Identification of Immune-Related LncRNA Pairs for Predicting Prognosis and Immunotherapeutic Response in Head and Neck Squamous Cell Carcinoma

Biomarker Potential of LINC00313 in Head and Neck Squamous Cell Carcinoma: Correlation with Epithelial-Mesenchymal Transition and Immune Cell Infiltration

Although LINC00313 is dysregulated in several tumors, its role in head and neck squamous cell carcinoma (HNSC) is not fully understood. The aim of this study was to analyze the role of LINC00313 in HNSC. The clinical information and LINC00313 expression data of HNSC were mined from the TCGA/GEO/cbioportal database. The correlation between LINC00313 expression and immune cell infiltration in HNSC tumors was analyzed by bioinformatics and gene enrichment analysis was performed. LINC00313 was silenced in HNSC cell lines, and changes at the genetic and molecular levels were verified through qRT-PCR and Western blotting. The researchers also validated its functional phenotype through a series of cell function experiments. The results showed that overexpression and copy number variation of LINC00313 in HNSC were associated with poorer prognosis. In addition, LINC00313 expression was significantly negatively correlated with immune cell infiltration. Silencing of LINC00313 in HNSC cells significantly reduced the rate of cell migration. LINC00313 may affect the progression of HNSC by regulating epithelial-mesenchymal transition. In conclusion, LINC00313 is a potential biomarker of HNSC prognosis and a potential target for immunotherapy.

Prediction-oriented prognostic biomarker discovery with survival machine learning methods

Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting. The performances of these feature selection methods when applied to survival models, on the other hand, deserve further investigation. In this paper, we construct and compare a series of prediction-oriented biomarker selection frameworks by leveraging recent machine learning algorithms, including random survival forests, extreme gradient boosting, light gradient boosting and deep learning-based survival models. Additionally, we adapt the recently proposed prediction-oriented marker selection (PROMISE) to a survival model (PROMISE-Cox) as a benchmark approach. Our simulation studies indicate that boosting-based approaches tend to provide superior accuracy with better true positive rate and false positive rate in more complicated scenarios. For demonstration purpose, we applied the proposed biomarker selection strategies to identify prognostic biomarkers in different modalities of head and neck cancer data.

Identifying Oxidized Lipid Metabolism-Related LncRNAs as Prognostic Biomarkers of Head and Neck Squamous Cell Carcinoma

The relationship between oxidized lipid metabolism and the immunological function of cancer is well known. However, the functions and regulatory mechanisms of lncRNAs associated with oxidized lipid metabolism in head and neck squamous cell carcinoma (HNSCC) remain to be fully elucidated. In this study, we established an oxidized lipid metabolism-related lncRNA prognostic signature to assess the prognosis and immune infiltration of HNSCC patients. The HNSCC transcriptome was obtained from The Cancer Genome Atlas. The choice of the target genes with a relevance score greater than 10 was performed via a correlation analysis by GeneCards. Patients were categorized by risk score and generated with multivariate Cox regression, which was then validated and evaluated using the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC). A nomogram was constructed by combining the risk score with the clinical data. We constructed a risk score with 24 oxidized lipid metabolism-related lncRNAs. The areas’ 1-, 2-, and 3-year OS under the ROC curve (AUC) were 0.765, 0.724, and 0.724, respectively. Furthermore, the nomogram clearly distinguished the survival probabilities of patients in high- and low-risk groups, between which substantial variations were revealed by immune infiltration analysis. The results supported the fact that oxidized lipid metabolism-related lncRNAs might predict prognoses and assist with differentiating amid differences in immune infiltration in HNSCC.

The Two Faces of Immune-Related lncRNAs in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a group of cancers originating from the mucosal epithelium in the oral cavity, larynx, oropharynx, nasopharynx, and hypopharynx. Molecular factors can be key in the diagnosis, prognosis, and treatment of HNSCC patients. Long non-coding RNAs (lncRNAs) are molecular regulators composed of 200 to 100,000 nucleotides that act on the modulation of genes that activate signaling pathways associated with oncogenic processes such as proliferation, migration, invasion, and metastasis in tumor cells. However, up until now, few studies have discussed the participation of lncRNAs in modeling the tumor microenvironment (TME) to generate a protumor or antitumor environment. Nevertheless, some immune-related lncRNAs have clinical relevance, since AL139158.2, AL031985.3, AC104794.2, AC099343.3, AL357519.1, SBDSP1, AS1AC108010.1, and TM4SF19-AS1 have been associated with overall survival (OS). MANCR is also related to poor OS and disease-specific survival. MiR31HG, TM4SF19-AS1, and LINC01123 are associated with poor prognosis. Meanwhile, LINC02195 and TRG-AS1 overexpression is associated with favorable prognosis. Moreover, ANRIL lncRNA induces resistance to cisplatin by inhibiting apoptosis. A superior understanding of the molecular mechanisms of lncRNAs that modify the characteristics of TME could contribute to increasing the efficacy of immunotherapy.

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

BACKGROUND

To construct and verify a novel prognostic model for thyroid cancer (THCA) based on N7-methylguanosine modification-related lncRNAs (m7G-lncRNAs) and their association with immune cell infiltration.

METHODS

In this study, we identified m7G-lncRNAs using co-expression analysis and performed differential expression analysis of m7G-lncRNAs between groups. We then constructed a THCA prognostic model, performed survival analysis and risk assessment for the THCA prognostic model, and performed independent prognostic analysis and receiver operating characteristic curve analyses to evaluate and validate the prognostic value of the model. Furthermore, analysis of the regulatory relationship between prognostic differentially expressed m7G-related lncRNAs (PDEm7G-lncRNAs) and mRNAs and correlation analysis of immune cells and risk scores in THCA patients were carried out.

RESULTS

We identified 29 N7-methylguanosine modification-related mRNAs and 116 differentially expressed m7G-related lncRNAs, including 87 downregulated and 29 upregulated lncRNAs. Next, we obtained 8 PDEm7G-lncRNAs. A final optimized model was constructed consisting of 5 PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, TMEM105, SMG7-AS1 and HMGA2-AS1). Six PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, HMGA2-AS1, LINC01976, MID1IP1-AS1, and SMG7-AS1) had positive regulatory relationships with 10 PDEm7G-mRNAs, while 2 PDEm7G-lncRNAs (LINC02026 and TMEM105) had negative regulatory relationships with 2 PDEm7G-mRNAs. Survival curves and risk assessment predicted the prognostic risk in both groups of patients with THCA. Forest maps and receiver operating characteristic curves were used to evaluate and validate the prognostic value of the model. Finally, we demonstrated a correlation between different immune cells and risk scores.

CONCLUSION

Our results will help identify high-risk or low-risk patients with THCA and facilitate early prediction and clinical intervention in patients with high risk and poor prognosis.

Computational identification of immune-related lncRNA signature for predicting the prognosis and immune landscape of human glioblastoma multiforme

Emerging evidence shows immune-related long noncoding RNAs (ir-lncRNAs) perform critical roles in tumor progression and prognosis assessment. However, the identification of ir-lncRNAs and their clinical significance in human glioblastoma multiforme (GBM) remain largely unexplored. Here, a designed computational frame based on immune score was used to identify differentially expressed ir-lncRNAs (DEir-lncRNAs) from The Cancer Genome Atlas (TCGA) GBM program. The immune-related lncRNA signature (IRLncSig) composed of prognosis-related DEir-lncRNAs selected by Cox regression analysis and its clinical predictive values were verified, which was further validated by another dataset from the Gene Expression Omnibus database (GEO). Subsequently, the association between IRLncSig and immune cell infiltration, immune checkpoint inhibitor (ICI) biomarkers, O6-methylguanine-DNA methyltransferase (MGMT) gene expression, and biological function were also analyzed. After calculation, five prognosis-related ir-lncRNAs were included in the establishment of IRLncSig. The risk assessment based on IRLncSig indicated that the high-IRLncSig-score group was significantly associated with poor prognosis (p < 0.001), significant aggregation of macrophages (p < 0.05), higher ICI biomarker expression, and MGMT gene expression (p < 0.05). Signature-related lncRNAs may be involved in immune activities in the tumorigenesis and progression of GBM. In summary, the novel IRLncSig shows a promising clinical value in predicting the prognosis and immune landscape of GBM.

Identification of Gene-Tyrosine Kinase 2 (TYK2) in Head and Neck Squamous Cell Carcinoma Patients-An Integrated Bioinformatics Approach

Background

The human tyrosine kinase 2 (TYK2) has been found to be associated with at least 20 autoimmune diseases; however, its tumor-regulating role in head and neck squamous cell carcinoma (HNSC) has not been researched by using an integrative bioinformatics approach, yet.

Objective

To investigate the regulating mechanisms of the TYK2 gene in HNSC in terms of its expression pattern, prognostic values, involved biological functions, and implication of tumor immunity.

Methods

The TYK2 gene expression pattern and regulatory involvement in HNSC were investigated using publically accessible data from TCGA database. R software tools and public web servers were utilized to conduct statistical analysis on cancer and noncancerous samples.

Results

TYK2 was found to be significantly upregulated in HNSC samples compared with healthy control samples. The expression of TYK2 gene was shown to be associated with the prognosis of HNSC by showing its upregulation represented better survival outcome. The regulating role of TYK2 in HNSC was found mainly in several pathways including DNA replication, base excision repair, apoptosis, p53 signaling pathway, and NF-kappa B signaling pathway. The gene set enrichment analysis (GSEA) results showed that TYK2-significantly correlated genes were mainly enriched in several biological functional terms including cell cycle, DNA replication, PLK1 pathway, ATR pathway, and Rho GTPase pathway. In addition, TYK2 was found to be involved in tumor immunity, showing positive correlation with the majority of tumor infiltrating immune cells, immune checkpoint genes, and significant representative components of tumor microenvironment, according to the ESTIMATE-Stromal-Immune score.

Conclusions

Given the dysregulation, prognostic values, regulating tumor progression-related pathways, and the tumor immune-modulatory role of TYK2 in HNSC, the TYK2 gene should be regarded as a potential therapeutic target in treating head and neck cancer.

The role of long non-coding RNAs in the pathogenesis of head and neck squamous cell carcinoma

Head and neck cancers are a heterogeneous collection of malignancies of the upper aerodigestive tract, salivary glands, and thyroid. However, the molecular mechanisms underlying the carcinogenesis of head and neck squamous cell carcinomas (HNSCCs) remain poorly understood. Over the past decades, overwhelming evidence has demonstrated the regulatory roles of long non-coding RNAs (lncRNAs) in tumorigenesis, including HNSCC. Notably, these lncRNAs have vital roles in gene regulation and affect various aspects of cellular homeostasis, including proliferation, survival, and metastasis. They exert regulating functions by interacting with nucleic acids or proteins and affecting cancer cell signaling. LncRNAs represent a burgeoning field of cancer research, and we are only beginning to understand the importance and complicity of lncRNAs in HNSCC. In this review, we summarize the deregulation and function of lncRNAs in human HNSCC. We also review the working mechanism of lncRNAs in HNSCC pathogenesis and discuss the potential application of lncRNAs as diagnostic/prognostic tools and therapeutic targets in human HNSCC.

A novel immune-related lncRNA pair signature for prognostic prediction and immune response evaluation in gastric cancer: a bioinformatics and biological validation study

Background

Gastric cancer (GC), the most commonly diagnosed cancer worldwide with poor 5-year survival rate in advanced stages. Although immune-related and survival-related biomarkers, which typically comprise aberrantly expressed long non-coding RNAs (lncRNAs) and genes, have been identified, there are no reports of immune-related lncRNA pair (IRLP) signatures for GC.

Methods

In this study, we acquired lncRNA expression profiles from The Cancer Genome Atlas (TCGA) and used the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model (iteration = 1000) to develop a IRLP prognostic signature. The area under curve (AUC) was used to assess the prognosis predictive power. The multivariate Cox regression analysis was performed to identify whether this signature was an independent prognostic factor. The immune cell infiltration analysis was performed between the two risk groups. Last, molecular experiments were performed to explore LINC01082 is involved in the development of GC.

Results

We acquired lncRNA expression profiles and used the LASSO Cox model to develop an 18-IRLP signature with a strong prognostic predictive power. The 5-year AUC values of the training, validation, and overall TCGA datasets were 0.77, 0.86, and 0.80, respectively. The different prognostic outcomes between the high- and low-risk groups were determined using our 18-IRLP signature. Moreover, our 18-IRLP signature was an independent prognostic factor as per the multivariate Cox regression analysis, and showed better prognostic evaluation than the traditional TNM staging system as well as other clinical features. We also found differences in cancer-associated fibroblast and macrophage M2 infiltration and the expression of PD-L1, CTLA4, LAG3, and HLA were also observed between the two risk groups (P < 0.05). Analysis of biological functions revealed that target genes of the lncRNAs in the IRLP signature were enriched in focal adhesion and regulation of actin cytoskeleton. Finally, as one of significant candidates of IRLP signature, overexpression of LINC01082 suppressed the invasion ability of GC cells as well as PD-L1 expression profiles.

Conclusions

Our novel 18-IRLP signature provides new insights regarding immunological biomarkers, imparts a better understanding of the tumor immune microenvironment, and can be used for predicting prognosis and evaluating immune response in GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02493-2.

Identification and in vitro validation of prognostic lncRNA signature in head and neck squamous cell carcinoma

Long non-coding RNAs (lncRNAs) are promising cancer prognostic markers. However, the clinical significance of lncRNA signatures in evaluating overall survival (OS) outcomes of head and neck squamous cell carcinoma (HNSCC) has not been explored. This study aimed to assess the significance of lncRNA in HNSCC and to develop a lncRNA signature related to OS in HNSCC. LncRNA expression matrices were retrieved from the Cancer Genome Atlas (TCGA) data. Least Absolute Shrinkage and Selection of the Operator (LASSO), univariate and multivariate Cox regression were used for establishing a prognostic model. In vitro experiments were carried out to demonstrate the biological role of lncRNA. A prognosis model based on 7 DElncRNAs was finally established.The patients were then divided into high-risk and low-risk groups. Relative to the low-risk group, overall survival times for patients in the high-risk group were significantly low (P=2.466e−07). Risk score remained an independent prognostic factor in univariate (HR=1.329, 95%CI=1.239−1.425, p < 0.001) and multivariate (HR=1.279, 95%CI=1.184−1.382, p < 0.001) Cox regression analyses. The area under the curve (AUC) of the signature was as high as 0.78. Expressions of FOXD2-AS1  in tumor tissues were elevated, and significantly correlated with OS (P=0.008). FOXD2-AS1 silencing then significantly reduced HNSCC cell proliferation, invasion, and migration. In conclusion, a lncRNA signature was established for HNSCC prognostic prediction and FOXD2-AS1 was identified as an HNSCC oncogene.