Screening and identification of key biomarkers in adrenocortical carcinoma based on bioinformatics analysis

Comprehensive analysis of GINS subunit expression, prognostic value, and immune infiltration in clear cell renal cell carcinoma

Background

In recent decades, there has been increasing evidence that Go-Ichi-Nii-San (GINS) subunits play an important role in the development and progression of various tumors. However, little research has been conducted on the role of GINS subunits in clear cell renal cell carcinoma (ccRCC). This study sought to explore the differential expression, prognosis, and immunological significance of GINS subunits in ccRCC.

Methods

We used various analysis packages of R (version 3.6.3), the University of ALabama at Birmingham CANcer (UALCAN) data analysis portal, the Cancer Cell Line Encyclopedia (CCLE), the cBio Cancer Genomics Portal (cBioPortal), and the Tumor Immune Estimation Resource (TIMER) to study the gene expression, promoter methylation level, gene mutations, prognostic and diagnostic value, immune infiltration, pathway enrichment, and other aspects of the GINS subunits. Next, the genes related to the GINS subunits were analyzed using the STRING and GeneMANIA platforms, and the correlation between GINS subunits and the functions involved were investigated.

Results

The expression level of GINS1/2/3/4 was significantly higher in ccRCC tumor tissues than normal tissues, and was significantly related to tumor grade and stage. The expression of GINS1/2/4 may be related to the methylation degree of the promoter region. The prognostic and diagnostic analyses showed that the increased expression of GINS1 was associated with various poor prognoses and had diagnostic value. The GINS subunit mutation also significantly affected the clinical prognosis of ccRCC patients. Finally, the correlation analysis of the immune infiltration level, co-expression, and enrichment of related genes indicated that GINS subunit expression was associated with different levels of ccRCC immune infiltration.

Conclusions

The analysis results showed that the differential expression of GINS subunits in ccRCC, which had prognostic and diagnostic value, was correlated with clinicopathological stage, immune infiltration, and other related aspects. GINS1 may serve as a new potential prognostic biomarker for ccRCC patients and be used to guide treatment.

Wnt/β-catenin signaling pathway in the tumor progression of adrenocortical carcinoma

Adrenocortical carcinoma (ACC) is an uncommon, aggressive endocrine malignancy with a high rate of recurrence, a poor prognosis, and a propensity for metastasis. Currently, only mitotane has received certification from both the US Food and Drug Administration (FDA) and the European Medicines Agency for the therapy of advanced ACC. However, treatment in the advanced periods of the disorders is ineffective and has serious adverse consequences. Completely surgical excision is the only cure but has failed to effectively improve the survival of advanced patients. The aberrantly activated Wnt/β-catenin pathway is one of the catalysts for adrenocortical carcinogenesis. Research has concentrated on identifying methods that can prevent the stimulation of the Wnt/β-catenin pathway and are safe and advantageous for patients in view of the absence of effective treatments and the frequent alteration of the Wnt/β-catenin pathway in ACC. Comprehending the complex connection between the development of ACC and Wnt/β-catenin signaling is essential for accurate pharmacological targets. In this review, we summarize the potential targets between adrenocortical carcinoma and the Wnt/β-catenin signaling pathway. We analyze the relevant targets of drugs or inhibitors that act on the Wnt pathway. Finally, we provide new insights into how drugs or inhibitors may improve the treatment of ACC.

Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

A pan-cancer analysis of GINS complex subunit 4 to identify its potential role as a biomarker in multiple human cancers

This study was initiated to explore the expression variation, clinical significance, and biological importance of the GINS complex subunit 4 (GINS4) in different human cancers as a shared biomarker via pan-cancer analysis through different platforms including UALCAN, Kaplan Meier (KM) plotter, TNMplot, GENT2, GEPIA, DriverDBv3, Human Protein Atlas (HPA), MEXPRESS, cBioportal, STRING, DAVID, MuTarge, Enrichr, TIMER, and CTD. Our findings have verified the up-regulation of GINS4 in 24 major subtypes of human cancers, and its overexpression was found to be substantially associated with poor overall survival (OS), relapse-free survival (RFs), and metastasis in ESCA, KIRC, LIHC, LUAD, and UCEC. This suggested that GINS4 plays a significant role in the development and progression of these five cancers. Furthermore, we noticed that GINS4 is also overexpressed in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics. Enrichment analysis revealed the involvement of GINS4 associated genes in a variety of diverse GO and KEGG terms. We also explored few significant correlations between GINS4 expression and promoter methylation, genetic alterations, CNVs, other mutant genes, tumor purity, and immune cells infiltration. In conclusion, our results elucidated that GINS4 can serve as a shared diagnostic, prognostic biomarker, and a potential therapeutic target in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics.

Histological scores and tumor size on stage II in adrenocortical carcinomas

Adrenocortical carcinomas (ACC) are aggressive tumors with a poor prognosis. Histological scores are advised for the diagnosis, however, there are borderline cases that may be misjudged as adrenocortical adenomas (ACA). The three main scores used are: Weiss Modified System (WMS), Reticulin Algorithm (RA), and Helsinki Score (HS). We intend to compare the accuracy of the three scores in ACC diagnosis and to identify predictive factors of overall survival (OS). Retrospective study (2004–2016) at Centro Hospitalar e Universitário de Coimbra of the adrenal tumors, classified as ACC or ACA, with a history of posterior tumor relapse/metastases, without lesions in the contralateral adrenal gland: 13F and 6M, with a median age of 51 ± 12.41 years. Nodules’ median size was 9.20 ± 6.2 cm. Patients had a median OS of 52 ± 18.6 months, with 57.9% and 46.3%, at 3 and 5 years. Seven patients had local recurrence and nine had metastases. Thirteen cases were in stage II. The WMS and the HS allowed a diagnosis of ACC in 15 cases and the RA defined ACC in 17 cases. All cases had, at least, focal disruption of the reticulin framework. More than 5 mitosis/50 HPF was associated with worse OS: 49.67 ± 21.43 versus 108.86 ± 14.02 months (p = 0.026). In patients with stage II, tumor size ⩾10 cm was associated with worse OS: 19.25 ± 7.15 versus 96.11 ± 16.7 months (p = 0.007), confirmed by multivariate analysis (p = 0.031). The correct diagnosis of ACC is a pathologist responsibility. The RA seems the most accurate. Any loss of the reticulin framework should raise awareness for malignancy. In patients on stage II, a size ⩾10 cm is a predictor of worse prognosis.

The Role of Biomarkers in Adrenocortical Carcinoma: A Review of Current Evidence and Future Perspectives

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy arising from the adrenal cortex often with unexpected biological behavior. It can occur at any age, with two peaks of incidence: in the first and between fifth and seventh decades of life. Although ACC are mostly hormonally active, precursors and metabolites, rather than end products of steroidogenesis are produced by dedifferentiated and immature malignant cells. Distinguishing the etiology of adrenal mass, between benign adenomas, which are quite frequent in general population, and malignant carcinomas with dismal prognosis is often unfeasible. Even after pathohistological analysis, diagnosis of adrenocortical carcinomas is not always straightforward and represents a great challenge for experienced and multidisciplinary expert teams. No single imaging method, hormonal work-up or immunohistochemical labelling can definitively prove the diagnosis of ACC. Over several decades' great efforts have been made in finding novel reliable and available diagnostic and prognostic factors including steroid metabolome profiling or target gene identification. Despite these achievements, the 5-year mortality rate still accounts for approximately 75% to 90%, ACC is frequently diagnosed in advanced stages and therapeutic options are unfortunately limited. Therefore, imperative is to identify new biological markers that can predict patient prognosis and provide new therapeutic options.

Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients

BACKGROUND

The current guideline for the management of adrenocortical carcinoma (ACC) is insufficient for accurate risk prediction to guide adjuvant therapy. Given frequent and severe therapeutic side effects, a better estimate of survival is warranted for risk-specific assignment to adjuvant treatment. We attempted to construct an integrated model based on a prognostic gene signature and clinicopathological features to improve risk stratification and survival prediction in ACC.

METHODS

Using a series of bioinformatic and statistical approaches, a gene-expression signature was established and validated in two independent cohorts. By combining the signature with clinicopathological features, a decision tree was generated to improve risk stratification, and a nomogram was constructed to personalize risk prediction. Time-dependent receiver operating characteristic (tROC) and calibration analysis were performed to evaluate the predictive power and accuracy.

RESULTS

A three-gene signature could discriminate high-risk patients well in both training and validation cohorts. Multivariate regression analysis demonstrated the signature to be an independent predictor of overall survival. The decision tree could identify risk subgroups powerfully, and the nomogram showed high accuracy of survival prediction. Particularly, expression of a gene hitherto unknown to be dysregulated in ACC, TIGD1, was shown to be prognostically relevant.

CONCLUSION

We propose a novel gene signature to guide decision-making about adjuvant therapy in ACC. The score shows unprecedented survival prediction and hence constitutes a huge step towards personalized management. As a secondary important finding, we report the discovery and validation of a new oncogene, TIGD1, which was consistently overexpressed in ACC. TIGD1 might shed further light on the biology of ACC and might give rise to targeted therapies that not only apply to ACC but potentially also to other malignancies.

Future Directions in Diagnosis, Prognosis and Disease Monitoring of Adrenocortical Carcinoma: Novel Non-Invasive Biomarkers

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with frequent metastatic spread and poor prognosis. The disease can occur at any age with unexpected biological behavior. Recent genome-wide studies of ACC have contributed to our understanding of the disease, but diagnosis of ACC remains a challenge, even for multidisciplinary expert teams. Patients with ACC are frequently diagnosed in advanced stages and have limited therapeutic options. Therefore, for earlier diagnosis and better clinical management of adrenocortical carcinoma, specific, sensitive, and minimal invasive markers are urgently needed. Over several decades, great efforts have been made in discovering novel and reliable diagnostic and prognostic biomarkers including microRNAs, steroid profilings, circulating tumor cells, circulating tumor DNAs and radiomics. In this review, we will summarize these novel noninvasive biomarkers and analyze their values for diagnosis, predicting prognosis, and disease monitoring. Current problems and possible future application of these non-invasive biomarkers will also be discussed.

A Targeted Bioinformatics Assessment of Adrenocortical Carcinoma Reveals Prognostic Implications of GABA System Gene Expression

Adrenocortical carcinoma (ACC) is a rare but deadly cancer for which few treatments exist. Here, we have undertaken a targeted bioinformatics study of The Cancer Genome Atlas (TCGA) ACC dataset focusing on the 30 genes encoding the γ-aminobutyric acid (GABA) system—an under-studied, evolutionarily-conserved system that is an emerging potential player in cancer progression. Our analysis identified a subset of ACC patients whose tumors expressed a distinct GABA system transcriptome. Transcript levels of ABAT (encoding a key GABA shunt enzyme), were upregulated in over 40% of tumors, and this correlated with several favorable clinical outcomes including patient survival; while enrichment and ontology analysis implicated two cancer-related biological pathways involved in metastasis and immune response. The phenotype associated with ABAT upregulation revealed a potential metabolic heterogeneity among ACC tumors associated with enhanced mitochondrial metabolism. Furthermore, many GABA_A receptor subunit-encoding transcripts were expressed, including two (GABRB2 and GABRD) prognostic for patient survival. Transcripts encoding GABA_B receptor subunits and GABA transporters were also ubiquitously expressed. The GABA system transcriptome of ACC tumors is largely mirrored in the ACC NCI-H295R cell line, suggesting that this cell line may be appropriate for future functional studies investigating the role of the GABA system in ACC cell growth phenotypes and metabolism.

Expression Profile of GINS Complex Predicts the Prognosis of Pancreatic Cancer Patients

Background

The GINS complex has been implicated in the prognosis of various cancers. It comprises four subunits, encoded by GINS1, GINS2, GINS3, and GINS4 genes. Based on the current understanding, no report exists on the role of the GINS complex in pancreatic cancer.

Methods

We employed various bioinformatics databases including GEPIA, UALCAN, GEPIA2, and Kaplan Meier Plotter to identify the expression profile of the four genes (GINS1, GINS2, GINS3, and GINS4), their correlation with pancreatic cancer grade as well as their prognostic value of in pancreatic cancer. Western blotting and qRT-PCR analyses were conducted to verify the expression profiles of the four genes in pancreatic cancer. CCK8 and EdU cell experiments were conducted to reveal the role played by the four genes in pancreatic cancer cell proliferation.

Results

Based on GEPIA, Western blotting, and qRT-PCR analyses, all the four genes in the GINS complex were overexpressed in pancreatic cancer. Notably, the expression of each member was significantly associated with pancreatic cancer grade. The prognostic analysis revealed that not only the whole GINS complex but also each individual were prognostic biomarkers for pancreatic cancer. CCK8 and EdU experiments demonstrated that inhibition of the expression of each GINS member lowered pancreatic cancer cell proliferation.

Conclusion

This work implicated GINS1, GINS2, GINS3, and GINS4 genes as critical prognostic markers for pancreatic cancer.