Construction and validation of a transcription factors-based prognostic signature for ovarian cancer

Machine learning constructs a T cell-related signature for predicting prognosis and drug sensitivity in ovarian cancer

BACKGROUND

The leading cause of death related to gynecologic cancer is ovarian cancer, which typically has a poor prognosis. T cells are referred to as key mediators of immunosurveillance and tumor eradication, and unbalanced regulation or lack of T cells in tumors result in immunotherapy resistance.

METHODS

The identification of T cell related markers depended on single-cell RNA-seq analysis. Using data from multiple datasets, including TCGA, GSE14764, GSE26193, GSE26712, and GSE140082, we constructed a prognostic signature called TRS (T cell-related signature) using 10 different machine learning algorithms. The correlation between TRS and drug sensitivity were analyzed using the data from GSE91061 and IMvigor210 dataset.

RESULTS

PlsRcox method based TRS was as a risk factor for the clinical outcome of ovarian cancer patients. In comparison with stage, grade and many prognostic signatures, the performance of our TRS in evaluating the clinical outcome was better in ovarian cancer. TRS-based risk score showed distinct association with the level of ESTIMATE score, immune-related function score and immune cells. Moreover, TRS could be used to predict the immunotherapy response and chemotherapy response in ovarian cancer.

CONCLUSION

In conclusion, we constructed a powerful TRS in ovarian cancer, which could accurately predict the clinical outcome of patients and be used to predict the immunotherapy response and chemotherapy response.

Transcription factors-related molecular subtypes and risk prognostic model: exploring the immunogenicity landscape and potential drug targets in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer, with a high mortality rate and poor prognosis. Mutated or dysregulated transcription factors (TFs) are significantly associated with carcinogenesis. The aim of this study was to develop a TF-related prognostic risk model to predict the prognosis and guide the treatment of HCC patients.

METHODS

RNA sequencing data were obtained from the TCGA database. The ICGC and GEO databases were used as validation datasets. The consensus clustering algorithm was used to classify the molecular subtypes of TFs. Kaplan‒Meier survival analysis and receiver operating characteristic (ROC) analysis were applied to evaluate the prognostic value of the model. The immunogenic landscape differences of molecular subtypes were evaluated by the TIMER and xCell algorithms. Autodock analysis was used to predict possible binding sites of trametinib to TFs. RT‒PCR was used to verify the effect of trametinib on the expression of core TFs.

RESULTS

According to the differential expression of TFs, HCC samples were divided into two clusters (C1 and C2). The survival time, signaling pathways, abundance of immune cell infiltration and responses to chemotherapy and immunotherapy were significantly different between C1 and C2. Nine TFs with potential prognostic value, including HMGB2, ESR1, HMGA1, MYBL2, TCF19, E2F1, FOXM1, CENPA and ZIC2, were identified by Cox regression analysis. HCC patients in the high-risk group had a poor prognosis compared with those in the low-risk group (p < 0.001). Moreover, the area under the ROC curve (AUC) values of the 1-year, 2-year and 3-year survival rates were 0.792, 0.71 and 0.695, respectively. The risk model was validated in the ICGC database. Notably, trametinib sensitivity was highly correlated with the expression of core TFs, and molecular docking predicted the possible binding sites of trametinib with these TFs. More importantly, the expression of core TFs was downregulated under trametinib treatment.

CONCLUSIONS

A prognostic signature with 9 TFs performed well in predicting the survival rate and chemotherapy/immunotherapy effect of HCC patients. Trimetinib has potential application value in HCC by targeting TFs.

Machine learning developed a PI3K/Akt pathway-related signature for predicting prognosis and drug sensitivity in ovarian cancer

BACKGROUND

Ovarian cancer is one of the deadliest malignancies among females, generally having a poor prognosis. The PI3K/Akt pathway plays a vital role in the oncogenesis and progression of many types of cancer. Limited studies have fully clarified the role of PI3K/Akt pathway in the prognosis of ovarian cancer and its correlation with drug sensitivity.

METHODS

A prognostic PI3K/Akt pathway related signature (PRS) was constructed with 10 machine learning algorithms using TCGA, GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082 datasets. Gaussian mixture and logistic regression were performed to identify the optimal models for classifying lymphatic and venous invasion.

RESULTS

The optimal prognostic PRS developed by Lasso + survivalSVM algorithm acted as an independent risk factor for overall survival (OS) of ovarian cancer patients and had a good performance in evaluating OS rate of ovarian cancer patients. Significant correlation was obtained between PRS-based risk score and Immune score, ESTIMATE score, immune cells and cancer-related hallmarks. Low risk score indicated a lower immune escape score, TIDE score, and higher PD1&CTLA4 immunophenoscore in ovarian cancer. Moreover, PRS-based risk score acted as an indicator for drug sensitivity in the immunotherapy and chemotherapy of ovarian cancer patients.

CONCLUSIONS

All in all, our study developed a prognostic PRS showing powerful and good performance in predicting clinical outcome of ovarian cancer patients. PRS could serve as an indicator for drug sensitivity in the chemotherapy and immunotherapy.

Network-based analysis identifies key regulatory transcription factors involved in skin aging

Skin aging is a complex process involving intricate genetic and environmental factors. In this study, we performed a comprehensive analysis of the transcriptional regulatory landscape of skin aging in canines. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify aging-related gene modules. We subsequently validated the expression changes of these module genes in single-cell RNA sequencing (scRNA-seq) data of human aging skin. Notably, basal cell (BC), spinous cell (SC), mitotic cell (MC), and fibroblast (FB) were identified as the cell types with the most significant gene expression changes during aging. By integrating GENIE3 and RcisTarget, we constructed gene regulation networks (GRNs) for aging-related modules and identified core transcription factors (TFs) by intersecting significantly enriched TFs within the GRNs with hub TFs from WGCNA analysis, revealing key regulators of skin aging. Furthermore, we demonstrated the conserved role of CTCF and RAD21 in skin aging using an H2O2-stimulated cell aging model in HaCaT cells. Our findings provide new insights into the transcriptional regulatory landscape of skin aging and unveil potential targets for future intervention strategies against age-related skin disorders in both canines and humans.

Development and validation of a novel anoikis-related gene signature for predicting prognosis in ovarian cancer

Anoikis plays a critical role in variable cancer types. However, studies that focus on the prognostic values of anoikis-related genes (ANRGs) in OV are scarce. Cohorts with transcriptome data and corresponding clinicopathologic data of OV patients were collected and consolidated from public databases. Multiple bioinformatics approaches were used to screen key genes from 446 anoikis-related genes, including Cox regression analysis, random survival forest analysis, and Kaplan-Meier analysis of best combinations. A five-gene signature was constructed in the discovery cohort (TCGA) and validated in four validation cohorts (GEO). Risk score of the signature stratified patients into high-risk (HRisk) and low-risk (LRisk) subgroups. Patients in the HRisk group were associated with worse OS than those in the LRisk group in both the TCGA cohort (p<0.0001, HR=2.718, 95%CI:1.872-3.947) and the four GEO cohorts (p<0.05). Multivariate Cox regression analyses confirmed that the risk score served as an independent prognostic factor in both cohorts. The signature's predictive capacity was further demonstrated by the nomogram analysis. Pathway enrichment analysis revealed that immunosuppressive and malignant progression-related pathways were enriched in the HRisk group, including TGF-β, WNT and ECM pathways. The LRisk group was characterized by immune-active signaling pathways (interferon-gamma, T cell activation, etc.) and higher proportions of anti-tumor immune cells (NK, M1, etc.) while HRisk patients were associated with higher stromal scores and less TCR richness. In conclusion, the signature reveals a close relationship between the anoikis and prognosis and may provide a potential therapeutic target for OV patients.

Nanomedicine as potential cancer therapy via targeting dysregulated transcription factors

Cancer as a disease possess quite complicated pathophysiological implications and is among the prominent causes of morbidity and mortality on global scales. Anti-cancer chemotherapy, surgery, and radiation therapy are some of the present-day conventional treatment options. However, these therapeutic paradigms own several retreats, including lack of specificity, non-targeted toxicological implications, inefficient drug delivery to targeted cells, and emergence of cancer resistance, ultimately causing ineffective cancer management. Owing to the advanced and better biophysical characteristic features and potentiality for the tailoring and customizations and in several fashions, nanotechnology can entirely transubstantiate the cancer identification and its managements. Additionally, nanotechnology also renders several answers to present-day mainstream limitations springing-up in anti-cancer therapeutics. Nanocarriers, owing to their outstanding physicochemical features including but not limited to their particle size, surface morphological features viz. shape etc., have been employed in nanomedicinal platforms for targeting various transcription factors leading to worthy pharmacological outcomes. This transcription targeting activates the wide array of cellular and molecular events like antioxidant enzyme-induction, apoptotic cell death, cell-cycle arrest etc. These outcomes are obtained after the activation or inactivation of several transcription factors and cellular pathways. Further, nanoformulations have been precisely calibrated and functionalized with peculiar targeting groups for improving their efficiency to deliver the drug-payload to specified and targeted cancerous cells and tissues. This review undertakes an extensive, across-the-board and all-inclusive approach consisting of various studies encompassing different types of tailored and customized nanoformulations and nanomaterials designed for targeting the transcription factors implicated in the process of carcinogenesis, tumor-maturation, growth and metastasis. Various transcription factors viz. nuclear factor kappa (NF-κB), signal transducer and activators of transcription (STAT), Cmyc and Twist-related protein 1 (TWIST1) along with several types of nanoparticles targeting these transcription factors have been summarized here. A section has also been dedicated to the different types of nanoparticles targeting the hypoxia inducing factors. Efforts have been made to summarize several other transcription factors implicated in various stages of cancer development, growth, progression and invasion, and their targeting with different kinds of nanomedicinal agents.

Exploration of the Immunotyping Landscape and Immune Infiltration-Related Prognostic Markers in Ovarian Cancer Patients

Background

Increasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.

Methods

The ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.

Results

Three immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.

Conclusions

Our study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.