Abstract
Background/Aim: Pancreatic cancer (PC) has one of the highest mortality rates, with an overall five-year survival rate of only 7%. When diagnosed, PC is limited to the pancreas in only 20% of patients, whereas in 50% it has already metastasized. This is due to its late diagnosis, which makes the treatments used, such as radiotherapy, difficult, and reduces survival rates. Therefore, the importance of this study in detecting genes that may become possible biomarkers for this type of tumor, especially regarding the human secretome, is highlighted. These genes participate in pathways that are responsible for tumor migration and resistance to therapies, along with other important factors. Materials and Methods: To achieve these goals, the following online tools and platforms have been expanded to discover and validate these biomarkers: The Human Protein Atlas database, the Xena Browser platform, Gene Expression Omnibus, the EnrichR platform and the Kaplan-Meier Plotter platform. Results: Our study adopted a methodology that allows the identification of potential biomarkers related to the effectiveness of radiotherapy in PC. Inflammatory pathways were predominantly enriched, related to the regulation of biological processes, primarily in cytokine-derived proteins, which are responsible for tumor progression and other processes that contribute to the development of the disease. Conclusion: Radiotherapy treatment demonstrated greater efficacy when used in conjunction with other forms of therapy since it decreased the expression of essential genes involved in several inflammatory pathways linked to tumor progression.
Although pancreatic cancer (PC) is not as common as other types of cancer, it has one of the highest mortality rates. The overall five-year survival rate is about 7%; for patients undergoing surgical resection, this percentage increases to 15-25% (1). This type of cancer affects people aged between 60 and 80 years old and is more common in men, 50% higher than in women (2).
The low survival rate is due to the difficulty of early diagnosis, as most patients are asymptomatic, and only show signs of the disease during the more advanced stages (3). The disease is limited to the pancreas in only 20% of patients (stage 1 and 2), another 30% of cases develop locally, where the cancer is linked to most of the vascular vessels (stage 3), while 50% of cases are diagnosed in the metastasis stage (stage 4) (4). Among all types of PC, the most common is pancreatic ductal adenocarcinoma (PDAC), comprising approximately 95% of cases. It is defined as an infiltrative neoplasm with glandular differentiation derived from the pancreatic ductal tree (5).
Surgical resection remains the curative mainstay in the treatment of pancreatic tumor, but its effectiveness alone is not enough to suppress the disease, since more than 90% of patients relapse or die from the disease after the procedure without additional therapy. One of the alternatives discovered for this problem is a combination of adjuvant chemotherapy after surgery (6). However, PDAC has a predisposition to developing chemoresistance to the drugs used, hindering the action of the therapy, and decreasing its effectiveness (7).
In this case, another method indicated is radiotherapy (RT), which consists of the destruction or reduction of the tumor using ionizing radiation. In the direct interaction, high-energy photons interact with electrons, causing damage to the DNA strands of cancer cells (8). In the indirect interaction, the process of radiolysis occurs when radiation interacts with water molecules promoting the formation of free radicals and affecting DNA or proteins (9). In addition to the photon beam, other types of radiation are also used, such as particle radiation with neutrons, protons, and carbon ions (10). Usually, RT is administered in conjunction with other therapies and can be used as a curative approach to eradicate tumors, as well as palliatively to mitigate symptoms caused by cancer. It can also be performed as a neoadjuvant treatment to limit or decrease tumor size, such as adjuvant therapy to prevent disease recurrence (11).
Generally, the recommended dose in conventional RT (XRT) is 45-54 Grays (Gy) in fractions of 1.8 to 2 Gy, with 25-27 total fractions. These numbers may vary if RT is applied together with other treatments (12). Another method, stereotactic body radiation therapy (SBRT), is playing a growing role in the neoadjuvant treatment of patients where tumors are borderline and may, or may not, undergo resection. SBRT uses high doses of radiation per fraction (>5 Gy) in fewer fractions (from 1 to 5) to deliver focal and ablative doses directly to the tumor, decrease tumor spread and allow a negative margin resection (when there are no tumor cells near the edges of the removed tissue) (13).
These and other radiotherapy methods can be used in conjunction with chemotherapy. Chemoradiotherapy (CRT) is still very uncertain for PDAC due to conflicting results from disease-related studies. Although good responses have been obtained, considerable toxicity and side effects have been noted (14). However, most PC tumors are radioresistant, which is one of the main reasons for the ineffectiveness of RT. PDAC induces activation of stellate cells, producing large amounts of extracellular matrix (ECM) proteins, thereby increasing resistance around cancer (extensive fibrosis). This rigidity induces blood vessel collapse and consequently heterogeneous expansions of low oxygen concentration (hypoxia), a factor that can prevent cell death driven by radiation (15). Due to this, the importance of finding alternatives to improve PC treatments is highlighted, to improve the response to therapy and, consequently, the quality life of patients.
The use of possible biomarkers has gained great attention from researchers in recent years, as they help in the early detection of the disease, which improves the prognosis and predicts how the tumor will respond to certain treatments (16). Given this, the secretome has been widely used to study these markers, since the molecules of this set are directly secreted by the tumor, and from there, they circulate around the body, allowing communication between the tumor and its microenvironment (17). This same group also allows a liquid biopsy to be carried out, since this method searches for tumor biomarkers circulating in the blood or even in the saliva, which is considered a less invasive exam compared to a traditional biopsy (18). Along with the prediction of biomarkers, the use of bioinformatic tools and web servers has seen advances, since they store omics data (data from the genome, transcriptome, miRnoma, proteome, etc.) from clinical samples (19, 20). The aim of our study was to adopt a methodology that allows potential biomarkers to be identified that relate to the effectiveness of radiotherapy in PC.
Materials and Methods
Reference secretome selection. The secretome list used as a reference was obtained from The Human Protein Atlas database (https://www.proteinatlas.org/) (21). A list of 1,904 proteins secreted in the blood, brain, digestive system, male and female reproductive systems, extracellular matrix, in different locations such as the eye or skin and unknown locations were selected. This list was chosen because it excludes proteins that are associated with the membrane or in intracellular compartments, such as endosomes or lysosomes, and thus emphasizes only proteins that are secreted by different cell types.
Downloaded data of normal and tumor samples. After obtaining the list with 1904 proteins, it was submitted to the Xena Browser platform (https://xenabrowser.net/) (22). On this platform, studies that include samples of normal tissue, obtained from Genotype Tissue Expression (GTEx), and pancreatic cancer primary tumor from The Cancer Genome Atlas (TCGA) were used as parameters. Subsequently, the following parameters were selected: Data type: Phenotypic; Phenotypic: sample type and primary site. Finally, filters for primary pancreas tumors were applied before the secretome list was submitted to the tool. It obtained the transcriptome data of these genes in the different samples that were found in other databases. Expression data were obtained in counts (which is the raw value of the expression, without normalization), and samples that lacked expression of the listed proteins, or that did not represent primary tumor and normal tissue, were excluded.
High purity tumor specimen filtration. The filtrations of samples that showed high purity were performed according to Raphael et al. (23), who used the ABSOLUTE algorithm method to assess tumor purity and estimated a median of 33% variation. A list of high purity pancreas tumor samples was obtained from the supplementary material (available from the authors) from the article. Samples with a purity index ≥33% were selected, and later these were compared to the list of tumor samples from both tumors obtained in XENA. The samples present in both lists were selected as samples with high purity and were used for further analysis.
Differential protein expression analysis. To determine which proteins were altered in the tumor tissue compared to normal proteins, the Appyters tool (https://appyters.maayanlab.cloud/#/) was used (24). The data are RSEM expected counts, therefore the EDGER analysis method was used, selecting a p-value <0.05 and log2FC >1.5.
Separation of differentially expressed genes related to radiotherapy efficacy. To analyze gene expression in patients undergoing radiotherapy, the Gene Expression Omnibus (GEO) was used. The analyzed data were taken from two different studies available on the GEO platform. The first study, from Farren et al. (GSE129492) (25), presents data in a microarray, with 24 samples from PC patients undergoing 4 types of treatment: surgical resection only (control); chemotherapy; chemotherapy followed by SBRT and chemotherapy followed by XRT, all as neoadjuvant therapies and with 6 patients each. Since it is a microarray, analysis was performed in limma on the GEO2R platform, with log2FC >1.5 and p-value <0.05. Groups treated were analyzed and the samples were compared with the PC samples from the TCGA, obtained previously, which presented up-regulated genes. The second study by Mills et al. (GSE185311) (26), presents RNAseq data, with 22 samples from patients with PC, divided into two groups: untreated (13 samples) and treated with SBRT radiotherapy (9 samples). To identify which genes were up or down-regulated, an analysis was performed on the Appyters platform, through DESeq2 (27), with a p-value <0.05 and log2FC >1.5. In both studies, expression data were obtained in counts.
Volcano plot construction. Volcano plots were built using the volcanozeR tool (28). The value 1.5 of significance was used in FoldChange and 0.05 in p-value.
Functional enrichment of differentially expressed genes. The EnrichR platform (https://maayanlab.cloud/Enrichr/) was used to determine the biological pathways and processes influenced by the previously identified DEGs. Within this, the following databases were selected for analysis: WIKIPATHWAYS 2021 HUMAN; KEGG 2021 HUMAN; REACTOME 2021; NCI-NATURE 2021; GENE ONTOLOGY BIOLOGICAL PROCESS and GENE ONTOLOGY MOLECULAR FUNCTION. With these data, it was possible to identify the metabolic pathways that were influenced by the identified DEGs, in addition to the biological processes involved. Analyses were performed on down-regulated genes. Graphs were generated according to p-value <0.05 and the percentage of enriched genes per term.
Immunohistochemical validation of down-regulated genes. For a better demonstration of protein expression in tissues and cells between normal and tumor tissues, antibodies were validated using the immunohistochemistry method through The Human Protein Atlas platform (https://www.proteinatlas.org/).
Survival curve validation. Subsequently, the prognosis linked to genes highlighted as possible biomarkers was evaluated through the Kaplan-Meier Plotter platform (https://kmplot.com/analysis/) (29) with the Pan-cancer RNA-seq database using the data from pancreatic adenocarcinoma, where survival curves were generated for each indicated biomarker.
Results
After submitting the 1,904 secretome proteins to the Xena Browser platform, spreadsheets referring to normal and tumor tissue of the pancreas were obtained. Thus, a total of 345 samples were obtained, where 167 came from normal tissue, which belongs to the GTEx and 178 came from primary tumor tissue samples, referring to the TCGA database.
In the next step, tumor samples were identified that showed a high level of purity without many infiltrates of inflammatory cells. Thus, according to Raphael et al. (23), 76 samples with high pancreas tumor purity were found. Using the EdgeR tool, it was possible to analyze which genes are differentially expressed in the PC samples (Figure 1). For an adjusted p-value <0.01, when we compared normal tissue samples with primary PC tumors, 283 significant genes were found, among them, 240 up-regulated and 43 down-regulated.
Differentially expressed genes comparing normal tissue samples from GTEx database and PC tumor tissue samples from TCGA database, using the EdgeR analysis from Appyters tool. Down-regulated genes are shown in blue, up-regulated genes in red and non-significant genes are shown in gray.
In the study carried out by Farren et al. (GSE129492) (25), after analyzing the defined groups, it was possible to identify differentially expressed genes in four comparisons: chemotherapy and chemotherapy treatments followed by XRT (Figure 2A); chemotherapy and chemotherapy treatments followed by SBRT (Figure 2B); surgical resection (control) versus chemotherapy followed by XRT (Figure 2C) and surgical resection (control) versus chemotherapy followed by SBRT (Figure 2D). In the study carried out by Mills et al. (GSE185311) (26), 8 DEGs were found when comparing the untreated patient group with those treated with SBRT, which were 3 up-regulated DEGs and 5 down-regulated DEGs (Figure 3). The number of DEGs found in the group comparisons is shown in Table I.
Volcano plot of the expression of significant genes present in four groups of comparisons in the study GSE129492 using the GEO2R tool. (A) Chemotherapy versus chemotherapy followed by conventional radiotherapy (XRT); (B) chemotherapy versus chemotherapy followed by stereotactic body radiation therapy (SBRT); (C) surgical resection (control) versus chemotherapy followed by XRT; (D) surgical resection (control) versus chemotherapy followed by SBRT. Note the greater number of down-regulated genes (in blue) compared to the up-regulated genes (in red).
Volcano plot of the expression of significant genes present in the groups of patients treated and not treated with SBRT in GSE 185311 using the GEO2R tool. Our study identified 3 up-regulated genes (GDF15, CDKN1A and CRP) and 2 down-regulated genes (CCL21 and TNFRSF13C).
Differentially expressed genes from the GSE129492 and GSE185311 studies.
After aggregating both studies, a list was obtained with 363 genes with low expression after radiation treatment. We then compared the final list of low expression DEGs in each treatment with the list of up-regulated genes in the PDAC samples with high purity from TCGA. Thus, 7 genes were obtained as potential biomarkers, namely interferon gamma (IFNG), interleukin 1 alpha (IL1A), interleukin-12 subunit beta (IL12B), interleukin 26 (IL26), leukocyte associated immunoglobulin like receptor 2 (LAIR2), semenogelin 1 (SEMG1) and X-C motif chemokine ligand 2 (XCL2).
Observing the 7 down-regulated genes that had been enriched, a high enrichment of inflammatory pathways (Figure 4) could be found. In the ontology databases, the presence of several terms relating to the production and action of cytokines was evident. This pattern was repeated when we searched the pathway-related databases.
Enrichment graph of the 7 down-regulated genes identified (IFNG, IL1A, IL12B, IL26, LAIR2, SEMG1 and XCL2), from ontology databases. (A) GO: BIOLOGICAL PROCESS, GO: MOLECULAR FUNCTION and pathways databases; (B) REACTOME 2021 and NCI-NATURE 2021 databases.
As shown in Figure 5, out of the 7 genes found as potential biomarkers of radiotherapy efficacy, only 4 (IFNG, IL1A, IL12B, SEMG1 showed a significant difference in survival analysis (p-value <0.05). In the immuno-histochemical analysis (Figure 6), only 2 genes, IFNG and IL12B, showed changes in their expression when comparing the Pancreas Tissue and Pancreas Pathology in “The Human Protein Atlas” database. The genes IL1A, IL26 and LAIR2 have no pathological samples in the database. The genes SEMG1 and XCL2 had no immunological detection in the normal and tumor samples present in the database.
Survival curves generated from the identified potential biomarkers: IFNG (A), IL1A (B), IL12B (C), SEMG1 (D). Survival curves were generated with the KMPlotter tool.
Immunohistochemical validation of IFNG (A), and IL12B (B), in normal and tumoral pancreatic tissue from the database “The Human Protein Atlas”.
Discussion
PC is known to have a high mortality rate, mainly due to its late diagnosis, with about 50% of patients diagnosed with metastasis (4). Due to this, the detection of biomarkers for this tumor has been widely studied in the scientific world using online tools, because in addition to being easy to access with low cost, it is not necessary to perform invasive tests on patients (19). However, studies emphasize chemotherapy, and do not consider the discovery of radiotherapeutic biomarkers. This is extremely important since treatment for PC can be performed in a neoadjuvant manner, enhancing treatment when the response to chemotherapy is low (30). Our study aims to identify DEGs that may work as potential biomarkers for PC, focusing on genes of proteins secreted by the tumor and that can be detected in the blood. Through a combination of tools, we identified 7 potential biomarkers (IFNG, IL1A, IL12B, IL26, LAIR2, SEMG1 and XCL2), which had their expression significantly reduced after treatment with radiotherapy techniques.
IFNG biomarker. Tumor development has uninterrupted cellular communication through cytokines that determine an inflammatory profile, favoring progression, angiogenesis, cell invasion, and metastasis (31). Of the identified biomarkers, IFNG has a very specific behavior in cancer. This molecule is produced mainly by NK and T cells as a response to a certain inflammatory stimulus (32) and is fundamental for the early combat of pathogens and adverse conditions. IFNG stimulates the production of class II MHC transactivator (CIITA), which leads to increased expression of class II MHC on the cell surface. In addition, it acts as a signal for the differentiation of CD8+ cell production (33, 34). In the PC, it was demonstrated that the use of IFNG associated with programmed cell death ligand 1 (PD-1) inhibitors led to a lower expression of CXCL8 (IL-8), a neutrophil chemotactic factor that could be enhancing angiogenesis, survival, proliferation, metastasis, and multidrug resistance (35) and the receptor CXCR2, thus decreasing the immune evasion of this prognosis and consequently for a better prognosis (36).
However, there are also reports that IFNG has a protumoral role, since IFNG alone leads to a greater expression of PD-L1, which modulates the tumor microenvironment, and thus leads to T cell exhaustion and a consequent immune evasion (37). This is evident in several tumor types. In colon cancer, IFNG promoted tumor evasion through a lower expression of gp70 antigens. Moreover, in melanoma, the higher expression of IFNG led to a higher expression of MHC II causing a more aggressive phenotype (32, 38). Furthermore, there is evidence that IFNG participates in the mesenchymal epithelial transition in papillary thyroid cancer, besides expressing molecules such as MUC16 and CLTA-4 that generate tumor tolerance (32). In PC, this IFNG-induced resistance is present and can be broken down by CDK 1/2/5 inhibitors (39). Therefore, the role of IFNG in tumor progression remains a paradox, where different factors influence its pro or antitumor activity (31, 40, 41). As such, our results demonstrate that a lower expression leads to a higher survival rate, so there are indications that it is an important biomarker to predict the effectiveness of radiotherapy in PDAC.
IL1A biomarker. Another gene recognized in our study was IL1A, which is responsible for the production of interleukin 1alpha, and plays a key factor in the inflammatory process, inducing the production of proinflammatory mediators, such as COX2, IL-6, and TNF, considerably amplifying the inflammatory process. This is very important when observing the relationship of this molecule with tumor progression, since COX2 and IL-6 can modulate the tumor microenvironment, helping tumor cells to survive and proliferate (42). Another point is that IL-1α has been shown to act by recruiting adhesion, invasion, and angiogenesis molecules in pancreatic cancer cell lines co-cultured with fibroblasts (43). In addition, an important feature that demonstrates the centrality of this gene in tumor progression is its polymorphisms, where numerous SNPs of this gene were detected in different populations that can increase the predisposition and malignancy of different tumor types (44).
Emphasizing PDAC, studies show that IL1A plays a central role in its invasiveness, as it plays a fundamental role in the greater expression of MMP1 and MMP3, besides inhibiting TIMP3, which acts as an inhibitor of the previously mentioned MMPs (45). A correlation was detected between the increase in IL1A expression and the modulation of the PDAC microenvironment, with emphasis on the recruitment of undifferentiated macrophages (M0), which is a malignant inflammatory profile (45). It is known that IL1A acts on the NF-kB signaling pathway, a transcription factor involved in cell proliferation, survival, and angiogenesis, including tumoral cells (46). Finally, IL1A can indirectly stimulate the migration of Th2 cells to the tumor microenvironment, correlated with reduced patient survival in PC (47). Therefore, an indication of a decreased IL1A expression in PDAC submitted to radiotherapy, warns of the importance of this being identified as a potential biomarker for this treatment, after all, it is related to several key processes for tumor progression and metastasis.
IL12 biomarker. The behavior of cancer, even though there are several studies, is still a complex field, as there are genes that act in several non-specific ways, and there may be duplicity in the fight or progression of the tumor.
The cytokine Interleukin 12 (IL12) was originally identified as pro-inflammatory, and played an important role in cellular immunity, acting as a stimulatory factor for natural killer (NK) cells and T cells, a maturation factor for cytotoxic lymphocytes (NKSF) and induces the production of cytokines such as IFN-γ (48). It is produced by phagocytic cells, such as dendritic cells, monocytes and macrophages that support the activity as an antitumor agent (49).
IL12 is encoded by two genes, IL12A (p35) and IL12B (p40), and since this cytokine performed well in tumorigenesis, some studies are being carried out to evaluate the polymorphisms of these two genes with risks linked to cancer progression (47, 48). Chen et. al. (50) and Karimi-Zarchi et al. (49) reported that the IL12B rs3212227 AC/CC genotypes are associated with a high risk of cervical cancer, particularly in women of Asian ethnicity. Some studies have also proposed that IL12B may have a risk role in other types of cancer, such as lung cancer (51), colorectal cancer (52), and hepatocellular carcinoma (53).
The role of IL12B in PC is still uncertain and controversial, and few studies have been carried out relating to this type of tumor. Han et al. (54) indicated that myofibroblasts adjacent to PDAC tumors overexpress the IL12B gene, stimulating an immune response, which is correlated with patient survival.
According to these findings, the importance of new studies is highlighted to analyze the behavior of the IL12B gene in cancer, especially in PC, since it appears to act in an oncogenic and oncosuppressive manner. It is also extremely important to carry out an in-depth verification of how this gene is related to PDAC treatments, such as radiotherapy, to define the ideal therapy for each patient.
IL26 biomarker. Another gene found is IL26, an amphipathic member of the IL-10 cytokine family and the IL-20 subfamily (55). This is known to participate in inflammatory signaling through canonical receptors and to help with intracellular inflammatory signaling and cellular transduction directly binding with DNA (51, 56). T cells, more specifically Th17 cells, are responsible for producing the interleukins IL-26 and deregulated function of these cells can contribute to the development of certain diseases, such as cancer (57). Trotter et al. (58) indicated that IL26 can be a new biomarker for metastasis in breast cancer. Recently, Sun et.al (59), demonstrated through studies of single-cell RNA-seq (scRNA-seq), that exhausted CD8+ T cells associated with the gastric tumor, act in the promotion of its proliferation, through the signaling mediated by IL26. Also, Xi et al. (60) linked high IL26 levels with poor overall survival for colorectal cancer.
Regarding PDAC, the activity of IL26 is still unknown, and requires further studies of the gene to combat this tumor type (61). In the study by Mayer et al. (62) IL26 was described to bind to a heterodimeric receptor, which consists of IL10RB and IL20RA, two factors found in tumor cells. Furthermore, it is important to note that T+ cells for IL26 are related to a prognosis for PDAC and are associated with a poor tumor promoter subpopulation. With these findings, the importance of new studies on IL26 as a possible biomarker in radiotherapy treatment for CP is highlighted, due to the role of this gene in relation to the development of the disease.
LAIR2 biomarker. LAIR2 human protein is a soluble receptor produced by leukocytes that antagonizes LAIR1 functions (63, 64). The common situation of collagen overexpression in several tumor types that interact with LAIR1 has been associated with a poor prognosis (65) since this receptor inhibits NK cell activity, T cell and other immune cells (66). Studies show that LAIR2 may be a potential therapeutic target, given that, when it is more expressed than its analog LAIR1, there was a reduction in lung cancer growth and less metastasis (67). In addition, this molecule has already been described as having a better prognosis, inhibiting immunosuppression, and sensitizing T cells hybridomas (68) when binding to collagen. The role of this gene in PC is also still poorly described, along with how it interacts with radiotherapy.
SEMG1 and SEMG2 biomarkers. Semenogelin 1 and Semenogelin 2 (SEMG1 and SEMG2, respectively) are two non-X-linked CTAs that normally regulate motility and the maturity of sperm but become re-expressed in different cancerous tissues (69). SEMGs have been found expressed in various malignancies including prostate cancer (70) and lung cancer (71). Both SEMG1 and SEMG2 have biological activity in tumors, both speed up energy metabolism, and possess anti-proliferative qualities (69). Through a dataset reanalysis, SEMG1 expression in different cancers was assessed. SEMG1 expression was found to be negatively linked to the survival rate of cancer patients with large B-cell lymphoma, colon, lung, and PDAC (72). Interestingly, in contrast to the activity seen previously, SEMG1 appears to play a protective role in renal cancer (73), where Kaplan-Meier survival curves and log-rank tests demonstrated a positive relation between SEMG1 expression and a prognosis. In addition, there was a significant relationship between SEMG1 expression and tumor recurrence. Patients with SEMG1-negative tumors had a higher risk of recurrence than those with SEMG1-expressing tumors. Shuvalov et al. (74) showed that SEMG1 and SEMG2 proteins exhibit oncosuppressive properties in a model of pancreatic carcinoma, using an in vitro assay with Mia-Paca 2 cells. Thus, the role of SEMG1 needs further investigation on how it acts in other tumor types such as PC, as, depending on the specific cellular scenario, SEMGs can exhibit both oncogenic and oncosuppressive characteristics.
XCL2 biomarker. Many studies have shown evidence of the role of chemokines in the pathogenic regulation of several tumor types, in events such as proliferation, migration, and invasion, in addition to cell signaling to adjacent tissues acting as tumor promoters or suppressors (75). XCL2 is a chemokine of the XC family, often associated with another chemokine – XCL1. In numerous tumor types, this chemokine appears to be associated with a worse prognosis (76). Higher expression of XCL1 was seen in lung tumor cells in a study compared to nearby normal tissues (77). In addition, chemokine expression was higher depending on the pathological stage of the tumor tissue, where tumor tissue with a higher number of metastatic lymph nodes showed greater expression when compared to tissues with a lower number of metastatic lymph nodes (77). These data point to a critical function for this chemokine in lung cancer, with implications for other tumor types. In our study, we found that the XCL2 chemokine was downregulated by radiotherapy in PDAC, indicating the effectiveness of this therapy.
In summary, the use of a bioinformatic analysis performed with caution has shown that there are great prospects in basic research for the early screening of tumoral diseases, as well as advanced treatments for personalized medicine. Since these diseases are multifactorial and can come from different cells within the same tissue, it would be impossible to track biomarkers without the aid of computer analyses such as the one in the present study.
Conclusion
Most cancer studies are aimed at chemotherapy treatment, which is demonstrated by the small numbers of data in the database on patients undergoing radiation therapies. However, radiotherapy is widely used in cancers such as breast, lung, prostate, among others. In this bioinformatics study into PDAC, radiotherapy treatment demonstrated greater efficacy when used in conjunction with other forms of therapy since it decreased the expression of essential genes involved in several inflammatory pathways linked to tumor progression. This has led to new hypotheses for studies with those possible candidates for biomarkers of their effectiveness for treating PC.
Acknowledgements
This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq, and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and supported by Sao Paulo Research Foundation (FAPESP) (process No. 2019/01869-7).
Footnotes
↵* These Authors contributed equally to this study.
Conflicts of Interest
The Authors declare no conflicts of interest.
Authors’ Contributions
Gabriel H. Caxali: Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Laíza Brugnerotto: Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Mirian C. E. Aal: Formal analysis, Investigation, Writing – review & editing. Camila Ferreira Bannwart Castro: Formal analysis, Investigation, Writing – review & editing. Flávia K. Delella: Conceptualization, Writing – review & editing, Project administration, Funding acquisition.
- Received March 27, 2023.
- Revision received May 1, 2023.
- Accepted May 15, 2023.
- Copyright © 2023, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
