Integrative Insights into Cancer Biology from Gene Expressions and Signaling Pathways to Computational Approaches and Meta-Analyses

*Amrita Saini, ¹Dr. Prajkta Gill,
* Research Scholar, Chitkara College of pharmacy, Chandigarh,
¹Assistant Professor, Chitkara College of pharmacy, Chandigarh

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Author Information: Amrita Saini, & Dr. Prajkta Gill

Title: Integrative Insights into Cancer Biology from Gene Expressions and Signaling Pathways to Computational Approaches and Meta-Analyses

Journal: PEXACY International Journal of Pharmaceutical Science

Volume and Issue: Vol. 2, Number 8

Page Numbers: 150–168

Version: Version 1

DOI: https://doi.org/10.5281/zenodo.8296051

Abstract: Cancer remains a leading cause of mortality worldwide, and its complex biology requires an integrative approach for effective understanding and treatment. This review article aims to provide a comprehensive overview of various critical aspects of cancer biology, including gene expressions, epigenetic modifications, and cell proliferation mechanisms. A particular focus is given to the role of the NF-κB signaling pathway in cancer pathogenesis and progression. The article also delves into the utility of computational approaches, such as microarray meta-analysis, in consolidating data from multiple studies to identify consistently altered genes and pathways. These computational methods offer a robust framework for the discovery of potential therapeutic targets and diagnostic markers. The review concludes by discussing the limitations, challenges, and future directions in the application of these methodologies in cancer research.

Keywords: Cancer Biology, Gene Expressions, Epigenetic Changes, Cell Proliferation, NF-κB Signaling Pathway, Computational Approaches, Microarray Meta-Analysis, Therapeutic Targets, Diagnostic Markers.

Article can be accessed online on: PEXACY International Journal of Pharmaceutical Science

Corresponding Author- *Amrita1457@gmail.com

Update: Received on 21/08/2023; Accepted; 26/08/2023, Published on; 29/08/2023

INTRODUCTION

Cancer remains one of the most formidable challenges in the realm of modern medicine. Despite significant advancements in diagnostic and therapeutic modalities, the molecular intricacies of cancer continue to elude comprehensive understanding. One area of burgeoning interest is the role of gene expressions in the pathogenesis and progression of cancer. Gene expressions serve as the functional units of the genome and are pivotal in determining the cellular phenotype. Their dysregulation is often implicated in the onset and progression of various malignancies, thereby making them potential targets for therapeutic interventions.

Gene Expressions and Epigenetic Changes

The landscape of cancer genomics is not merely a result of genetic mutations but also involves extensive epigenetic changes. Chromatin remodeling, for instance, has been identified as a significant mechanism inducing global changes in cancer epigenomes (Youn et al., 2018). These epigenetic alterations can affect gene expressions, thereby contributing to the cancer phenotype.

Cell Proliferation and Gastric Cancer

In gastric cancer, alterations in cell proliferation-related gene expressions have been observed. These changes can influence the rate of tumor growth and metastasis, thereby affecting the prognosis and treatment outcomes (Kim et al., 2011).

DNA Methylation

DNA methylation is another epigenetic mechanism that can impact gene expression in cancer cells. The effect of DNA methylation on gene expression has been studied to understand its role in cancer pathogenesis (Lee et al., 2014).

Role of NF-κB Signaling Pathway

The NF-κB signaling pathway has been targeted in cancer therapy due to its role in regulating gene expressions related to cell survival and inflammation. Dietary polyphenols have been studied for their potential to modulate this pathway (Khan et al., 2020).

Computational Approaches

Recent advancements in computational biology have enabled the development of frameworks like TOOme, which infers the cancer tissue-of-origin by integrating both gene mutation and expression data (He et al., 2020).

Microarray Meta-Analysis

Meta-analysis of cancer microarray data has also been employed to select genes that are consistently differentially expressed across multiple studies, thereby providing more robust markers for cancer diagnosis and treatment (Ma & Huang, 2009).

GENE EXPRESSIONS AND EPIGENETIC CHANGES

The intricate relationship between gene expressions and epigenetic changes in the context of cancer is a subject of profound scientific inquiry. Epigenetics, broadly defined as heritable changes in gene function that do not involve alterations to the underlying DNA sequence, plays a pivotal role in the regulation of gene expressions.

These epigenetic changes can manifest in various forms, such as DNA methylation, histone modifications, and chromatin remodeling, among others. One of the seminal works in this area is a pan-cancer analysis that delved into the role of chromatin remodeling as a significant mechanism inducing global changes in cancer epigenomes (Youn et al., 2018). The study provided compelling evidence that chromatin states could influence gene expressions in cancer, thereby contributing to the heterogeneity and complexity of the disease.

Chromatin remodeling is particularly noteworthy because it involves the repositioning or removal of nucleosomes on DNA, thereby making certain regions of the genome more accessible for transcriptional machinery. This increased accessibility can either upregulate or downregulate gene expressions, depending on the specific regulatory elements exposed. In the context of cancer, chromatin remodeling can lead to the activation of oncogenes or the silencing of tumor suppressor genes.

The pan-cancer analysis by Youn et al. (2018) was instrumental in highlighting how chromatin remodeling could lead to global changes in the epigenome, thereby affecting gene expressions across various types of cancer. The study employed a comprehensive approach, integrating data from multiple cancer types to identify common epigenetic changes that could serve as potential therapeutic targets.

Another layer of complexity is added by the phenomenon of DNA methylation, a type of epigenetic modification where a methyl group is added to the DNA molecule. DNA methylation generally leads to the repression of gene expression and is often observed in the promoter regions of genes. Lee et al. (2014) conducted an in-depth study to determine the effect of DNA methylation on gene expression in cancer cells.

Their work elucidated that aberrant methylation patterns could silence tumor suppressor genes or activate oncogenes, thereby contributing to cancer pathogenesis. The study also emphasized the potential of demethylating agents in reversing these changes, offering a therapeutic avenue for targeted cancer treatment.

The interplay between gene expressions and epigenetic changes is not merely a one-way street. Gene expressions themselves can influence epigenetic states. For instance, genes involved in DNA methylation and chromatin remodeling can be differentially expressed in cancer, thereby affecting the epigenetic landscape. This intricate feedback loop adds another layer of complexity to our understanding of cancer biology and highlights the need for integrated approaches that consider both genetic and epigenetic factors.

In summary, the relationship between gene expressions and epigenetic changes in cancer is a complex, bidirectional interplay that offers multiple avenues for therapeutic intervention. Understanding this relationship is crucial for the development of targeted therapies and personalized medicine approaches in cancer treatment.

The works of Youn et al. (2018) and Lee et al. (2014) serve as foundational studies in this domain, providing valuable insights into how epigenetic changes, particularly chromatin remodeling and DNA methylation, can influence gene expressions in cancer. These studies underscore the importance of a multi-faceted approach to cancer research, one that integrates genomics, epigenomics, and transcriptomics to provide a comprehensive understanding of this complex disease.

CELL PROLIFERATION AND GASTRIC CANCER

The relationship between cell proliferation and gastric cancer is a complex interplay that involves multiple molecular mechanisms, signaling pathways, and genetic alterations. Here, I will delve into some of the key aspects of this relationship, supported by scientific literature.

Role of Non-Coding RNAs

1. Circular RNAs: Circular RNA_LARP4 has been shown to inhibit cell proliferation and invasion in gastric cancer by sponging miR-424-5p and regulating LATS1 expression (J. Zhang et al., 2017).
2. Long Non-Coding RNAs (lncRNAs): LncRNA MALAT1 regulates cell proliferation and cisplatin resistance in gastric cancer via the PI3K/AKT pathway (Dai et al., 2020). Another lncRNA, LINC01503, promotes gastric cancer cell proliferation and invasion by regulating Wnt signaling (Ding et al., 2021).

MicroRNAs and Signaling Pathways

1. miR-12129: This microRNA suppresses cell proliferation and blocks cell cycle progression by targeting SIRT1 in gastric cancer (W. Zhang et al., 2020).
2. miR-130a/miR-107: These microRNAs are sponged by circular RNA-ZFR, which inhibits cell proliferation and promotes apoptosis by modulating PTEN (Liu et al., 2018).

Protein Interactions and Other Mechanisms

1. BDNF Expression: CircHIPK3 promotes cell proliferation and migration by sponging miR-107 and regulating BDNF expression (Wei et al., 2020).
2. FBXW7-MCL1 Axis: Lycorine hydrochloride inhibits cell proliferation and induces apoptosis by promoting this axis in gastric cancer (Li et al., 2020).
3. MCM3AP-AS1: This regulates miR-708-5p, affecting cell proliferation and apoptosis (Wang et al., 2020).
4. HOXC10: Overexpression of this gene promotes cell proliferation and migration (Kim et al., 2019).
5. KLF4: The lncRNA SNHG5/miR-32 axis regulates gastric cancer cell proliferation and migration by targeting KLF4 (Zhao et al., 2017).

Implications for Research and Treatment

Understanding the molecular mechanisms that regulate cell proliferation in gastric cancer is crucial for developing targeted therapies. For instance, targeting specific non-coding RNAs or microRNAs could offer a novel approach to control cell proliferation and, consequently, cancer progression. Further research is needed to explore other potential molecular targets and to understand the broader landscape of cell proliferation in gastric cancer. This could involve multi-omics approaches, including genomics, transcriptomics, and proteomics, to provide a more comprehensive view.

In summary, cell proliferation in gastric cancer is regulated by a myriad of factors, including non-coding RNAs, microRNAs, and various signaling pathways. Targeting these could provide new avenues for therapeutic intervention.

DNA METHYLATION

DNA methylation is a critical epigenetic modification that plays a pivotal role in the regulation of gene expression. It involves the addition of a methyl group to the carbon 5 position of the cytosine ring in a CpG dinucleotide, thereby affecting the transcriptional activity of genes. DNA methylation is a highly regulated process, and its dysregulation is implicated in various pathological conditions, including cancer (Koch et al., 2018). The role of DNA methylation in cancer is complex and multifaceted, involving both gene silencing and activation, which contributes to tumorigenesis, metastasis, and resistance to therapy (Ehrlich, 2002). This review aims to provide a comprehensive understanding of the role of DNA methylation in cancer, focusing on its impact on gene expression, cell proliferation, and therapeutic resistance.

DNA Methylation and Gene Expression

The relationship between DNA methylation and gene expression is intricate. Methylation of promoter regions is generally associated with gene silencing, whereas methylation within gene bodies can either enhance or repress gene expression (Klutstein et al., 2016). Aberrant DNA methylation patterns, including both hypermethylation and hypomethylation, are commonly observed in cancer. Hypermethylation of tumor suppressor genes leads to their inactivation, contributing to uncontrolled cell proliferation and tumorigenesis. On the other hand, hypomethylation of oncogenes results in their overexpression, further promoting cancer progression (Lakshminarasimhan & Liang, 2016).

DNA Methylation and Cell Proliferation

DNA methylation also plays a crucial role in regulating cell proliferation, a fundamental process that is often dysregulated in cancer. Methylation patterns can influence the cell cycle, apoptosis, and DNA repair mechanisms, thereby affecting the proliferative capacity of cancer cells (Das et al., 2018). For instance, hypermethylation of genes involved in cell cycle regulation can lead to uncontrolled cell division, while hypomethylation of genes associated with apoptosis can result in resistance to cell death, both of which are hallmarks of cancer (Fukushige & Horii, 2013).

DNA Methylation and Therapeutic Resistance

The role of DNA methylation in therapeutic resistance is gaining increasing attention. Methylation-induced silencing of genes involved in drug metabolism and transport can lead to decreased drug efficacy, contributing to resistance (Koo et al., 2014). Moreover, changes in DNA methylation patterns can also affect the tumor microenvironment, thereby influencing the response to immunotherapy (Ding et al., 2021).

In summary, DNA methylation is a key epigenetic modification with significant implications in cancer. Its role is complex, affecting gene expression, cell proliferation, and therapeutic resistance. Understanding the intricacies of DNA methylation in cancer can provide valuable insights into the mechanisms underlying tumorigenesis and offer potential avenues for therapeutic intervention (Gebhard et al., 2010; Laird & Jaenisch, 1996).

THE ROLE OF NF-ΚB SIGNALING PATHWAY IN CANCER

The Nuclear Factor Kappa B (NF-κB) signaling pathway is a critical regulator of various cellular processes, including inflammation, immunity, and cell survival. It has been extensively studied for its role in cancer development and progression. The NF-κB pathway is a complex network of proteins that regulate gene expression, and its dysregulation is implicated in multiple types of cancer, contributing to tumor initiation, progression, and resistance to therapy (Hayden & Ghosh, 2008).

NF-ΚB AND CANCER INITIATION

The NF-κB pathway plays a significant role in the initiation of cancer by regulating genes involved in cell proliferation and apoptosis. Activation of NF-κB can lead to the expression of pro-survival genes, thereby providing a conducive environment for the survival and proliferation of cancer cells (Karin, 2006). For instance, NF-κB activation has been shown to upregulate the expression of anti-apoptotic proteins like Bcl-2, thereby inhibiting programmed cell death and promoting tumorigenesis (Wang et al., 2011).

NF-κB in Cancer Progression and Metastasis

The role of NF-κB in cancer progression is multifaceted. It regulates the expression of genes involved in cell migration, invasion, and angiogenesis, thereby contributing to the metastatic potential of cancer cells (Ben-Neriah & Karin, 2011). For example, NF-κB activation has been associated with increased expression of matrix metalloproteinases (MMPs), which are enzymes that degrade the extracellular matrix and facilitate cancer cell invasion (Gupta et al., 2010).

NF-κB and Therapeutic Resistance

NF-κB activation is also implicated in resistance to chemotherapy and targeted therapies. It has been shown that activation of NF-κB can induce the expression of multi-drug resistance genes, thereby reducing the efficacy of chemotherapeutic agents (Baud & Karin, 2009). Moreover, NF-κB activation can also interfere with the effectiveness of targeted therapies by upregulating survival pathways that bypass the inhibited targets (Luo et al., 2012).

NF-κB as a Therapeutic Target

Given its central role in cancer biology, NF-κB is considered a promising therapeutic target. Several inhibitors targeting different components of the NF-κB pathway are under investigation, both in pre-clinical and clinical settings (Gilmore & Herscovitch, 2006).

The NF-κB signaling pathway plays a crucial role in various aspects of cancer biology, from initiation and progression to therapeutic resistance. Understanding the intricate mechanisms of NF-κB regulation and its downstream effects can provide valuable insights into cancer biology and offer potential avenues for therapeutic intervention.

COMPUTATIONAL APPROACHES

The NF-κB signaling pathway is a complex network of proteins that plays a pivotal role in regulating various cellular processes, including inflammation, cell survival, and immune responses. Given its multifaceted role in cancer biology, understanding the intricacies of this pathway is of paramount importance. Computational approaches have emerged as powerful tools to dissect the complexities of the NF-κB signaling pathway, offering insights into its role in cancer initiation, progression, and treatment resistance (Hayden & Ghosh, 2008).

Computational Modeling of NF-κB Dynamics

Computational models have been developed to simulate the dynamics of NF-κB activation and its downstream effects. These models incorporate various components of the pathway, including IKK complexes, IκB proteins, and NF-κB dimers, to predict how different stimuli can lead to NF-κB activation and subsequent gene expression (Karin, 2006). Such models have been instrumental in identifying potential drug targets within the pathway (Wang et al., 2011).

Systems Biology Approaches

Systems biology integrates experimental data with computational models to provide a holistic view of the NF-κB signaling pathway. By employing techniques like network analysis and pathway enrichment, researchers have been able to identify key nodes and interactions that are critical for the oncogenic functions of NF-κB (Ben-Neriah & Karin, 2011).

Machine Learning in Drug Discovery

Machine learning algorithms have been applied to predict the efficacy of various NF-κB inhibitors. These algorithms use features like molecular docking scores, physicochemical properties, and pathway interactions to predict the potential of a compound to inhibit NF-κB activity (Gupta et al., 2010).

Computational approaches offer a powerful means to understand the complexities of the NF-κB signaling pathway in cancer. From dynamic modeling to machine learning, these techniques provide valuable insights that can guide experimental research and therapeutic development.

MICROARRAY META-ANALYSIS IN CANCER RESEARCH

The advent of high-throughput technologies like microarrays has revolutionized the field of cancer research by enabling the simultaneous analysis of thousands of genes. However, the sheer volume of data generated poses challenges in interpretation and validation. Microarray meta-analysis has emerged as a robust computational approach to overcome these challenges, offering a way to integrate findings from multiple studies to identify consistently altered genes and pathways in cancer (Goonesekere et al., 2018).

Methodological Framework

Microarray meta-analysis involves a series of steps starting from data collection to statistical analysis. The primary aim is to identify differentially expressed genes (DEGs) that are consistently reported across multiple studies. This approach not only increases the statistical power but also minimizes the biases and errors associated with individual studies. Various statistical methods and software packages have been developed to facilitate this complex analysis, each with its own set of assumptions and limitations (Pavlou et al., 2014).

Applications in Cancer Subtypes

One of the significant advantages of microarray meta-analysis is its ability to dissect the molecular heterogeneity of cancer. For instance, in breast cancer, meta-analysis has been instrumental in identifying genes like CD80 and ISG15, which are associated with disease progression and metastasis (Li et al., 2020). Similarly, in ovarian cancer, genes like GAS6 have been identified as independent predictors of poor survival through meta-analysis (Buehler et al., 2013).

Therapeutic Implications

The identification of DEGs through meta-analysis has profound implications in the discovery of novel therapeutic targets. For example, the overexpression of programmed death-ligand 1 (PD-L1) in ovarian cancer was identified through meta-analysis, suggesting its potential as a therapeutic target (Wang, 2019). Moreover, these DEGs can serve as biomarkers for disease diagnosis, prognosis, and treatment response, thereby personalizing cancer therapy (Bozovic-Spasojevic et al., 2017).

Future Directions

While microarray meta-analysis has significantly advanced our understanding of cancer biology, there are still challenges that need to be addressed. These include the standardization of data preprocessing steps, dealing with batch effects, and integrating data from different platforms. The advent of machine learning and artificial intelligence is expected to further refine the meta-analysis methods, making them more accurate and efficient (Mamtani & Kulkarni, 2012). Microarray meta-analysis serves as a powerful tool for the integration of high-throughput data in cancer research. It not only enhances the reliability of the findings but also paves the way for the discovery of novel genes and pathways that could serve as potential therapeutic targets or biomarkers. As computational methods continue to evolve, the scope and applicability of microarray meta-analysis in cancer research are expected to expand further.

CONCLUSION

In summary, microarray meta-analysis stands as a cornerstone in the realm of cancer research, offering a robust computational framework for the integration and interpretation of high-throughput gene expression data. This approach has proven invaluable in enhancing the statistical power of studies, thereby facilitating the identification of differentially expressed genes (DEGs) that are consistently altered across multiple investigations. Such DEGs serve as critical molecular signatures, offering profound insights into the complex landscape of cancer biology, from initiation and progression to therapeutic resistance (Goonesekere et al., 2018; Li et al., 2020).

The utility of microarray meta-analysis extends beyond mere identification of DEGs; it provides a comprehensive understanding of the molecular heterogeneity inherent in various cancer subtypes. This is particularly important given the increasing focus on personalized medicine. By identifying genes and pathways that are specifically altered in different cancer subtypes, meta-analysis contributes to the development of targeted therapies and prognostic markers, thereby personalizing treatment strategies (Wang, 2019; Bozovic-Spasojevic et al., 2017).

However, it is crucial to acknowledge the challenges and limitations associated with microarray meta-analysis. These include issues related to data preprocessing, batch effects, and the integration of data from diverse platforms. The field is in dire need of standardized protocols and methodologies to address these challenges. The advent of machine learning and artificial intelligence holds promise in refining meta-analysis techniques, making them more accurate and efficient (Mamtani & Kulkarni, 2012).

As we move forward, the role of microarray meta-analysis in cancer research is expected to evolve in tandem with advancements in computational methods and high-throughput technologies. It will continue to serve as a powerful tool for data integration, offering a more reliable and comprehensive view of the cancer transcriptome. This, in turn, will pave the way for the discovery of novel therapeutic targets and biomarkers, thereby contributing to the overarching goal of improving cancer diagnosis, treatment, and ultimately, patient outcomes.

DISCUSSION

The role of microarray meta-analysis in cancer research is undeniably transformative, serving as a nexus between high-throughput technologies and actionable insights into the complex landscape of cancer biology. This discussion aims to delve deeper into the methodological advancements, applications, and future directions of microarray meta-analysis, while also addressing its limitations and challenges.

Methodological Advancements

Over the years, the methodology behind microarray meta-analysis has evolved significantly. The initial focus was primarily on identifying differentially expressed genes (DEGs) that were consistently altered across multiple studies. However, the field has since expanded to include more sophisticated statistical models and algorithms that can account for study-specific variations, thereby enhancing the reliability of the findings (Pavlou et al., 2014). These advancements have been pivotal in mitigating the limitations associated with individual studies, such as small sample sizes and experimental biases, thus increasing the statistical power and robustness of the meta-analytic results.

Applications in Understanding Cancer Heterogeneity

One of the most compelling applications of microarray meta-analysis is its ability to dissect the molecular heterogeneity inherent in various cancer subtypes. This is particularly crucial in the era of personalized medicine, where treatment strategies are tailored to the individual patient’s molecular profile. For instance, the identification of genes like CD80 and ISG15 in breast cancer has provided valuable insights into disease progression and metastasis, thereby aiding in the development of targeted therapies (Li et al., 2020).

Therapeutic and Diagnostic Implications

Beyond its role in understanding the molecular underpinnings of cancer, microarray meta-analysis has profound implications in the therapeutic landscape. The identification of DEGs and associated pathways through meta-analysis has led to the discovery of novel therapeutic targets. For example, the overexpression of PD-L1 in ovarian cancer, identified through meta-analysis, has opened new avenues for immunotherapy (Wang, 2019). Additionally, these DEGs can serve as potential biomarkers for early diagnosis, prognosis, and monitoring treatment response, thereby contributing to the personalization of cancer therapy (Bozovic-Spasojevic et al., 2017).

Limitations and Challenges

While the contributions of microarray meta-analysis to cancer research are substantial, it is essential to acknowledge its limitations. These include issues related to data preprocessing, the heterogeneity of microarray platforms, and the lack of standardized protocols. Addressing these challenges requires collaborative efforts from both experimental and computational scientists to develop standardized methodologies and software tools.

Future Directions

The future of microarray meta-analysis is promising, especially with the advent of machine learning and artificial intelligence. These computational techniques are expected to refine the existing methods, making them more accurate and efficient. Moreover, the integration of microarray data with other omics data types, such as proteomics and metabolomics, is an exciting avenue for future research, offering a more holistic view of cancer biology (Mamtani & Kulkarni, 2012).

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47. Pavlou, M. P., Dimitromanolakis, A., Martinez-Morillo, E., Smid, M., Foekens, J. A., & Diamandis, E. P. (2014). Integrating meta-analysis of microarray data and targeted proteomics for biomarker identification: application in breast cancer. Journal of Proteome Research, 13(6), 2897–2909.
48. Buehler, M., Tse, B., Leboucq, A., Jacob, F., Caduff, R., Fink, D., Goldstein, D. R., & Heinzelmann-Schwarz, V. (2013). Meta-analysis of microarray data identifies GAS6 expression as an independent predictor of poor survival in ovarian cancer. BioMed Research International, 2013, 238284.
49. Bozovic-Spasojevic, I., Zardavas, D., Brohée, S., Ameye, L., Fumagalli, D., Ades, F., de Azambuja, E., Bareche, Y., Piccart, M., Paesmans, M., & Sotiriou, C. (2017). The prognostic role of androgen receptor in patients with early-stage breast cancer: A meta-analysis of clinical and gene expression data. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 23(11), 2702–2712.
50. Mamtani, M., & Kulkarni, H. (2012). Association of HADHA expression with the risk of breast cancer: targeted subset analysis and meta-analysis of microarray data. BMC Research Notes, 5(1), 25.
51. Pavlou, M. P., Dimitromanolakis, A., Martinez-Morillo, E., Smid, M., Foekens, J. A., & Diamandis, E. P. (2014). Integrating meta-analysis of microarray data and targeted proteomics for biomarker identification: application in breast cancer. Journal of Proteome Research, 13(6), 2897–2909. DOI
52. Li, Y., Bai, W., & Zhang, L. (2020). The Overexpression of CD80 and ISG15 are associated with the progression and metastasis of breast cancer by a meta-analysis integrating three microarray datasets. Pathology Oncology Research: POR, 26(1), 443–452.
53. Wang, L. (2019). Prognostic effect of programmed death-ligand 1 (PD-L1) in ovarian cancer: a systematic review, meta-analysis and bioinformatics study. Journal of Ovarian Research, 12(1), 37.
54. Bozovic-Spasojevic, I., Zardavas, D., Brohée, S., Ameye, L., Fumagalli, D., Ades, F., de Azambuja, E., Bareche, Y., Piccart, M., Paesmans, M., & Sotiriou, C. (2017). The prognostic role of androgen receptor in patients with early-stage breast cancer: A meta-analysis of clinical and gene expression data. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 23(11), 2702–2712.
55. Mamtani, M., & Kulkarni, H. (2012). Association of HADHA expression with the risk of breast cancer: targeted subset analysis and meta-analysis of microarray data. BMC Research Notes, 5(1), 25.