Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes

Identification and validation of a prognostic signature of drug resistance and mitochondrial energy metabolism-related differentially expressed genes for breast cancer

BACKGROUND

Drug resistance constitutes one of the principal causes of poor prognosis in breast cancer patients. Although cancer cells can maintain viability independently of mitochondrial energy metabolism, they remain reliant on mitochondrial functions for the synthesis of new DNA strands. This dependency underscores a potential link between mitochondrial energy metabolism and drug resistance. Hence, drug resistance and mitochondrial energy metabolism-related differentially expressed genes (DMRDEGs) may emerge as candidates for novel cancer biomarkers. This study endeavors to assess the viability of DMRDEGs as biomarkers or therapeutic targets for breast cancer.

METHODS

We utilized the DRESIS database and MSigDB to identify genes related to drug resistance. Additionally, we sourced genes associated with mitochondrial energy metabolism from GeneCards and extant literature. By merging these genes with differentially expressed genes observed in normal and tumor tissues from the TCGA-BRCA and GEO databases, we successfully identified the DMRDEGs. Employing unsupervised consensus clustering, we divided breast cancer patients into two distinct groups based on the DMRDEGs. Consequently, we identified four hub genes to formulate a prognostic model, applying Cox regression, LASSO regression, and Random Forest methods. Furthermore, we examined immune infiltration and tumor mutation burden of the genes within our model and scrutinized divergences in the immune microenvironment between high- and low-risk groups. Small hairpin RNA and lentiviral plasmids were designed for stable transfection of breast cancer cell lines MDA-MB-231 and HCC1806. By conducting clone formation, scratch test, transwell assays, cell viability assay and measurement of oxygen consumption we initiated a preliminary investigation into mechanistic roles of AIFM1.

RESULTS

We utilized DMRDEGs to develop a prognostic model that includes four mRNAs for breast cancer. This model combined with various clinical features and critical breast cancer facets, demonstrated remarkable efficacy in predicting patient outcomes. AIFM1 appeared to enhance the proliferation, migration, and invasiveness of breast cancer cell lines MDA-MB-231 and HCC1806. Moreover, by reducing oxygen consumption, it aids in the cancer cells' acquisition of drug resistance.

CONCLUSIONS

DMRDEGs hold promise as diagnostic markers and therapeutic targets for breast cancer. Among the associated mutated genes, ATP7B, FUS, AIFM1, and PPARG could serve as early diagnostic indicators, and notably, AIFM1 may present itself as a promising therapeutic target.

piR-26441 inhibits mitochondrial oxidative phosphorylation and tumorigenesis in ovarian cancer through m6A modification by interacting with YTHDC1

Ovarian cancer (OC) is a heterogeneous cancer. In contrast to other tumor cells, which rely primarily on aerobic glycolysis (Warburg effect) as their energy source, oxidative phosphorylation (OXPHOS) is also one of its major metabolic modes. Piwi-interacting RNAs (piRNAs) play a regulatory function in various biological processes in tumor cells. However, the role and mechanisms of piRNAs in OC and mitochondrial OXPHOS remain to be elucidated. Here, we found that piR-26441 was aberrantly downregulated in OC, and its overexpression suppressed the malignant features of OC cells and tumor growth in a xenograft model. Moreover, overexpression of piR-26441 significantly reduced mitochondrial OXPHOS levels in OC cells. Furthermore, piR-26441 directly binds to and upregulates the expression of YTHDC1 in OC cells. piR-26441 also increased m6A levels, thereby interacting with YTHDC1 to destabilize the mRNA of TSFM. The resultant TSFM loss reduced mitochondrial complex I activity and mitochondrial OXPHOS, leading to mitochondrial dysfunction in OC cells, increased reactive oxygen species levels, and thus, DNA damage and apoptosis in OC cells, thereby inhibiting OC progression. Additionally, ago-piR-26441 suppressed tumor growth and mitochondrial metabolism in the patient-derived organoid model. Altogether, piR-26441 could inhibit OC cell growth via the YTHDC1/TSFM signaling axis, underscoring its significant importance in the context of OC, as well as offering potential as a therapeutic target.

Senescence-Related LncRNAs: Pioneering Indicators for Ovarian Cancer Outcomes

In gynecological oncology, ovarian cancer (OC) remains the most lethal, highlighting its significance in public health. Our research focused on the role of long non-coding RNA (lncRNA) in OC, particularly senescence-related lncRNAs (SnRlncRNAs), crucial for OC prognosis. Utilizing data from the genotype-tissue expression (GTEx) and cancer genome Atlas (TCGA), SnRlncRNAs were discerned and subsequently, a risk signature was sculpted using co-expression and differential expression analyses, Cox regression, and least absolute shrinkage and selection operator (LASSO). This signature's robustness was validated through time-dependent receiver operating characteristics (ROC), and multivariate Cox regression, with further validation in the international cancer genome consortium (ICGC). Gene set enrichment analyses (GSEA) unveiled pathways intertwined with risk groups. The ROC, alongside the nomogram and calibration outcomes, attested to the model's robust predictive accuracy. Of particular significance, our model has demonstrated superiority over several commonly utilized clinical indicators, such as stage and grade. Patients in the low-risk group demonstrated greater immune infiltration and varied drug sensitivities compared to other groups. Moreover, consensus clustering classified OC patients into four distinct groups based on the expression of 17 SnRlncRNAs, showing diverse survival rates. In conclusion, these findings underscored the robustness and reliability of our model and highlighted its potential for facilitating improved decision-making in the context of risk assessment, and demonstrated that these markers potentially served as robust, efficacious biomarkers and prognostic tools, offering insights into predicting OC response to anticancer therapeutics.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-024-00163-z.

Glutamine metabolism prognostic index predicts tumour microenvironment characteristics and therapeutic efficacy in ovarian cancer

Mounting evidence has highlighted the multifunctional characteristics of glutamine metabolism (GM) in cancer initiation, progression and therapeutic regimens. However, the overall role of GM in the tumour microenvironment (TME), clinical stratification and therapeutic efficacy in patients with ovarian cancer (OC) has not been fully elucidated. Here, three distinct GM clusters were identified and exhibited different prognostic values, biological functions and immune infiltration in TME. Subsequently, glutamine metabolism prognostic index (GMPI) was constructed as a new scoring model to quantify the GM subtypes and was verified as an independent predictor of OC. Patients with low-GMPI exhibited favourable survival outcomes, lower enrichment of several oncogenic pathways, less immunosuppressive cell infiltration and better immunotherapy responses. Single-cell sequencing analysis revealed a unique evolutionary trajectory of OC cells from high-GMPI to low-GMPI, and OC cells with different GMPI might communicate with distinct cell populations through ligand-receptor interactions. Critically, the therapeutic efficacy of several drug candidates was validated based on patient-derived organoids (PDOs). The proposed GMPI could serve as a reliable signature for predicting patient prognosis and contribute to optimising therapeutic strategies for OC.

Protein expression and localization of ABC transporters in pancreatic adenocarcinoma: Prognostic role of ABCC8

BACKGROUND

ATP-binding cassette (ABC) transporters translocate various substances across cellular membranes. Their deregulation may cause cancer drug resistance or perturbations in the supply of building blocks for cancer cells and modify patients' prognosis. This study investigated protein expression and cellular localization of the previously suggested putative prognostic biomarkers - ABCB2/TAP1, ABCC7/CFTR, ABCC8/SUR1, and ABCD4 in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Protein expression and localization were assessed by immunohistochemistry in formalin-fixed paraffin-embedded primary tumor tissue blocks of 61 PDAC patients and associated with clinical data and the survival of patients.

RESULTS

No CFTR protein expression was observed in PDAC, while TAP1 and ABCC8 were expressed predominantly in the cytoplasm of tumor cells. Most samples (81 %) had detectable both membranous and cytoplasmic ABCD4 staining and 42 % had ABCD4 expressed in the apical orientation. Negative membranous ABCD4 staining was significantly more frequent in advanced stage III or IV tumors (p = 0.022). Small or medium counts of individual ABCC8-positive cells in the stroma surrounding tumor tubules were also more often found in stage III or IV (p = 0.044). Patients with moderate or strong ABCC8 cytoplasmic staining intensity in tumor cells had a 3.5-fold higher risk of disease progression than those with weak staining (p = 0.002).

CONCLUSIONS

The study shows for the first time that the cytoplasmic ABCC8 protein expression has prognostic value in PDAC.

A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning

BACKGROUND

Ovarian cancer ranks the leading cause of gynecologic cancer-related death in the United States and the fifth most common cause of cancer-related mortality among American women. Increasing evidences have highlighted the vital role of macrophages M2/M1 proportion in tumor progression, prognosis and immunotherapy.

METHODS

Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers. Integrative procedure including 10 machine learning algorithms were performed to develop a prognostic macrophage related signature (MRS) with TCGA, GSE14764, GSE140082 datasets. The role of MRS in tumor microenvironment (TME) and therapy response was evaluated with the data of CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC, GSE91061 and IMvigor210 dataset.

RESULTS

The optimal MRS developed by the combination of CoxBoost and StepCox[forward] algorithm served as an independent risk factor in ovarian cancer. Compared with stage, grade and other established prognostic signatures, the current MRS had a better performance in predicting the overall survival rate of ovarian cancer patients. Low risk score indicated a higher TME score, higher level of immune cells, higher immunophenoscore, higher tumor mutational burden, lower TIDE score and lower IC50 value in ovarian cancer. The survival prediction nomogram had a good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of ovarian cancer patients.

CONCLUSION

All in all, the current study constructed a powerful prognostic MRS for ovarian cancer patients using 10 machine learning algorithms. This MRS could predict the prognosis and drug sensitivity in ovarian cancer.