Potential Biomarkers for Predicting the Overall Survival of Lung squamous cell carcinoma: A analysis of Ferroptosis-Related lncRNAs

Abstract Background In 502 Lung squamous cell carcinoma (LUSC) samples from The Cancer Genome Atlas (TCGA) datasets, the predictive significance of ferroptosis-related long non-coding RNAs (lncRNAs) was investigated. In LUSC, we meant to express how ferroptosis-associated lncRNAs interact with immune cell infiltration. Methods Gene expression enrichment was investigated using gene set enrichment analysis in the Kyoto Encyclopedia of Genes and Genomes. The prognostic model was constructed using Lasso regression. To better understand immune cell infiltration in different risk groups and its relationship to clinical outcome, researchers analyzed by modifications in the tumor microenvironment (TME) and immunological association. The expression of lncRNA was intimately connected to that of ferroptosis, according to co-expression analyses. Ferroptosis-related lncRNAs were shown to be partially overexpressed in high-risk patients in the absence of additional clinical signs, suggesting that they may be incorporated into a prediction model to predict LUSC prognosis. GSEA revealed the immunological and tumor-related pathways in the low-risk group. Results According to TCGA, CCR and inflammation-promoting genes were considered to be significantly different between the low-risk and high-risk groups. The expression of C10orf55, AC016924.1, AL161431.1, LUCAT1, AC104248.1, and MIR3945HG were likewise different in the two risk groups. Conclusion LncRNAs linked to ferroptosis are connected to the occurrence and development of LUSC. With the use of matching prognostic models, the prognosis of LUSC patients can be predicted. In LUSC, ferroptosis-related lncRNAs and immune cell infiltration in the TME might be novel therapeutic targets that should be investigated further.

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Identification of Immune-related lncRNA Panel for Predicting Immune Checkpoint Blockade and Prognosis in Head and Neck Squamous Cell Carcinoma

10.21203/rs.3.rs-858241/v1 ◽

2021 ◽

Author(s):

Chongchang Zhou ◽

Guowen Zhan ◽

Zhisen Shen ◽

Yi Shen ◽

Hongxia Deng ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Head And Neck ◽

Squamous Cell ◽

Immune Cell ◽

Risk Model ◽

Low Risk ◽

Neck Squamous Cell Carcinoma ◽

Cell Infiltration ◽

Immune Cell Infiltration

Abstract Immunotherapy is changing head and neck squamous cell carcinoma (HNSCC) treatment pattern. According to the Chinese Society of Clinical Oncology (CSCO) guidelines, immunotherapy has been deemed as first-line recommendation for recurrent/metastatic HNSCC, marking that advanced HNSCC has officially entered the era of immunotherapy. Long non-coding RNAs impact every step of cancer immunity. Therefore, reliable immune-lncRNA able to accurately predict the immune landscape and survival of HNSCC are crucial to clinical management. In the current study, we downloaded the transcriptomic and clinical data of HNSCC from The Cancer Genome Altas and identified differentially expressed immune-related lncRNAs (DEir-lncRNAs). Further then, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify proper DEir-lncRNAs to construct optimal risk model. Low-risk and high-risk groups were classified based on the optimal cut-off value generated by the areas under curve for receiver operating characteristic curves (AUC), and Kaplan-Meier survival curves were utilized to validate the prediction model. We then evaluated the model based on the clinical factors, immune cell infiltration, chemotherapeutic and immunotherapeutic efficacy between two groups. Our results constructed a risk model consisted of 18 DEir-lncRNA pairs showing significantly association with survival of patients with HNSCC. Besides, HNSCC patients with low risk score significantly enriched of CD8+ T cell, and corelated with high chemosensitivity and immunotherapeutic sensitivity. In summary, our risk model could be served as a promising clinical prediction indicator, effective discoursing of the immune cell infiltration of HNSCC patients, and distinguishing patients who could benefit from chemotherapy and immunotherapy.

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Comprehensive profiling of genomic and transcriptomic differences between risk groups of lung adenocarcinoma and lung squamous cell carcinoma

10.1101/2020.12.31.424952 ◽

2021 ◽

Author(s):

Talip Zengin ◽

Tuğba Önal-Süzek

Keyword(s):

Lung Cancer ◽

Squamous Cell Carcinoma ◽

High Risk ◽

Lung Adenocarcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Cox Model ◽

Lung Squamous Cell Carcinoma ◽

Risk Groups ◽

Low Risk

AbstractLung cancer is the second frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma and lung squamous cell carcinoma are subtypes of non-small cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed univariate cox model and then lasso regularized cox model with leave-one-out cross-validation using TCGA gene expression data in tumor samples. We generated a 35-gene signature and a 33-gene signature for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high-risk and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more down-regulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both upregulated and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. Low-risk groups of both LUAD and LUSC, tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.

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A Four-gene Autophagy-related Prognostic Signature and Its Association With Immune Landscape in Lung Squamous Cell Carcinoma

10.21203/rs.3.rs-125355/v1 ◽

2020 ◽

Author(s):

Lumeng Luo ◽

Minghe Lv ◽

Xuan Li ◽

Tiankui Qiao ◽

Kuaile Zhao ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

High Risk ◽

Cell Carcinoma ◽

Risk Score ◽

Squamous Cell ◽

Immune Suppression ◽

Risk Group ◽

Lung Squamous Cell Carcinoma ◽

High Risk Group ◽

Low Risk

Abstract Background: Recent advances in immune checkpoint inhibitors (ICIs) have dramatically changed the therapeutic strategy against lung squamous cell carcinoma (LUSC). In the era of immunotherapy, effective biomarkers to better predict outcomes and inform treatment decisions for patients diagnosed with LUSC are urgently needed. We hypothesized that immune contexture of LUSC is potentially dictated by tumor intrinsic events, such as autophagy. Thus, we attempted to construct an autophagy-related risk signature and examine its prediction value for immune phenotype in LUSC.Method: The expression profile of LUSC was obtained from the cancer genome atlas (TCGA) database and the profile of autophagy-related genes (ARGs) was extracted. The survival‑related ARGs (sARGs) was screened out through survival analyses. Random forest was performed to select the sARGs and construct a prognostic risk signature based on these sARGs. The signature was further validated by receiver operating characteristic (ROC) analysis and Cox regression. GEO dataset was used as an independent testing dataset. Patients were divided into high-risk and low-risk group based on the risk score. Then, gene set enrichment analysis (GSEA) was conducted between the two groups. The Single-Sample GSEA (ssGSEA) was introduced to quantify the relative infiltration of immune cells. The correlations between risk score and several main immune checkpoints were examined. And the ESTIMATE algorithm was used to calculate the estimate/immune/stromal scores of the LUSC. Results: Four ARGs (CFLAR, RGS19, PINK1 and CTSD) with the most significant prognostic values were enrolled to construct the risk signature. Patients in high-risk group had better prognosis than the low-risk group (P < 0.0001 in TCGA; P < 0.01 in GEO) and considered as an independent prognosis factor. We also found that high-risk group indicated an immune-suppression status and had higher levels of infiltrating regulatory T cells and macrophages, which are correlated with worse outcome. Besides, risk score showed a significantly positive correlation with the expression of PD-1 and CTLA4, as well as estimate score and immune score.Conclusion: This study established a novel autophagy-related four-gene prognostic risk signature, and the autophagy-related scores are associated with immune landscape of LUSC, with higher score indicating a stronger immune-suppression status.

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Characterization of the Immune Cell Infiltration Landscape in Head and Neck Squamous Cell Carcinoma to Aid Immunotherapy

Molecular Therapy — Nucleic Acids ◽

10.1016/j.omtn.2020.08.030 ◽

2020 ◽

Vol 22 ◽

pp. 298-309

Author(s):

Xinhai Zhang ◽

Mengqi Shi ◽

Tielou Chen ◽

Boxin Zhang

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Head And Neck ◽

Squamous Cell ◽

Immune Cell ◽

Neck Squamous Cell Carcinoma ◽

Cell Infiltration ◽

Immune Cell Infiltration

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Characterization of the immune cell infiltration landscape in head and neck squamous cell carcinoma to aid immunotherapy

10.21203/rs.3.rs-23982/v1 ◽

2020 ◽

Author(s):

Xinhai Zhang ◽

Tielou Chen ◽

Boxin Zhang

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Head And Neck ◽

Signaling Pathway ◽

Squamous Cell ◽

Immune Cell ◽

Neck Squamous Cell Carcinoma ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Stromal Component

Abstract Background: The tumor microenvironment chiefly consists of tumor cells, and tumor-infiltrating immune cells admixed with the stromal component. The recent clinical trial has shown that the tumor immune cell infiltration is correlated with the sensitivity to immunotherapy and the prognosis of head and neck squamous cell carcinoma (HNSC). However, to date, the immune infiltrative landscape of HNSC has not yet been elucidated. Methods: We proposed two computational algorithms to unravel the immune infiltration landscape of 1029 HNSC patients. The Boruta algorithm and principal component algorithms (PCA) were employed to quantify three immune cell infiltration gene subtypes categorized as per the immune cell infiltrations pattern. Results: The high ICI score subtype was characterized by a higher tumor mutation burden (TMB) and the immune-activated signaling pathway. However, a low ICI score subtype was categorized as per the activation of immunosuppressive signaling pathways such as TGF-BETA, WNT signaling pathway, and lower TMB. Two immunotherapy cohorts confirmed patients with higher ICI score demonstrated significant therapeutic advantages and clinical benefits.Conclusions: This demonstrated that the ICI score could serve as an effective prognostic biomarker and predictive indicator for immunotherapy. A comprehensive understanding of the HNSC immune landscape might help in tailoring immunotherapeutic strategies for different patients.

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Profiles of immune cell infiltration and their clinical significance in head and neck squamous cell carcinoma

International Immunopharmacology ◽

10.1016/j.intimp.2020.106364 ◽

2020 ◽

Vol 82 ◽

pp. 106364

Author(s):

Yu Jin ◽

Xing Qin

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Head And Neck ◽

Clinical Significance ◽

Squamous Cell ◽

Immune Cell ◽

Neck Squamous Cell Carcinoma ◽

Cell Infiltration ◽

Immune Cell Infiltration

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Integrated Bioinformatics Analysis of Differentially-Expressed Genes and Immune Cell Infiltration Characteristics in Esophageal Squamous Cell Carcinoma

10.21203/rs.3.rs-153887/v1 ◽

2021 ◽

Author(s):

Zitong Feng ◽

Jingge Qu ◽

Xiao Liu ◽

Jinghui Liang ◽

Yongmeng Li ◽

...

Keyword(s):

Gene Expression ◽

Squamous Cell Carcinoma ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Differentially Expressed Genes ◽

Squamous Cell ◽

Bioinformatics Analysis ◽

Immune Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration

Abstract Esophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. Identifying the best-targeted therapy, appropriate biomarkers and individual treatment for patients with ESCC remains a significant challenge. The present study aimed to elucidate key candidate genes and immune cell infiltration characteristics in ESCC by integrated bioinformatics analysis. We downloaded nine gene expression datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between ESCC tissues and normal tissues in each dataset were identified by the “limma” R package, and a total of 152 robust DEGs were identified by robust rank aggregation (RRA) algorithm. Functional enrichment analyses of the robust DEGs showed that these genes were significantly associated with extracellular matrix related process. Immune cell infiltration analysis was also conducted by CIBERSORT algorithm. We found that M0 and M1 macrophages were increased dramatically in ESCC while M2 macrophages decreased. Nine hub genes were picked out from a protein-protein interaction (PPI) network used by the CytoHubba plugin in Cytoscape. According to the receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis, the genes PLAU, SPP1 and VCAN had high diagnostic and prognostic values for ESCC patients. Based on univariate and multivariate regression analyses, seven genes (IL18, PLAU, ANO1, SLCO1B3, CST1, NELL2 and MAGEA11) from the robust DEGs were used to construct a good prognostic model. A nomogram that incorporates seven genes signature was established to develop a quantitative method for ESCC prognosis. Our results might provide aid for exploring potential therapeutic targets and prognosis evaluation in ESCC.

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Prognostic significance of eight immune-related genes on survival in patients with lung squamous cell carcinoma

Biomarkers in Medicine ◽

10.2217/bmm-2020-0483 ◽

2021 ◽

Vol 15 (4) ◽

pp. 295-306

Author(s):

Hansheng Wu ◽

Shujie Huang ◽

Weitao Zhuang ◽

Guibin Qiao

Keyword(s):

Squamous Cell Carcinoma ◽

High Risk ◽

Cell Carcinoma ◽

Differential Expression ◽

Squamous Cell ◽

Lung Squamous Cell Carcinoma ◽

Risk Groups ◽

Related Gene ◽

Immune Related Gene ◽

Immune Related Genes

Aim: To build a valid prognostic model based on immune-related genes for lung squamous cell carcinoma (LUSC). Materials & methods: Differential expression of immune-related genes between LUSC and normal specimens from TCGA dataset and underlying molecular mechanisms were systematically analyzed. Constructing and validating the high-risk and low-risk groups for LUSC survival. Results: The immune-related gene-based prognostic index (IRGPI) could predict the overall survival in patients with different clinicopathological characteristics. Functional enrichment analysis of differential expression of immune-related gene signature indicated distinctive molecular pathways between high-risk and low-risk groups. Conclusion: Analysis of IRGs in LUSC enable us to stratify patients into distinct risk groups, which may help to screen LUSC patients at risk and decision making on follow-up therapeutic intervention.

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Characterization of Molecular Subtypes in Head and Neck Squamous Cell Carcinoma With Distinct Prognosis and Treatment Responsiveness

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.711348 ◽

2021 ◽

Vol 9 ◽

Author(s):

Pei Zhang ◽

Shue Li ◽

Tingting Zhang ◽

Fengzhen Cui ◽

Ji-Hua Shi ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Head And Neck ◽

Squamous Cell ◽

Immune Cell ◽

Neck Squamous Cell Carcinoma ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Oncogenic Signaling ◽

Mtorc1 Signaling

Head and neck squamous cell carcinoma (HNSCC) is one of the most aggressive malignancies with complex phenotypic, etiological, biological, and clinical heterogeneities. Previous studies have proposed different clinically relevant subtypes of HNSCC, but little is known about its corresponding prognosis or suitable treatment strategy. Here, we identified 101 core genes from three prognostic pathways, including mTORC1 signaling, unfold protein response, and UV response UP, in 124 pairs of tumor and matched normal tissues of HNSCC. Moreover, we identified three robust subtypes associated with distinct molecular characteristics and clinical outcomes using consensus clustering based on the gene expression profiles of 944 HNSCC patients from four independent datasets. We then integrated the genomic information of The Cancer Genome Atlas (TCGA) HNSCC cohort to comprehensively evaluate the molecular features of different subtypes and screen for potentially effective therapeutic agents. Cluster 1 had more arrested oncogenic signaling, the highest immune cell infiltration, the highest immunotherapy and chemotherapeutic responsiveness, and the best prognosis. By contrast, Cluster 3 showed more activated oncogenic signaling, the lowest immune cell infiltration, the lowest immunotherapy and chemotherapy responsiveness, and the worst prognosis. Our findings corroborate the molecular diversity of HNSCC tumors and provide a novel classification strategy that may guide for prognosis and treatment allocation.

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