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Abstract Background As the most malignant tumor of the female reproductive system, ovarian cancer (OC) has garnered increasing attention. The Warburg effect, driven by glycolysis, accounts for tumor cell proliferation under aerobic conditions. However, the metabolic heterogeneity linked to glycolysis in OC remains elusive. Methods We integrated single-cell data with OC to score glycolysis level in tumor cell subclusters. This led to the identification of a subcluster predominantly characterized by glycolysis, with a strong correlation to patient prognosis. Core transcription factors were pinpointed using hdWGCNA and metaVIPER. A specific transcription factor regulatory network was then constructed. A glycolysis-related prognostic model was developed and tested for estimating OC prognosis with a total of 85 machine-learning combinations, focusing on specific upregulated genes of two subtypes. We identified IGF2 as a key within the prognostic model and investigated its impact on OC progression and drug resistance through in vitro experiments, including the transwell assay, lactate production detection, and the CCK-8 assay. Results Analysis showed that the Malignant 7 subcluster was primarily related to glycolysis. Two OC molecular subtypes, CS1 and CS2, were identified with distinct clinical, biological, and microenvironmental traits. A prognostic model was built, and IGF2 emerged as a key gene linked to prognosis. Experiments have proven that IGF2 can promote the glycolysis pathway and the malignant biological progression of OC cells. Conclusions We developed two novel OC subtypes based on glycolysis score, established a stable prognostic model, and identified IGF2 as the marker gene. These insights provided a new avenue for exploring OC’s molecular mechanisms and personalized treatment approaches.

Abstract Background Ovarian cancer (OC) is the most malignant tumor with the worst prognosis in female reproductive system. Mitophagy and long non-coding RNAs (lncRNAs) play pivotal roles in tumorigenesis, development, and drug resistance. The effects of mitophagy-related lncRNAs on OC prognosis and therapeutic response remain unelucidated. Methods We retrieved OC-related RNA sequence, copy number variation, somatic mutation, and clinicopathological information from The Cancer Genome Atlas database and mitophagy-related gene sets from the Reactome database. Pearson’s correlation analysis was used to distinguish mitophagy-related lncRNAs. A prognostic lncRNA signature was constructed using UniCox, LASSO, and forward stepwise regression analysis. Individuals with a risk score above or below the median were classified as high- or low-risk groups, respectively. The risk model was analyzed using the Kaplan–Meier estimator, receiver operating characteristic curve, decision curve analysis, and Cox regression analysis and validated using an internal dataset. LINC00174 was validated in clinical samples and OC cell lines. We also reviewed reports on the role of LINC00174 in cancer. Subsequently, a nomogram model was constructed. Furthermore, the Genomics of Drug Sensitivity in Cancer database was used to explore the relationship between the risk model and anti-tumor drug sensitivity. Gene set variation analysis was performed to assess potential differences in biological functions between the two groups. Finally, a lncRNA prognostic signature-related competing endogenous RNA (ceRNA) network was constructed. Results The prognostic signature showed that patients in the high-risk group had a poorer prognosis. The nomogram exhibited satisfactory accuracy and predictive potential. LINC00174 mainly acts as an oncogene in cancer and is upregulated in OC; its knockdown inhibited the proliferation and migration, and promoted apoptosis of OC cells. High-risk patients were more insensitive to cisplatin and olaparib than low-risk patients. The ceRNA network may help explore the potential regulatory mechanisms of lncRNAs. Conclusion The mitophagy-related lncRNA signature can help estimate the survival and drug sensitivity, the ceRNA network may provide novel therapeutic targets for patients with OC.

Abstract Background Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. In this study, we aimed to construct a glycolysis-related prognostic model for ovarian cancer and analyze its relationship with the tumor microenvironment’s immune cell infiltration. Methods We obtained six glycolysis-related gene sets for gene set enrichment analysis (GSEA). Ovarian cancer data from The Cancer Genome Atlas (TCGA) database and two Gene Expression Omnibus (GEO) datasets were divided into two groups after removing batch effects. We compared the tumor environments' immune components in high-risk and low-risk groups and analyzed the correlation between glycolysis- and immune-related genes. Then, we generated and validated a predictive model for the prognosis of ovarian cancer using the glycolysis-related genes. Results Overall, 27/329 glycolytic genes were associated with survival in ovarian cancer, 8 of which showed predictive value. The tumor cell components in the tumor microenvironment did not differ between the high-risk and low-risk groups; however, the immune score differed significantly between groups. In total, 13/24 immune cell types differed between groups, including 10 T cell types and three other immune cell types. Eight glycolysis-related prognostic genes were related to the expression of multiple immune-related genes at varying degrees, suggesting a relationship between glycolysis and immune response. Conclusions We identified eight glycolysis-related prognostic genes that effectively predicted survival in ovarian cancer. To a certain extent, the newly identified gene signature was related to the tumor microenvironment, especially immune cell infiltration and immune-related gene expression. These findings provide potential biomarkers and therapeutic targets for ovarian cancer.

Abstract Background Ovarian cancer (OV) is the deadliest gynecological cancer. Transmembrane protein 119 (TMEM119) has been reported as oncogene in several human cancers. However, the function of TMEM119 in OV is still poorly known. Methods Western blot and qRT-PCR were used to analyze TMEM119 levels. Transwell assays, wound healing assays, CCK-8 assays and EdU cell proliferation assays were designed to explore the function and potential mechanism of TMEM119 in malignant biological behaviors in OV. Results TMEM119 was observed to be overexpressed in OV tissues and associated with poor survival in OV patients. Knockdown and overexpression experiments demonstrated that TMEM119 promoted proliferation, invasion, and migration in OV cells in vitro. TMEM119 mRNA expression was related to the pathways of focal adhesion according to Gene Set Enrichment Analyses and was correlated with the mRNA expression level of platelet-derived growth factor receptor beta (PDGFRB). TMEM119 exerted oncogenic effects partially by regulating the expression of PDGFRB and by activating the PI3K/AKT signaling pathway. Conclusions Collectively, our findings highlight the potential role of TMEM119 in the malignant biological behavior of OV, which may serve as a potential biomarker and a therapeutic candidate for OV.

Abstract Background Very few proteins encoded by the presumed non-coding RNA transcripts have been identified. Their cellular functions remain largely unknown. This study identifies the tumor-suppressor function of a novel microprotein encoded by the precursor of miR-34a. It consists of 133 amino acid residues, thereby named as miPEP133 (pri-microRNA encoded peptide 133). Methods We overexpressed miPEP133 in nasopharyngeal carcinoma (NPC), ovarian cancer and cervical cancer cell lines to determine its effects on cell growth, apoptosis, migration, or invasion. Its impact on tumor growth was evaluated in a xenograft NPC model. Its prognostic value was analyzed using NPC clinical samples. We also conducted western blot, immunoprecipitation, mass spectrometry, confocal microscopy and flow cytometry to determine the underlying mechanisms of miPEP133 function and regulation. Results miPEP133 was expressed in normal human colon, stomach, ovary, uterus and pharynx. It was downregulated in cancer cell lines and tumors. miPEP133 overexpression induced apoptosis in cancer cells and inhibited their migration and invasion. miPEP133 inhibited tumor growth in vivo. Low miPEP133 expression was an unfavorable prognostic marker associated with advanced metastatic NPC. Wild-type p53 but not mutant p53 induced miPEP133 expression. miPEP133 enhanced p53 transcriptional activation and miR-34a expression. miPEP133 localized in the mitochondria to interact with mitochondrial heat shock protein 70kD (HSPA9) and prevent HSPA9 from interacting with its binding partners, leading to the decrease of mitochondrial membrane potential and mitochondrial mass. Conclusion miPEP133 is a tumor suppressor localized in the mitochondria. It is a potential prognostic marker and therapeutic target for multiple types of cancers.

Abstract Background Ovarian cancer (OC) is the most malignant tumor in the female reproductive system. About 75% of OC in complete remission of clinical symptoms still develop a recurrence. Therefore, searching for new treatment methods plays an important role in improving the prognosis of OC. Methods We downloaded the MAF files, RNA-seq data and clinical information from the TCGA database. The “maftools” package in R software was used to visualize the OC mutation data. We calculated the tumor mutation burden (TMB) of OC and analyzed its correlation with clinicopathological parameters and prognostic value. Tumor mutation burden related signature model was constructed to predict the overall survival (OS) of OC. Results The results revealed that there was a statistical correlation between TMB and FIGO stage, grade and tumor residual size of ovarian cancer patients. The Kaplan–Meier curve indicated that a high TMB is associated with better clinical outcomes of OC. The difference analysis indicated 24 upregulated genes and 619 downregulated genes in the high-TMB group compared with the low-TMB group. Besides, the TMBRS model based on five hub genes (RBMS3, PLA2G5, CDH2, AMHR2 and ADAMTS8) was constructed to predict the OS of OC. The ROC curve and validation data sets all revealed that the TMBRS model was reliable in predicting recurrence risk. Immune microenvironment analysis indicated the correlations between TMB and infiltrating immune cells. Conclusions Our results suggest that TMB plays an important role in the prognosis and guiding immunotherapy of OC. By detecting the TMB of OC, clinicians can more accurately treat patients with immunotherapy, thereby improving their survival rate.

Abstract Background Traumatic brain injury (TBI) is a critical public health and socioeconomic problem throughout the world. Inflammation-induced secondary injury is one of the vital pathogenic parameters of TBI. Molecular signaling cascades of pyroptosis, a specific type of cellular necrosis, are key drivers of TBI-induced inflammation. Methods In this study, mice with genetically ablated caspase-1 (caspase-1−/−) were subjected to controlled cortical impact injury in vivo, and primary neuron deficient in caspase-1 through siRNA knockdown and pharmacologic inhibition was stimulated by mechanical scratch, equiaxial stretch, and LPS/ATP in vitro. We evaluated the effects of caspase-1 deficiency on neurological deficits, inflammatory factors, histopathology, cell apoptosis, and pyroptosis. Results During the acute post-injury period (0–48 h) in vivo, motor deficits, anti-inflammatory cytokines (TGF-β and IL-10), pro-inflammatory cytokines (IFN-γ, IL-1β, and IL-18), and blood lactate dehydrogenase (LDH), as well as pyroptosis-related proteins (caspase-1, caspase-1 fragments, caspase-11 and GSDMD), were increased. Caspase-1 was activated in the cortex of TBI mice. Inflammatory activation was more profound in injured wild-type mice than in caspase-1−/− mice. In vitro, mechanical scratch, equiaxial stretch, and LPS/ATP-induced neuron pyroptosis, apoptosis, LDH release, and increased expression of inflammatory factors. The effects of mechanical and inflammatory stress were reduced through inhibition of caspase-1 activity through siRNA knockdown and pharmacologic inhibition. Conclusion Collectively, these data demonstrate that pyroptosis is involved in neuroinflammation and neuronal injury after TBI, and ablation of caspase-1 inhibits TBI-induced pyroptosis. Our findings suggest that caspase-1 may be a potential target for TBI therapy.