Identification and verification of a ten-gene signature predicting overall survival for ovarian cancer

Exploration of YBX1 role in the prognostic value and immune characteristics by single-cell and bulk sequencing analysis for liver hepatocellular carcinoma

BACKGROUND

Y-box binding protein 1 (YBX1) plays a variety of roles in progression of multiple tumors. However, the role of YBX1 in prognostic value and immune regulation for liver hepatocellular carcinoma (LIHC) remains unclear. The present study aimed to examine the effect of YBX1 on the regulation of tumor immunity and survival prediction in LIHC patients.

METHODS

YBX1-related expression profiles and single-cell and bulk sequencing analysis were performed using online databases. YBX1 expression was validated by a quantitative real-time PCR (qRT-PCR), western blotting and immunohistochemistry. Univariate/multivariate Cox regression analysis was performed to determine independent predictors of overall survival (OS). The ESTIMATE (i.e., Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis were used to assess the relationships between YBX1 and LIHC immunity.

RESULTS

YBX1 was over-expressed in LIHC tissues and cell lines. High YBX1 expression was significantly associated with poor OS. Univariate/multivariate Cox regression analysis revealed that YBX1 was an independent prognostic factor for LIHC. Gene set enrichment analysis revealed that YBX1 was associated with multiple signaling pathways correlated to LIHC. Additionally, YBX1 was expressed in multiple immune cells and was significantly correlated with immune cells, immune checkpoint markers and tumor immune microenvironment. The TIDE analysis demonstrated that LIHC patients with high YBX1 expression showed a higher T-cell dysfunction score and a higher exclusion score, as well as poorer immunotherapy response.

CONCLUSIONS

YBX1 plays crucial oncogenic roles in LIHC and is closely associated with the immune defense system. YBX1 inhibition may serve as a potential treatment for LIHC.

Therapeutic potential of mangiferin in cancer: Unveiling regulatory pathways, mechanisms of action, and bioavailability enhancements - An updated review

Mangiferin (MGF) is a phenolic compound, which is a major source of MGF is the mango tree. MGF possesses some antioxidant, anti-inflammatory, and cytoprotective properties, enabling it to play its role against various diseases such as diabetes, obesity, lung injuries, and cancer. The word "Cancer" depicts an uncontrolled and abnormal growth of cells. This review paper reveals MGF's therapeutic, curative and protective potential impact against lung, liver, ovarian, prostate, breast, stomach, and oral cancers. MGF is used in various types of research in the form of powder, liquid extract, intramuscular, intravenous, nanoparticles coated with gold, in the form of a solution, or in combination with other drugs to evaluate synergistic effects. Many studies showed that MGF is safe to use but has less bioavailability in the body and 0.111 mg/mL solubility in water. However, certain studies indicated that its bioavailability and retention time increased when taken in the form of nanoparticles and in combination with other drugs. MGF also increases the sensitivity of other drugs (i.e., cisplatin) resistant to tumors. MGF has different mechanisms of action for different cancers. It mainly targets enzymes, interleukins, tumor growth factors, signaling pathways, apoptotic proteins, and genes to inhibit the growth of tumors, volume, angiogenesis, cellular functionality, further progression, and movement to other areas of the body. Moreover, MGF increases apoptosis and body weight with no or fewer side effects on normal cells. MGF unveiled a novel gate toward the treatment of cancer. Further research and human trials are needed in this regard.

High expression of PI4K2A predicted poor prognosis of colon adenocarcinoma (COAD) and correlated with immunity

BACKGROUND

PI4K2A has been found to have a tumor-promoting role in various solid tumors and be involved in various biological procedures. In this article, we aim to investigate the prognostic values of PI4K2A and provide new insights in colon adenocarcinoma (COAD).

METHODS

The Cancer Genome Atlas (TCGA) database, Human Protein Atlas online database, and UALCAN database were used to analyze the expression of PI4K2A in COAD and the survival of patients. Univariate and multifactorial Cox regression analyses were used to assess the prognosis of PI4K2A on COAD. GSEA was used to explore PI4K2A-related signaling pathways. In addition, the effect of PI4K2A on immune checkpoint inhibitors (ICIs) treatment was investigated by constructing a TIDE model and predicting the association between PI4K2A and anticancer drug sensitivity through the CellMiner database.

RESULTS

In the TCGA database, PI4K2A was highly expressed in COAD and the similar results were verified by qRT-PCR. Survival analysis, utilizing Kaplan-Meier curves, revealed that COAD patients with high PI4K2A expression had a worse prognosis. In addition, PI4K2A expression was discovered to have been associated with T-stage, N-stage, and pathological stage by logistic analysis. Next, we utilized univariate and multifactorial Cox regression analyses to identify PI4K2A as an independent predictor. Additionally, GSEA analysis indicates that PI4K2A is enriched in MAPK signaling pathway, Toll-like receptor signaling pathway, etc. In COAD, PI4K2A was remarkably associated with the tumor immune microenvironment. In addition, by constructing a TIDE model, we discovered that COAD patients in the PI4K2A low-expression cohort were better treated with ICI. Finally, analysis of the CellMiner database predicted that PI4K2A was adversely correlated with the sensitivity of various anticancer drugs.

CONCLUSIONS

Our study suggests that PI4K2A may be a potential predictor of poor prognosis in COAD and a potential biomarker for early diagnosis, prognosis, and treatment.

Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients

Background

Ovarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.

Methods

We obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.

Results

Patients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).

Conclusion

ICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.

Nucleolar protein NOP2 could serve as a potential prognostic predictor for clear cell renal cell carcinoma

As an indispensable part for cancer precision medicine, biomarkers and signatures for predicting cancer prognosis and therapeutic benefits were urgently required. The purpose of this study was to investigate the prognostic roles of NOP2 in renal clear cell carcinoma (ccRCC) for overall survival (OS) and its relationships with immunity. NOP2-related gene expression matrix associated with clinical information was obtained from the Cancer Genome Atlas (TCGA) ccRCC dataset and NOP2-related pathways were identified by gene set enrichment analysis (GSEA). Associations among the NOP2 expression and MSI, TMB, TNB, and immunity were also explored. Both the NOP2 mRNA and protein/phosphoprotein had a higher expression in ccRCC tumor tissues than in normal kidney tissues (both P < 0.001) and elevated NOP2 expression was associated with poor OS (P < 0.001). Logistic regression analysis revealed the NOP2 expression was significantly linked to stage, age, grade, N stage, T stage, and M stage (all P < 0.05). Univariate/multivariate Cox hazard regression analysis results indicated that NOP2 was an independent prognostic factor for OS in ccRCC and GSEA revealed five NOP2-related signaling pathways. Nomogram based on NOP2 and eight clinical characteristic parameters (grade, age, stage, gender, T stage, race, M stage, N stage) was constructed and carefully evaluated. Furthermore, NOP2 gene expression was also found to be significantly related to MSI, TMB, and immunity. Our findings revealed that NOP2 might be a potential prognostic factor for OS in ccRCC and it was significantly associated with immunity, MSI, and TMB.

Elevated Ras related GTP binding B (RRAGB) expression predicts poor overall survival and constructs a prognostic nomogram for colon adenocarcinoma

Currently, no articles have explored the roles of RRAGB gene in the occurrence and development of cancer. By means of The Cancer Genome Atlas (TCGA) data mining, we found that this gene might be a novel prognostic predictor for colon adenocarcinoma (COAD). Hence, this article was carried out to explore its roles in COAD and associations with immunity. RRAGB single-gene expression matrix and corresponding clinical information were extracted from TCGA database. Univariate/multivariate cox regression analyses and gene set enrichment analysis (GSEA) were utilized to identify independent prognostic factors and RRAGB related pathways, respectively. Relationships between RRAGB and immunity were also analyzed. Boxplot and K-M survival analysis indicated that RRAGB was not only differently expressed in COAD (P < 0.05), but also significantly associated with overall survival (OS; P < 0.05). Univariate and multivariate Cox hazard regression analyses indicated that RRAGB could serve as an independent prognostic factor for COAD (both P < 0.05). GSEA identified five signaling pathways significantly enriched in the high-RRAGB expression phenotype. Moreover, a RRAGB-based nomogram was successfully constructed and displayed a satisfactory performance. In addition, RRAGB expression was found to be significantly associated with microsatellite instability (MSI), tumor mutational burden (TMB) and immunity. Our results revealed that RRAGB could be a prognostic biomarker for COAD in terms of OS and markedly related to MSI, TMB, and immunity. We also constructed an RRAGB-based nomogram with a satisfactory performance. Further researches should be carried out to validate our findings.

Combination of Immune-Related Genomic Alterations Reveals Immune Characterization and Prediction of Different Prognostic Risks in Ovarian Cancer

With the highest case-fatality rate among women, the molecular pathological alterations of ovarian cancer (OV) are complex, depending on the diversity of genomic alterations. Increasing evidence supports that immune infiltration in tumors is associated with prognosis. Therefore, we aim to assess infiltration in OV using multiple methods to capture genomic signatures regulating immune events to identify reliable predictions of different outcomes. A dataset of 309 ovarian serous cystadenocarcinoma patients with overall survival >90 days from The Cancer Genome Atlas (TCGA) was analyzed. Multiple estimations and clustering methods identified and verified two immune clusters with component differences. Functional analyses pointed out immune-related alterations underlying internal genomic variables potentially. After extracting immune genes from a public database, the LASSO Cox regression model with 10-fold cross-validation was used for selecting genes associated with overall survival rate significantly, and a risk score model was then constructed. Kaplan-Meier survival and Cox regression analyses among cohorts were performed systematically to evaluate prognostic efficiency among the risk score model and other clinical pathological parameters, establishing a predictive ability independently. Furthermore, this risk score model was compared among identified signatures in previous studies and applied to two external cohorts, showing better prediction performance and generalization ability, and also validated as robust in association with immune cell infiltration in bulk tissues. Besides, a transcription factor regulation network suggested upper regulatory mechanisms in OV. Our immune risk score model may provide gyneco-oncologists with predictive values for the prognosis and treatment management of patients with OV.

Development and Validation of an Immune-Related Gene-Pair Model of High-Grade Serous Ovarian Cancer After Platinum-Based Chemotherapy

Background

High-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.

Method

The study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1,526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.

Results

A prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant (p < 0.05). The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, the immune-related pathways were enriched in the low-risk group.

Conclusion

The prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.