC. Therefore, this study was designed to establish a novel FRlncRNAs signature to predict the survival of patients with HCC. METHOD: The expression profiles of lncRNAs were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. FRlncRNAs co-expressed with ferroptosis-related genes were utilized to establish a signature. Cox regression was used to construct a novel three FRlncRNAs signature in the TCGA cohort, which was verified in the GEO validation cohort. RESULTS: Three differently expressed FRlncRNAs significantly associated with prognosis of HCC were identified, which composed a novel FRlncRNAs signature. According to the FRlncRNAs signature, the patients with HCC could be divided into low- and high-risk groups. Patients with HCC in the high-risk group displayed shorter overall survival (OS) contrasted with those in the low-risk group (P < 0.001 in TCGA cohort and P = 0.045 in GEO cohort). This signature could serve as a significantly independent predictor in Cox regression (multivariate HR > 1, P < 0.001), which was verified to a certain extent in the GEO cohort (univariate HR > 1, P < 0.05). Meanwhile, it was also a useful tool in predicting survival among each stratum of gender, age, grade, stage, and etiology,etc. This signature was connected with immune cell infiltration (i.e., Macrophage, Myeloid dendritic cell, and Neutrophil cell, etc.) and immune checkpoint blockade targets (PD-1, CTLA-4, and TIM-3). CONCLUSION: The three FRlncRNAs might be potential therapeutic targets for patients, and their signature could be utilized for prognostic prediction in HCC.

A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most universal malignant liver tumors worldwide. However, there were no systematic studies to establish glycolysis­related gene pairs (GRGPs) signatures for the patients with HCC. Therefore, the study aimed to establish novel GRGPs signatures to better predict the prognosis of HCC. METHODS: Based on the data from Gene Expression Omnibus, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium databases, glycolysis-related mRNAs were used to construct GRGPs. Cox regression was applied to establish a seventeen GRGPs signature in TCGA dataset, which was verified in two validation (European and American, and Asian) datasets. RESULTS: Seventeen prognostic GRGPs (HMMR_PFKFB1, CHST1_GYS2, MERTK_GYS2, GPC1_GYS2, LDHA_GOT2, IDUA_GNPDA1, IDUA_ME2, IDUA_G6PD, IDUA_GPC1, MPI_GPC1, SDC2_LDHA, PRPS1_PLOD2, GALK1_IER3, MET_PLOD2, GUSB_IGFBP3, IL13RA1_IGFBP3 and CYB5A_IGFBP3) were identified to be significantly progressive factors for the patients with HCC in the TCGA dataset, which constituted a GRGPs signature. The patients with HCC were classified into low-risk group and high-risk group based on the GRGPs signature. The GRGPs signature was a significantly independent prognostic indicator for the patients with HCC in TCGA (log-rank P = 2.898e-14). Consistent with the TCGA dataset, the patients in low-risk group had a longer OS in two validation datasets (European and American: P = 1.143e-02, and Asian: P = 6.342e-08). Additionally, the GRGPs signature was also validated as a significantly independent prognostic indicator in two validation datasets. CONCLUSION: The seventeen GRGPs and their signature might be molecular biomarkers and therapeutic targets for the patients with HCC.

Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma.

There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR = 1.088, 95%CI = 1.057 - 1.120; P < 0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.