Research Article

Altered gene expression pattern due to different tumor percentage affects functions

Manal A Tashkandi ^1,#,*, Mohammed Y Refai ^1,#, Lina A Baz ^1,#, Hanadi M Baeissa ¹, Aminah A Barqawi ², and Pawan Kumar Sharma ³

¹    College of Science, Department of Biochemistry, University of Jeddah, Jeddah, P.O. Box 80327, Jeddah, 21589, Saudi Arabia.

²    College of Science, Department of Chemistry, Umm Al Qura University, Makkah Al-Mukarramah, 21955, Saudi Arabia.

³    Department of Computer Science, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, 110025, India

^#    Shared first authors

*    Correspondence: matashkandi@uj.edu.sa (M.A.T.)

Citation: Tashkandi M.A., Refai M.Y., Baz L.A., Baeissa H.M., Barqawi, A.A., and Shamra, P.K. Altered gene expression pattern due to different tumor per-centage affects functions. Glob. Jour. Bas. Sci. 2025, 1(6). 1-9. Received: March 06, 2024 Revised: April 03, 2025 Accepted: April 10, 2025 Published: April 15, 2025 doi: 10.63454/jbs20000028 ISSN: 3049-3315 Volume 1; Issue 6 Download PDF file

 Abstract: High-throughput data are produced on a big scale and at several levels in order to comprehend complex diseases including cancer, diabetes, and kidney disorders. A significant obstacle still exists, nonetheless, in extracting useful information from huge datasets for a thorough comprehension of cell phenotypes and disease pathogenesis. Big data is created to address biological concerns as a result of technological advancements, and it is always easy for biologists and computer scientists to collaborate to streamline the massive datasets and extract the information that is relevant and physiologically important. In order to achieve this, we have classified using expression datasets and inferred the corresponding functional annotation using a computational technique. In order to analyze changes in gene expression and functional annotation, we employed a dataset of prostate cancer patients with normal and variable tumor percentages. We have chosen a gene expression omnibus (GEO) dataset that includes human samples with a range of tumor percentages (0–85%). We arranged the samples according to tumor proportion in ascending order and compared them to the control group (samples with no tumor) to look for changes in gene expression and developed functions as the tumor percentage increased. When the tumor percentage is less than 50%, we see some fluctuation in the number of differentially expressed genes (DEGs), but after that, it increases exponentially. In terms of the pathways, there is a lot of variation in the number of enriched pathways, with tumor < 50% not increasing, while several cancer-associated pathways seem to be enriched for nearly all the different tumor percentages. Our analysis leads us to the conclusion that whereas a number of cancer-associated pathways are consistently enriched for all tumor percentages, the number of differentially expressed genes (DEGs) grows as the tumor percentage rises while the enriched pathways do not. Insulin resistance, acute myeloid leukemia, basal TFs, HIF1-a, neurotrophin, base excision repair, ErbB, VEGF, and mTOR signaling pathways are among the top-ranked enriched pathways. RPP30, SRP14, CCNE1, PRKAR1A, ABCF2, PCMT1, TUBA1C, STOML2, PPP2R4, and TPI1 are possible pathway components.

Keywords: Prostate cancer; tumor percentage; gene expression profiling; network analysis; functional impact

 

1. Introduction

The post-genomics era’s development in recent decades has produced enormous amounts of “big data” in the biological sciences, which opens up a wide range of interdisciplinary applications. Large datasets have also made it more difficult to handle, analyze, mine, store, and decipher relevant information. Numerous biological databases contain a variety of dataset kinds. In the biological sciences, databases like TCGA, Oncomine, Nephroseq, and GEO (gene expression omnibus) are commonly utilized. Numerous datasets pertaining to diabetes, cancer, and other biological issues are stored in these databases[1-10].

Prostate cancer is the fifth most prevalent cause of death for men globally and the second most common type of cancer to be diagnosed. Prostate cancer is frequently treated with androgen deprivation therapy, however resistance frequently reduces the benefits of this treatment for survival. Although immunotherapy has shown enormous promise in the treatment of solid tumors, patients with prostate cancer have not shown clinically significant improvements, underlining unique limitations of this therapeutic method. Therefore, it’s critical to investigate new ways to improve prostate cancer immunotherapy’s effectiveness in concert.

Beneath the bladder and encircling the urethra lies the male reproductive auxiliary organ known as the prostate gland. Contributing vital secretions to semen, which produce ejaculate and preserve sperm viability, is the primary role of the prostate. Usually in the mid-to-late stage of life, the cells in the prostate gland can develop into tumors. The mature human prostate has fibromuscular and periurethral areas in addition to central, transitional, and peripheral zones. The peripheral zone contributes most to normal prostate function in young adult men and comprises more than 70% of the prostate glandular tissue. Nearly 80% of prostate tumors originate in this region, making it the most frequent site of origin for neoplasms in the aging prostate. The typical gland is made up of stroma-embedded ducts and acini. The basement membrane is created by a layer of basal epithelium around a single layer of simple, columnar epithelium seen in the ducts and acini. The stromal cells that support spontaneous contractility and avoid fluid stagnation are mostly smooth muscle myocytes, to which this layer of extracellular matrix is attached. Fibroblasts are also found in the stroma, and they primarily support the ducts in the adult prostate. However, it is thought that fibroblast paracrine signaling plays a crucial role in the patterning of the duct during prostate development. According to laboratory data, these stromal fibroblasts have the ability to proliferate in the tumor microenvironment, also known as the tumor stroma, by triggering survival signaling and causing epithelial transformation. They are also thought to play a role in the long-term growth of cancer cells after treatment. Importantly, the androgen receptor (AR), which is thought to be the cause of hormone reliance in prostate cancer, is encoded by AR, which is highly expressed by these epithelial cells in both healthy and malignant organs. Furthermore, these cells release a serine protease called prostate-specific antigen (PSA), which is used to identify and diagnose prostate cancer. PSA is transcriptionally activated by the AR and is often higher in individuals with prostate cancer.

Prostate cancer affects millions of men annually. The disease is one of the most prevalent solid cancers in high-income areas, and the prognosis varies greatly depending on age, ethnicity, genetic background, and stage of progression. Based on the patient’s health status and the tumor’s histological, anatomical, and molecular characteristics, one can predict the course of a particular person’s illness. Living with prostate cancer for many men entails following a customized treatment plan for a slow-growing, frequently indolent tumor; however, for many others, relapse is anticipated after a definitive treatment, which may be swift, forceful, and, in rare instances, insensitive to standard care. As of right now, there is no foolproof way to tell aggressive tumors from indolent ones. But over the past century, significant advancements have changed the prognosis for patients with prostate cancer. These include the groundbreaking finding that the disease is hormone-dependent and the high therapeutic efficacy of using selective inhibitors to target this crucial characteristic, which is now known to be the high expression and frequent genetic amplification of AR. Specifically, the last ten years have witnessed unheard-of breakthroughs in proteome profiling, mRNA sequencing, and whole-genome DNA sequencing, which have offered unique insights into the genetic underpinnings thought to underlie various prostate cancer subtypes and subpathologies. Furthermore, significant advancements in PSA screening protocols and imaging modalities have resulted in their growing usage in the diagnosis of prostate cancer.

The identification of pathogenetically unique tumor types is the primary source of target-specificity in the treatment of complicated diseases, particularly cancer. Enhancements in tumor classification are usually beneficial to therapeutic methods. Target-specific therapy can increase efficacy and limit harm by employing improved classification. Numerous methods and technologies have been used in the past to retrieve biological datasets from these databases. For cancer molecular classification Cancer classification has been split into two difficulties by Golub TR et al.[11]: class prediction and class discovery. In this study, we have chosen a prostate cancer dataset that includes samples with different tumor percentages in order to comprehend how the pattern of gene expression and subsequent functions change as the tumor percentage rises[2, 5, 12-20].

Here, we have chosen a gene expression omnibus (GEO) dataset that contains human samples with a range of tumor percentages (0–85%). We arranged the samples according to tumor proportion in ascending order and compared them to the control group (samples with no tumor) to look for changes in gene expression and developed functions as the tumor percentage increased. Our findings suggest that whereas a number of cancer-associated pathways are consistently enriched for all tumor percentages, the number of differentially expressed genes (DEGs) grows as the tumor percentage rises but the enriched pathways do not. Insulin resistance, acute myeloid leukemia, basal TFs, HIF1-a, neurotrophin, base excision repair, ErbB, VEGF, and mTOR signaling pathways are among the top-ranked enriched pathways. RPP30, SRP14, CCNE1, PRKAR1A, ABCF2, PCMT1, TUBA1C, STOML2, PPP2R4, and TPI1 are possible pathway components.

2. Methods

We have utilized the GEO gene expression profiling array dataset (GSE17951[11]) for prostate cancer. We have included both normal (69) and tumor (68) samples from this expression dataset in our study. Tumor samples range in tumor percentage from 0.5 to 85%. Affymetrix Human Genome U133 Plus 2.0 Array results were used to create these gene expression profiling datasets. There are 154 samples in this dataset (69 normal or tumor-free, 68 with a tumor percentage of up to 85%, and 17 nearby stroma samples); the latter 17 samples have been removed from the analysis. We have analyzed the tumor and normal samples for differential gene expression analysis such that we have 32 DEG lists for 68 samples (tumor % > 0.0). In summary, raw fille processing, intensity computation, and normalization are the fundamental processes that are engaged in the entire investigation. The most popular methods for normalization are EB, RMA, and GCRMA. Here, we have normalized raw intensity using EB[21-27]. Following normalization, we go on to our objective, which is to comprehend the patterns of gene expression and the functions that may be deduced from them[28-43].

MATLAB functions, such as mattest, have been utilized for statistical analysis and the prediction of differential gene expression. We used the KEGG database for pathway analysis and wrote our own tool for network and pathway analysis (Figure 1a). Throughout the project, FunCoup2.0 was utilized to generate the DEGs networks, and Cytoscape[44] was utilized to visualize the networks. We have used MATLAB for the majority of our coding and computations. Four sorts of functional couplings or linkages, including protein complexes, physical interactions between proteins, metabolic processes, and signaling pathways, are predicted by FunCoup[45].

3. Results

Figure 1. Evolution of DEGs with the increase in tumor percentage. (a) Workflow. (b) Venn diagram to display the DEGs. (c) Evolution of DEGs with the increase in Tumor percentage: To analyze the DEGs for different percent of tumor in the biopsy samples, we have arranged the tumor samples based in increase order. In each step we add next two samples with higher tumor percent and have calculated DEGs. We observe that with the increase in tumor percentage number of DEGs are increased which means that the samples with higher tumor percent have higher level of gene expression aberrations. (d) Evolved enriched pathways with the increase in tumor percentage.

3.1. Gene expression profiling and the associated functions for varying tumor percentages: As indicated in the workflow Figure 1a, we have first chosen the data of interest (raw expression dataset) GSE1795 and processed it till normalization and log2 values of all the mapped genes are obtained. There are 154 samples in this dataset (69 normal or tumor-free, 68 with a tumor percentage of up to 85%, and 17 nearby stroma samples); the latter 17 samples have been removed from the analysis. We have analyzed the tumor and normal samples for differential gene expression analysis such that we have 32 DEG lists for 68 samples (tumor % > 0.0).

In this study, we examined the DEGs (Figures 1b and c) at varying tumor percentages and found that many genes are frequently altered regardless of tumor percentage, and that the number of DEGs rises as tumor percent rises (Figure 1c), but the number of pathways does not follow the same pattern (Figured 1). The number of enriched pathways varies significantly at lower tumor percentages, but it roughly stabilizes in samples with tumor percentages greater than 50%.

Figure 2. Major pathways and and their potential components. (a) Top ranked enriched pathways for different tumor percentage, (b) top ranked genes, and (c) Enriched pathways (between normal and tumor) analyzed for prostate cancer without classifying tumor percentage (GSE17951).

3.2. Top ranked enriched pathways and DEGs: Following the prediction of DEGs and enriched pathways, we examined enriched pathways and genes that were nearly changed in every tumor sample. The top ranked pathways that are commonly changed are insulin resistance, AML, basal TFs, HIF-1, neurotrophin, base excision repair, and ErbB signaling (Figure 2a). The top ranked genes are RPP30, SRP14, CCNE1, PRKAR1A, ABCF2, PCMT1, TUBA1C, STOML2, PPP2R4, TPI1, TUBB2B, and so on (Figure 2b). It is known that the identified genes and pathways contribute to cancer either directly or indirectly. The bulk of the top-ranked pathways (Figure 2a) belong to overall enriched pathways (between normal and tumor samples) (Figure 2c), as we have incorporated additional data (GSE17951) to portray the enriched pathways for greater clarity.

Figure 4. Clinical significance of some of the DEGs which display higher connectivities. These genes are overexpressed in prostate cancer sample (TCGA database). The percentage represents the number of patients with the respective genes overexpression.

Figure 3. Networks of DEGs. Here, the arrow thickness between the networks refer that the next network is for the DEGs at higher tumor percentage. The nodes with red color boundary are those genes which are overexpressed in the beginning when tumor percentage is less than 6 and have higher connectivity throughout irrespective of tumor percentage.

3.3. Network-level understanding of the DEGs: We have created the network of DEGs at extremely low tumor percentages (0.0%) and up to the maximum (85%) following the analysis of the DEGs and the enriched pathways. The DEGs at five distinct tumor percentages are covered by the first through the fifth of these five networks (Figure 3). Nearly every gene that is frequently differentially expressed is covered by the DEGs list that we have created for the network. Even in the network, we find that the majority of the genes are frequently involved. These genes are known to have very high connectivities, and the node degree distribution is power law distributed (Figure 3). It is also known that these genes may or may not be involved in cancer. Genes that are overexpressed at the start when the tumor percentage is less than six and have stronger connection throughout, regardless of tumor %, are shown by the nodes with red color boundaries (Figure 3). This leads us to believe that these genes may be responsible for the development of prostate cancer in humans. A list of genes that are overexpressed across the tumor proportion is displayed in Figure 4 and for which we have examined the clinical importance. There are some genes that are overexpressed in over 5% of patients; to address this, we have the TCGA database through cBioPortal.

 

4. Dicussion: The development of the post-genomics period in recent decades has produced a large amount of “big data” in the biological sciences, which opens up a wide range of interdisciplinary applications. The enormous volume of datasets has also made it more difficult to handle, process, mine, store, and extract useful information. Numerous studies on various forms of cancer have been conducted, and the raw data from these studies are openly accessible. Numerous research at various levels have been conducted. We have chosen a dataset from the early work that includes both normal samples and patients with varying tumor percentages. In order to comprehend how the gene expression pattern and resulting functions change as the tumor proportion rises, we have chosen this prostate cancer dataset with different tumor percentages[17, 23, 46-60].

The gene expression omnibus (GEO) GSE17951 dataset includes human subjects with a range of tumor percentages (0–85%). We arranged the samples according to tumor proportion in ascending order and compared them to the control group (samples with no tumor) to look for changes in gene expression and developed functions as the tumor percentage increased. Our research leads us to the conclusion that whereas a number of cancer-associated pathways are consistently enriched for all tumor percentages, the number of differentially expressed genes (DEGs) grows as the tumor percentage rises while the enriched pathways do not. To display the enriched pathways, we have also included extra data. As we can see in Figure 2c, the bulk of the top-ranked pathways (Figure 2a) are part of the overall enriched pathways (between normal and tumor samples).

In contrast, we concentrated on the dataset and primarily identified those genes and pathways that continuously remain altered regardless of the tumor percentage. Previous research, even in similar types of cancer, has revealed intriguing genes and pathways associated with the specific type of cancer. Remarkably, the most commonly altered pathways are insulin resistance, AML, basal TFs, HIF-1, neurotrophin, base excision repair, and ErbB signaling (Figure 2a). The most frequently altered genes are RPP30, SRP14, CCNE1, PRKAR1A, ABCF2, PCMT1, TUBA1C, STOML2, PPP2R4, TPI1, TUBB2B, and so on (Figure 2b). It provides accurate information about the genes and pathways that may show promise in the selective targeting of prostate cancer and aids in its comprehension and use for diagnostic purposes.

Multidisciplinary research on prostate cancer is quite busy and currently includes computational biology in addition to laboratory and clinical science. Among these studies are the exploration of novel preclinical hypotheses, the experimental confirmation of scientific discoveries, and the application of these discoveries in clinical settings. Before conducting clinical studies to try to enhance disease management, several steps are crucial. The design and specificity of new medicines and treatment plans, such as those that more effectively target important aspects of AR biochemistry, have also improved as a result of a greater understanding of the disease’s molecular underpinnings. From improved biological knowledge of each disease stage that influences clinical care to early illness identification and therapy, progress is ongoing in a number of domains[61-68].

Millions of men throughout the world suffer from prostate cancer. The illness makes up 7% of newly diagnosed male cancers worldwide (15% in industrialized nations), making it the second most frequent cancer in males after lung cancer. Prostate cancer is also one of the top causes of cancer-associated death in males, with over 1.2 million new cases diagnosed and over 350,000 deaths worldwide each year. The risk of prostate cancer rises significantly with age, and more than 85% of newly diagnosed cases occur in people over 60. Therefore, areas with high life expectancy, like the USA and the UK, have a notably high prevalence of prostate cancer. Globally, the incidence of prostate cancer is positively correlated with both GDP and the human development index (HDI), meaning that developed countries typically have greater incidences than undeveloped countries. It’s interesting to note that, although the incidence is rising in these regions, certain Asian nations with high HDIs, including South Korea and Japan, have relatively lower incidences than Western nations with comparable high HDIs[69-90].

Since greater screening frequency is linked to increased incidence through overdiagnosis, the rise in incidence may be the result of increased awareness of prostate cancer brought about by access to diagnostic screening in many of these regions. Furthermore, these areas have the highest age-standardized mortality rates from prostate cancer, while early detection access is anticipated to lower these rates. Repeated screening lowers the mortality rate from prostate cancer and boosts the diagnosis of all prostate tumors, including indolent ones, according to European studies using long-term follow-up data. The causes of the increasing age-adjusted mortality in emerging countries may also be related to the fact that economic development is linked to a rise in prostate cancer risk factors that surpasses the advantages of advancements in public health and treatment. Although there is insufficient evidence to support an impact on disease incidence, non-heritable variables such as obesity, cigarette smoke exposure, and a primarily Western diet are generally believed to increase prostate cancer-related mortality.

5. Conclusions: Our research leads us to the conclusion that whereas a number of cancer-associated pathways are consistently enriched for all tumor percentages, the number of differentially expressed genes (DEGs) grows as the tumor percentage rises while the enriched pathways do not. This indicates that, regardless of the tumor percentage, only specific pathways may be changed in cases of prostate cancer.

Author Contributions: Conceptualization, M.A.T., M.Y.R., L.A.B., H.M.B., A.A.B., and P.K.S.; methodology, M.A.T., M.Y.R., L.A.B., H.M.B., A.A.B., and P.K.S.; software, M.A.T.; validation, M.A.T. and M.Y.R.; formal analysis, M.A.T., M.Y.R., L.A.B., H.M.B., A.A.B., and P.K.S.; investigation, M.A.T.; resources, M.A.T. and M.M.; data curation, M.A.T. and M.Y.R.; writing—original draft preparation, M.A.T., M.Y.R., L.A.B., H.M.B., A.A.B., and P.K.S.; writing—review and editing, M.A.T., M.Y.R., L.A.B., H.M.B., A.A.B., and P.K.S.; visualization, M.A.T.; supervision, M.A.T. and H.M.B.; project administration, M.A.T.; funding acquisition, M.A.T. The author has read and agreed to the published version of the manuscript.

Funding: Not applicable.

Acknowledgments: We are grateful to the College of Science, Department of Biochemistry, University of Jeddah, Jeddah, P.O. Box 80327, Jeddah, 21589, Saudi Arabia for providing us all the facilities to carry out the entire work.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: A signed participant informed consent was obtained from every participant before enrollment in the study. Confidentiality will be assured by permitting anonymity data storage. The information is only accessible to authorized research team personnel.

Data Availability Statement: All the related data are supplied in this work or have been referenced properly.

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