A Novel Immune-Related Gene Signature to Identify the Tumor Microenvironment and Prognose Disease Among Patients With Oral Squamous Cell Carcinoma Patients Using ssGSEA: A Bioinformatics and Biological Validation Study

ANXA5: A Key Regulator of Immune Cell Infiltration in Hepatocellular Carcinoma

BACKGROUND Hepatocellular carcinoma (HCC) poses a significant threat to human life and is the most prevalent form of liver cancer. The intricate interplay between apoptosis, a common form of programmed cell death, and its role in immune regulation stands as a crucial mechanism influencing tumor metastasis. MATERIAL AND METHODS Utilizing HCC samples from the TCGA database and 61 anoikis-related genes (ARGs) sourced from GeneCards, we analyzed the relationship between ARGs and immune cell infiltration in HCC. Subsequently, we identified long non-coding RNAs (lncRNAs) associated with ARGs, using the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a robust prognostic model. The predictive capabilities of the model were then validated through examination in a single-cell dataset. RESULTS Our constructed prognostic model, derived from lncRNAs linked to ARGs, comprised 11 significant lncRNAs: NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18. The risk score assigned to HCC samples demonstrated associations with immune indicators and the infiltration of immune cells. Further, we identified Annexin A5 (ANXA5) as the pivotal gene among ARGs, with it exerting a prominent role in regulating the lncRNA gene signature. Our validation in a single-cell database elucidated the involvement of ANXA5 in immune cell infiltration, specifically in the regulation of mononuclear cells. CONCLUSIONS This study delves into the intricate correlation between ARGs and immune cell infiltration in HCC, culminating in the development of a novel prognostic model reliant on 11 ARGs-associated lncRNAs. Furthermore, our findings highlight ANXA5 as a promising target for immune regulation in HCC, offering new perspectives for immune therapy in the context of HCC.

MicroRNA expression as a prognostic biomarker of tongue squamous cell carcinoma (TSCC): a systematic review and meta-analysis

BACKGROUND

Recent studies have indicated that microRNA (miRNA) expression in tumour tissues has prognostic significance in Tongue squamous cell carcinoma (TSCC) patients. This study explored the possible prognostic value of miRNAs for TSCC based on published research.

METHODS

A comprehensive literature search of multiple databases was conducted according to predefined eligibility criteria. Data were extracted from the included studies by two researchers, and HR results were determined based on Kaplan‒Meier curves according to the Tierney method. The Newcastle‒Ottawa Scale (NOS) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation) pro-GDT were applied to assess the quality of all studies. Publication bias was estimated by funnel plot, Egger's rank correlation test and sensitivity analysis.

RESULTS

Eleven studies (891patients) were included, of which 6 reported up-regulated miRNAs and 7 mentioned down-regulated miRNAs. The pooled hazard ratio (HR) from the prognostic indicator overall survival (OS) was 1.34 (1.25-1.44), p < 0.00001, indicating a significant difference in miRNA expression between TSCC patients with better or worse prognosis.

CONCLUSION

MiRNAs may have high prognostic value and could be used as prognostic biomarkers of TSCC.

Prognostic characteristics of a six-gene signature based on ssGSEA in sarcoma

BACKGROUND

Sarcoma is a rare malignant tumor originating of the interstitial or connective tissue with a poor prognosis. Next-generation sequencing technology offers new opportunities for accurate diagnosis and treatment of sarcomas. There is an urgent need for new gene signature to predict prognosis and evaluate treatment outcomes.

METHODS

We used transcriptome data from the Cancer Genome Atlas (TCGA) database and single sample gene set enrichment analysis (ssGSEA) to explore the cancer hallmarks most associated with prognosis in sarcoma patients. Then, weighted gene coexpression network analysis, univariate COX regression analysis and random forest algorithm were used to construct prognostic gene characteristics. Finally, the prognostic value of gene markers was validated in the TCGA and Integrated Gene Expression (GEO) (GSE17118) datasets, respectively.

RESULTS

MYC targets V1 and V2 are the main cancer hallmarks affecting the overall survival (OS) of sarcoma patients. A six-gene signature including VEGFA, HMGB3, FASN, RCC1, NETO2 and BIRC5 were constructed. Kaplan-Meier analysis suggested that higher risk scores based on the six-gene signature associated with poorer OS (P < 0.001). The receiver Operating characteristic curve showed that the risk score based on the six-gene signature was a good predictor of sarcoma, with an area under the curve (AUC) greater than 0.73. In addition, the prognostic value of the six-gene signature was validated in GSE17118 with an AUC greater than 0.72.

CONCLUSION

This six-gene signature is an independent prognostic factor in patients with sarcoma and is expected to be a potential therapeutic target for sarcoma.

Multi-omics and single-cell sequencing analyses reveal the potential significance of circadian pathways in cancer therapy

BACKGROUND

Circadian rhythm disturbance is an independent risk factor for cancer. However, few studies have been reported on circadian rhythm related genes (CRGs) in cancer, so it is important to further explore the impact of CRGs in pan-cancer.

RESEARCH DESIGN AND METHODS

The Cancer Genome Atlas database was used to collect cancer-related data such as copy number variation, single nucleotide variants, methylation, and survival differences. Immunohistochemistry (IHC) was used to verify the expression of circadian rhythm hub genes. The circadian pathway scores (CRS) were calculated using single-sample gene enrichment analysis. TIMER and GEPIA databases were used for immune-cell integration and assessment. Single-cell sequencing data was used to evaluate the abundance of CRS in tumor microenvironment cells.

RESULTS

In this study, we found that the expression of circadian pathway varies between tumors. CSNK1E was significantly up-regulated in most tumors and CRY2 was significantly down-regulated in most tumors. The protein interaction network suggested CRY2 as the core gene and IHC verified its significant low expression in KIRC. In addition, CRGs were found to be protective factors in most tumors and have the potential to act as specific immune markers in different tumors. CRS was significantly lower in abundance in most tumors. CRS was significantly associated with overall survival in tumor patients and associated with the expression of many immune cells in the tumor immune microenvironment. CRS is significantly associated with tumor mutational burden and microsatellite instability scores in most tumors and may serve as a potential immunotherapeutic marker.

CONCLUSIONS

The circadian rhythm pathway may be a breakthrough point in regulating the tumor microenvironment meanwhile a suitable immunotherapy method in the future.

Comprehensive analysis of necroptotic patterns and associated immune landscapes in individualized treatment of skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) constitutes a malignant cutaneous neoplasm characterized by an exceedingly unfavorable prognosis. Over the past years, necroptosis, a manifestation of inflammatory programmed cell demise, has gained substantial traction in its application. However, a conclusive correlation between the expression of necroptosis-related genes (NRGs) and SKCM patient's prognosis remains elusive. In this endeavor, we have undertaken an integrative analysis of genomic data, aiming to provide an exhaustive evaluation of the intricate interplay between melanoma necroptosis and immune-infiltration nuances within the tumor microenvironment. Through meticulous scrutiny, we have endeavored to discern the prognostic potency harbored by individual necroptosis-associated genes. Our efforts culminated in the establishment of a risk stratification framework, allowing for the appraisal of necroptosis irregularities within each afflicted cutaneous melanoma patient. Notably, those SKCM patients classified within the low-risk cohort exhibited a markedly elevated survival quotient, in stark contrast to their high-risk counterparts (p < 0.001). Remarkably, the low-risk cohort not only displayed a more favorable survival rate but also exhibited an enhanced responsiveness to immunotherapeutic interventions, relative to their high-risk counterparts. The outcomes of this investigation proffer insights into a conceivable mechanistic underpinning linking necroptosis-related attributes to the intricacies of the tumor microenvironment. This prompts a conjecture regarding the plausible association between necroptosis characteristics and the broader tumor microenvironmental milieu. However, it is imperative to emphasize that the pursuit of discerning whether the expression profiles of NRG genes can indeed be regarded as viable therapeutic targets necessitates further comprehensive exploration and scrutiny. In conclusion, our study sheds light on the intricate interrelationship between necroptosis-related factors and the tumor microenvironment, potentially opening avenues for therapeutic interventions. However, the prospect of translating these findings into clinical applications mandates rigorous investigation.

Identification and Preliminary Clinical Validation of Key Extracellular Proteins as the Potential Biomarkers in Hashimoto's Thyroiditis by Comprehensive Analysis

Hashimoto's thyroiditis (HT) is an autoimmune disruption manifested by immune cell infiltration in thyroid tissue and the production of antibodies against thyroid-specific antigens, such as the thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TGAb). TPOAb and TGAb are commonly used in clinical tests; however, handy indicators of the diagnosis and progression of HT are still scarce. Extracellular proteins are glycosylated and are likely to enter body fluids and become readily available and detectable biomarkers. Our research aimed to discover extracellular biomarkers and potential treatment targets associated with HT through integrated bioinformatics analysis and clinical sample validations. A total of 19 extracellular protein-differentially expressed genes (EP-DEGs) were screened by the GSE138198 dataset from the Gene Expression Omnibus (GEO) database and protein annotation databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the function and pathway of EP-DEGs. STRING, Cytoscape, MCODE, and Cytohubba were used to construct a protein-protein interaction (PPI) network and screen key EP-DEGs. Six key EP-DEGs (CCL5, GZMK, CXCL9, CXCL10, CXCL11, and CXCL13) were further validated in the GSE29315 dataset and the diagnostic curves were evaluated, which all showed high diagnostic accuracy (AUC > 0.95) for HT. Immune profiling revealed the correlation of the six key EP-DEGs and the pivotal immune cells in HT, such as CD8+ T cells, dendritic cells, and Th2 cells. Further, we also confirmed the key EP-DEGs in clinical thyroid samples. Our study may provide bioinformatics and clinical evidence for revealing the pathogenesis of HT and improving the potential diagnosis biomarkers and therapeutic strategies for HT.

NUP62CL as an Immunological and Prognostic Biomarker of Oral Squamous Cell Carcinoma

Background

Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy with a high mortality rate and poor prognosis. The exploration and understanding of biomarkers will help to further improve the diagnosis and treatment of OSCC.

Methods

Tumor tissue samples from 319 OSCC patients were retrospectively collected, along with their clinical information. In combination with bioinformatics tools and multiplex immunohistochemistry (mIHC) analyses, we evaluated NUP62CL protein expression and its relationship to tumor-infiltrating immune cells (TIICs) and immune checkpoints in the tumor microenvironment (TME), as well as its association with clinical features and prognosis of OSCC.

Results

We identified high-NUP62CL expression in OSCC tissues, and high-NUP62CL protein expression was associated with large tumor size, advanced clinical stage and poor prognosis. In addition, NUP62CL protein expression was positively associated with the abundance of CD3+CD4+ T cells (P<0.01), CD3+CD8+ T cells (P<0.01), CD56+ NK cells (P<0.05), CD68+CD86+ macrophages (P<0.01) and CD68+CD163+ macrophages (P<0.01), as well as the immune checkpoints, including PD-1 (P<0.001), PD-L1 (P<0.001), and CTLA-4 (P<0.001) protein expression.

Conclusion

In conclusion, NUP62CL could be an effective prognostic and immunological biomarker for OSCC patients.

A novel investigation into an E2F transcription factor-related prognostic model with seven signatures for colon cancer patients

The pathogenesis of colon cancer, a common gastrointestinal tumour, involves complicated factors, especially a series of cell cycle-related genes. E2F transcription factors during the cell cycle play an essential role in the occurrence of colon cancer. It is meaningful to establish an efficient prognostic model of colon cancer targeting cellular E2F-associated genes. This has not been reported previously. The authors first aimed to explore the links of E2F genes with the clinical outcomes of colon cancer patients by integrating data from the TCGA-COAD (n = 521), GSE17536 (n = 177) and GSE39582 (n = 585) cohorts. The Cox regression and Lasso modelling approach to identify a novel colon cancer prognostic model involving several hub genes (CDKN2A, GSPT1, PNN, POLD3, PPP1R8, PTTG1 and RFC1) were utilised. Moreover, an E2F-related nomogram that efficiently predicted the survival rates of colon cancer patients was created. Additionally, the authors first identified two E2F tumour clusters, which showed distinct prognostic features. Interestingly, the potential links of E2F-based classification and 'protein secretion' issues of multiorgans and tumour infiltration of 'T-cell regulatory (Tregs)' and 'CD56dim natural killer cell' were detected. The authors' findings are of potential clinical significance for the prognosis assessment and mechanistic exploration of colon cancer.

MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures

BACKGROUND

. At present, the prognostic prediction in advanced oral cavity squamous cell carcinoma (OCSCC) is based on the tumor-node-metastasis (TNM) staging system, and the most used imaging modality in these patients is magnetic resonance image (MRI). With the aim to improve the prediction, we developed an MRI-based radiomic signature as a prognostic marker for overall survival (OS) in OCSCC patients and compared it with published gene expression signatures for prognosis of OS in head and neck cancer patients, replicated herein on our OCSCC dataset.

METHODS

For each patient, 1072 radiomic features were extracted from T1 and T2-weighted MRI (T1w and T2w). Features selection was performed, and an optimal set of five of them was used to fit a Cox proportional hazard regression model for OS. The radiomic signature was developed on a multi-centric locally advanced OCSCC retrospective dataset (n = 123) and validated on a prospective cohort (n = 108).

RESULTS

The performance of the signature was evaluated in terms of C-index (0.68 (IQR 0.66-0.70)), hazard ratio (HR 2.64 (95% CI 1.62-4.31)), and high/low risk group stratification (log-rank p < 0.001, Kaplan-Meier curves). When tested on a multi-centric prospective cohort (n = 108), the signature had a C-index of 0.62 (IQR 0.58-0.64) and outperformed the clinical and pathologic TNM stage and six out of seven gene expression prognostic signatures. In addition, the significant difference of the radiomic signature between stages III and IVa/b in patients receiving surgery suggests a potential association of MRI features with the pathologic stage.

CONCLUSIONS

Overall, the present study suggests that MRI signatures, containing non-invasive and cost-effective remarkable information, could be exploited as prognostic tools.

Construction of a prognostic model for triple-negative breast cancer based on immune-related genes, and associations between the tumor immune microenvironment and immunological therapy

BACKGROUND

Triple-negative breast cancer (TNBC) is the subtype of breast cancer with the worst prognosis, and it is highly heterogeneous. There is growing evidence that the tumor immune microenvironment (TIME) plays a crucial role in tumor development, maintenance, and treatment responses. Notably however, the full effects of the TIME on prognosis, TIME characteristics, and immunotherapy responses in TNBC patients have not been fully elucidated.

METHODS

Gene Expression Omnibus and The Cancer Genome Atlas data were used to data analysis. Single-cell sequencing and tissue microarray analysis were used to investigate gene expression. The concentrations and distributions of immune cell types were determined and analyzed using the CIBERSORT strategy. Tumor immune dysfunction and exclusion score and the IMvigor210 cohort were used to estimate the sensitivity of TNBC patients with different prognostic statuses to immune checkpoint treatment.

RESULTS

Five immune-related genes associated with TNBC prognosis (IL6ST, NR2F1, CKLF, TCF7L2, and HSPA2) was identified and a prognostic evaluation model was constructed based on those genes. The respective areas under the curve of the prognostic nomogram model at 3 and 5 years were 0.791 and 0.859. The group with a lower nomogram score, with a better prognosis survival status and clinical treatment benefit rate.

CONCLUSION

A prognostic model for TNBC that was closely related to the immune landscape and therapeutic responses was constructed. This model may help clinicians to make more precise and personalized treatment decisions pertaining to TNBC patients.

Immune infiltration associated C1q acts as a novel prognostic biomarker of cutaneous melanoma

C1q (complement C1q A chain, complement C1q B chain, and complement C1q C chain) is a recognized component of the classical complement pathway that influences the prognosis of various cancers. However, the effects of C1q on cutaneous melanoma (SKCM) outcomes and immune infiltration remain unknown. Gene expression profiling interactive analysis 2 and the human protein atlas were used to evaluate differential expression of C1q mRNA and protein. The relationship between C1q expression and clinicopathological features was also examined. The genetic alterations of C1q and their impact on survival were analyzed using the cbioportal database. The Kaplan-Meier approach was used to assess the significance of C1q in individuals with SKCM. The cluster profiler R package and the cancer single-cell state atlas database were used to investigate the function and mechanism of C1q in SKCM. The relationship between C1q and immune cell infiltration was estimated using single-sample gene set enrichment analysis. C1q expression was increased, and predicted a favorable prognosis. High C1q expression correlated with clinicopathological T stage, pathological stage, overall survival, and disease specific survival events. Moreover, C1q genetic alterations range from 2.7% to 4%, with no impact on prognosis. According to the enrichment analysis, C1q and immune-related pathways were closely connected. The link between complement C1q B chain and the functional state of inflammation was determined using the cancer single-cell state atlas database. In particular, C1q expression was significantly associated with infiltration of most immune cells and checkpoints PDCD1, CD274, and HAVCR2. The results of this study suggest that C1q is associated with prognosis and immune cell infiltration, supporting its value as a diagnostic and prognostic biomarker.

Immunosuppression by Inflammation-Stimulated Amplification of Myeloid-Derived Suppressor Cells and Changes in Expression of Immune Checkpoint HHLA2 in Chronic Obstructive Pulmonary Disease

Background

The interaction between immune checkpoint and myeloid-derived suppressor cells (MDSCs) play a significant role in inflammatory diseases. But their correlation with chronic obstructive pulmonary disease (COPD) remains unclear.

Methods

The differentially expressed immune checkpoints and immunocytes in the airway tissues of COPD patients were identified by bioinformatics analysis, followed by correlation analysis and identification of immune-related differential genes for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. The results of bioinformatics analysis were verified by ELISA and Real-Time PCR and transcriptome sequencing of the peripheral blood of both COPD patients and healthy subjects.

Results

The results of the bioinformatics analysis showed that the level of MDSCs in airway tissue and peripheral blood of COPD patients was higher than that of healthy controls. The expression of CSF1 in airway tissue and peripheral blood of COPD patients increased, and CYBB was increased in airway tissue and decreased in peripheral blood of COPD patients. The expression of HHLA2 in the airway tissue decreased in COPD patients, and showed a negative correlation with MDSCs, with a correlation coefficient of -0.37. The peripheral blood flow cytometry results indicated that MDSCs and Treg cells of COPD patients were higher than those in the healthy control group. The results of peripheral blood ELISA and RT-PCR showed that the HHLA2 and CSF1 levels in COPD patients were higher than those in the healthy control group.

Conclusion

In COPD, the bone marrow is stimulated to produce MDSCs, and a large number of MDSCs migrate to airway tissue through peripheral blood and cooperate with HHLA2 to exert an immunosuppressive effect. Whether MDSCs play an immunosuppressive effect during migration needs to be further confirmed.

Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignancy with a poor prognosis. This study aimed to distinguish patients with HCC having distinct tumour immune microenvironment (TIME) features and construct an immune-related gene signature (IRGs) to assess prognosis and provide a basis for personalised therapies. Methods: Transcriptomic data of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We assessed the immune cell infiltration in each HCC specimen using single sample gene set enrichment analysis (ssGSEA) and classified all patients with HCC into high- and low-immune clusters using a hierarchical clustering algorithm. The ESTIMATE and CIBERSORT computational methods were employed to verify the stability and effectiveness of the immune clusters. Subsequently, the differentially expressed genes (DEGs) of the high- and low-immune clusters and the immune-related genes intersected to obtain the immune-related DEGs. The least absolute shrinkage and selection operator (LASSO) was then employed to screen the optimal genes for the construction of a prognostic predictive signature and to divide patients into high- and low-risk subgroups. The predictive efficacy of the IRGs was further confirmed using Kaplan-Meier survival curves, univariate and multifactorial Cox regression and time-dependent ROC curves in the TCGA and GSE14520 validation cohorts. Furthermore, we developed a nomogram to predict the prognosis. Tumour mutation burden (TMB) was also analysed in the risk groups. Additionally, gene ontology and gene set variation analysis were used for biological function and pathway exploration. Lastly, drug sensitivity analyses were employed to investigate prospective therapeutics in the two risk populations. Results: Immune cluster analysis based on ssGSEA could well distinguish the TIME characteristics of patients with HCC. The stromal score, immune score and ESTIMATE score were all lower in the low-immune cluster. Meanwhile, most of the immune checkpoint-related genes and HLA family genes were overexpressed in the high-immune cluster, suggesting that this cluster could be a beneficial population for immune checkpoint inhibitors therapy. There were 1,617 DEGs between the two immune clusters, of which 414 genes intersected with immune-associated genes. Furthermore, Cox regression analysis revealed 49 DEGs that were associated with survival. Then, 19 DEGs were screened using the LASSO algorithm for IRGs construction and patients were classified into high- and low-risk groups. Both the constructed signature and nomogram had good prognostic predictive efficacy. The signature-based risk score was an independent prognostic predictor in both the TCGA and GSE14520 cohorts. Additionally, there was no significant difference in TMB between the two risk populations. Lastly, the half-maximal inhibitory concentrations of certain chemotherapeutic and targeted therapeutic agents differed between the two risk groups. Conclusion: Our study provides a personalized tool for predicting the prognosis and TIME landscape of HCC and a basis for developing personalised treatment regimens.