Exploration of a Robust and Prognostic Immune Related Gene Signature for Cervical Squamous Cell Carcinoma

Upregulation of HMOX1 associated with M2 macrophage infiltration and ferroptosis in proliferative diabetic retinopathy

The roles of immune cell infiltration and ferroptosis in the progression of proliferative diabetic retinopathy (PDR) remain unclear. To identify upregulated molecules associated with immune infiltration and ferroptosis in PDR, GSE60436 and GSE102485 datasets were downloaded from the Gene Expression Omnibus (GEO). Genes associated with immune cell infiltration were examined through Weighted Gene Co-expression Network Analysis (WGCNA) and CIBERSORT algorithm. Common differentially expressed genes (DEGs) were intersected with ferroptosis-associated and immune cell infiltration-related genes. Localization of cellular expression was confirmed by single-cell analysis of GSE165784 dataset. Findings were validated by qRT-PCR, ELISA, Western blotting, and immunofluorescence staining. As a result, the infiltration of M2 macrophages was significantly elevated in fibrovascular membrane samples from PDR patients than the retinas of control subjects. Analysis of DEGs, M2 macrophage-related genes and ferroptosis-related genes identified three hub intersecting genes, TP53, HMOX1 and PPARA. qRT-PCR showed that HMOX1 was significantly higher in the oxygen-induced retinopathy (OIR) mouse model retinas than in controls. Single-cell analysis confirmed that HMOX1 was located in M2 macrophages. ELISA and western blotting revealed elevated levels of HMOX1 in the vitreous humor of PDR patients and OIR retinas, and immunofluorescence staining showed that HMOX1 co-localized with M2 macrophages in the retinas of OIR mice. This study offers novel insights into the mechanisms associated with immune cell infiltration and ferroptosis in PDR. HMOX1 expression correlated with M2 macrophage infiltration and ferroptosis, which may play a crucial role in PDR pathogenesis.

Effect of GLIS1 on the Lymph Node Metastasis of Cervical Squamous Carcinoma Based on Transcriptome Analysis

Cervical cancer remains the leading cause of cancer-related mortality among female reproductive malignancies, and lymph node metastasis (LNM) represents the major reason for its poor prognosis. In this study, we aimed to identify transcriptome differences in patients with cervical squamous cell carcinoma (CSCC) who developed LNM or not and to outline the function of GLIS1 in determining metastatic fate in CSCC. In The Cancer Genome Atlas-endocervical adenocarcinoma project, patients with LNM had shorter overall survival than those without. Transcriptome data from CSCC patients with and without LNM were analyzed to screen for differentially expressed genes (DEGs). DEGs were enriched in metastasis-related pathways, such as extracellular matrix organization, cell-cell adhesion, and regulation of tissue remodeling. GLIS1 was overexpressed in tumor tissues of patients with LNM. COMP and ITGA11 were screened as downstream targets of GLIS1. GLIS1 promoted their transcription by binding to the promoter regions of COMP and ITGA11. GLIS1 enhanced the migration, invasion, and epithelial-mesenchymal transition in CSCC cells, while the knockdown of COMP or ITGA11 reversed the promotion of GLIS1 on CSCC cell malignant phenotype. Together, our results demonstrate that GLIS1 might be related to the LNM of CSCC patients via COMP and ITGA11.

Development and validation of a prognostic prediction model for cervical cancer patients treated with radical radiotherapy: a study based on TCGA database

Background

Radiotherapy or concurrent chemoradiotherapy is the standard treatment for patients with locally advanced or inoperable cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). However, treatment failure for CESC patients treated with radical radiotherapy still occurs due to local recurrence and distant metastasis. The previous prediction models were focused on all CESC patients, neglecting the prognostic differences under different treatment modalities. Therefore, there is a pressing demand to explore novel biomarkers for the prognosis and sensitivity of radiotherapy in CESC patients treated with radical radiotherapy. As a single biomarker has limited effect in stratifying these patients, our objective was to identify radioresponse-related mRNAs to ameliorate forecast of the prognosis for CESC patients treated with radical radiotherapy.

Methods

Sample data on CESC patients treated with radical radiotherapy were obtained from The Cancer Genome Atlas (TCGA) database. We randomly separated these patients into a training and test cohorts using a 1:1 ratio. Differential expression analysis was carried out to identify radioresponse-related mRNA sets that were significantly dysregulated between complete response (CR) and radiographic progressive disease (RPD) groups, and univariate Cox regression analyses, least absolute shrinkage and selection operator (LASSO) method and multivariate Cox regression were performed to identify the radioresponse-related signature in the training cohort. we adopted survival analysis to measure the predictive value of the radioresponse-related signature both in the test and entire cohorts. Moreover, we developed a novel nomogram to predict the overall survival (OS) of CESC patients treated with radical radiotherapy. In addition, immune infiltration analysis and Gene Set Enrichment Analysis (GSEA) were conducted to preliminarily explore possible mechanisms.

Results

This study included a total of 92 CESC patients subjected to radical radiotherapy. We developed and verified a risk score model based on radioresponse-related mRNA. The radioresponse-related mRNA signature and International Federation of Gynecology and Obstetrics (FIGO) stage were served as independent prognostic factors for CESC patients treated with radical radiotherapy. Moreover, a nomogram integrating radioresponse-related mRNA signature with FIGO stage was established to perform better for predicting 1-, 3-, and 5-year survival rates. Mechanically, the low-risk group under the risk score of this model had a better survival status, and the distribution of CD4 T cells was potentially involved in the regulation of radiotherapy response in CESC, leading to a better survival outcome in the low-risk group.

Conclusions

This study presents a new radioresponse-related mRNA signature that shows promising clinical efficacy in predicting the prognosis of CESC patients treated with radical radiotherapy.

A Novel Eight-Gene Signature for Lipid Metabolism Predicts the Progression of Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

Recently, studies on the mechanisms underlying lipid metabolic reprogramming in cancer have increased. However, its significance in cervical cancer remains unclear. In the present study, a prognostic signature was constructed for patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) using the expression profiles of lipid metabolism-related genes (LMRGs). Furthermore, using various bioinformatics methods, a prognostic gene signature was developed for progression-free survival (PFS). This signature was externally validated using a cervical cancer dataset (GSE44001). The characteristics of the molecular subgroups of LMRGs were analyzed, and target LMRGs were identified via differential gene analysis of the expression profiles and weighted gene correlation network analysis. Thereafter, the identified target genes were used to develop the prognostic gene signature using univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. The performance of the LMRG signature was evaluated using Kaplan-Meier curves, time-dependent receiver operating characteristic curves, decision curve analysis, mutation landscapes, gene set enrichment analysis, and immune score calculation. As a result, a novel eight-LMRG signature comprising ALDH3B2, CERS3, FA2H, GLTP, NR1H3, PLIN3, SLC44A3, and SQLE was constructed. Using this gene signature, patients with CESC and significantly distinguished PFS were divided. This eight-LMRG signature exhibited independent prognostic potential and superior predictive performance compared with a previously developed 12-gene signature. Our findings suggest that our novel eight-LMRG signature contributes to the implementation of precision medicine strategies for managing patients with cervical cancer by facilitating CESC prognosis.

Bioinformatics analysis of immune characteristics in tumors with alternative carcinogenesis pathways induced by human papillomaviruses

BACKGROUND

Human papillomaviruses (HPVs) induce a subset of head and neck squamous cell carcinomas (HNSCC) and anogenital cancers, particularly cervical cancer (CC). The major viral proteins that contribute to tumorigenesis are the E6 and E7 oncoproteins, whose expression is usually enhanced after the integration of viral DNA into the host genome. Recently, an alternative tumorigenesis pathway has been suggested in approximately half of HNSCC and CC cases associated with HPV infection. This pathway is characterized by extrachromosomal HPV persistence and increased expression of the viral E2, E4, and E5 genes. The E6, E7, E5, and E2 proteins have been shown to modify the expression of numerous cellular immune-related genes. The antitumor immune response is a critical factor in the prognosis of HPV-driven cancers, and its characterization may contribute to the prediction and personalization of the increasingly used cancer immunotherapy.

METHODS

We analyzed the immune characteristics of HPV-dependent tumors and their association with carcinogenesis types. Transcriptomic HNSCC and CC datasets from The Cancer Genome Atlas were used for this analysis.

RESULTS

Clustering with immune-related genes resulted in two clusters of HPV16-positive squamous cell carcinomas in both tumor types: cluster 1 had higher activation of immune responses, including stimulation of the antigen processing and presentation pathway, which was associated with higher immune cell infiltration and better overall survival, and cluster 2 was characterized by keratinization. In CC, the distribution of tumor samples into clusters 1 and 2 did not depend on the level of E2/E5 expression, but in HNSCC, most E2/E5-high tumors were localized in cluster 1 and E2/E5-low tumors in cluster 2. Further analysis did not reveal any association between the E2/E5 levels and the expression of immune-related genes.

CONCLUSIONS

Our results suggest that while the detection of immune responses associated with preserved expression of genes encoding components of antigen processing and presentation machinery in HPV-driven tumors may be markers of better prognosis and an important factor in therapy selection, the type of carcinogenesis does not seem to play a decisive role in the induction of antitumor immunity.

Identification of key genes and their correlation with immune infiltration in osteoarthritis using integrative bioinformatics approaches and machine-learning strategies

Osteoarthritis (OA) is a common degenerative joint disease and is closely associated with chronic, low-grade inflammation. Regulating ferroptosis by targeting ferroptosis-related genes may be a fast and effective way to delay the degeneration of OA. However, the molecular mechanisms and gene targets related to ferroptosis in OA are still unclear. Data of OA samples from 3 gene expression omnibus (GEO) datasets were combined to identify differentially expressed genes (DEGs). Ferroptosis-related genes (FRGs) retrieved by the Ferroptosis database were intersected with DEGs, and the intersected hub genes were used for functional enrichment analysis. The feature genes were obtained from the least absolute shrinkage and selection operator (LASSO) algorithm, support vector machine recursive feature elimination (SVM-RFE) algorithm, and random forest (RF) algorithm. Single sample gene set enrichment analysis (ssGSEA) was used to compare immune infiltration between OA patients and normal controls, and the correlation between feature genes and immune cells was analyzed. The expression levels of feature genes were confirmed by RT-PCR. In addition, to explore the applicability of these genes, we extended the bioinformatics analysis of these feature genes to cancer. Finally, 4 feature genes, GABARAPL1, TNFAIP3, ARNTL, and JUN, were confirmed in OA. Theirs expression level were validated by RT-PCR. ROC curves of the 4 genes exhibit excellent diagnostic efficiency for OA, suggesting that the 4 genes were associated with the pathogenesis of OA. Another GEO dataset validated this result. Further analysis revealed that the 4 feature genes were all closely related to the immune infiltration cells in OA. Additionally, results of prognosis analysis indicated that JUN might be a promising therapeutic target for cancer. GABARAPL1, TNFAIP3, ARNTL, and JUN may be predicted biomarkers for OA. The feature genes and association between feature genes and immune infiltration may provide potential biomarkers for OA prediction along with the better assessment of the disease.

Tertiary lymphoid structure patterns predicted anti-PD1 therapeutic responses in gastric cancer

OBJECTIVE

Recent studies have highlighted the distinct value of tertiary lymphoid structure (TLS) for immunotherapeutic response prediction. However, it remains unclear whether TLS could play such roles in gastric cancer (GC).

METHODS

In this study, tumor tissue slices from 292 GC patients from Zhongshan Hospital were firstly reviewed to explore the correlation between TLS and clinical characteristics. Subsequently, we curated 38 reported genes that may function as triggers of TLS and performed consensus molecular subtyping in public RNA-seq datasets to determine TLS patterns in GC. Based on the differentially expressed genes acquired from two TLS patterns, we quantified TLS-related genes on the principal component analysis (PCA) algorithm to develop TLS score. A Zhongshan immunotherapy cohort including 13 patients who received programmed cell death 1 (PD1) blockade therapy was established to conduct RNA sequencing analysis and multiplex immunohistochemistry (mIHC) tests using formalin-fixed and paraffin-embedded (FFPE) tissues. The corresponding TLS score and immune cell counts were further compared based on therapeutic response variations.

RESULTS

Mature TLS was revealed as an independent prognostic factor in 292 GC patients. Patients with higher TLS score was characterized by prolonged survival time and superior response to immunotherapy. TLS score was correlated with immunotherapy-related characters, such as microsatellite instability (MSI) and tumor mutation burden (TMB). In addition, RNA-seq data analysis in the Zhongshan immunotherapy cohort indicated that a higher TLS score was correlated with a superior response to PD1 blockade therapy. mIHC tests also revealed that PD1+CD8+ T cell counts were significantly increased in the high-TLS score group.

CONCLUSIONS

This study highlighted that TLS was significantly associated with immune landscape diversity and complexity. Quantitatively evaluating TLS patterns of individual tumor will strengthen our understanding of TME characteristics and promote more effective immunotherapy strategies.

Construction of a novel signature and prediction of the immune landscape in gastric cancer based on necroptosis-related genes

Necroptosis, a type of programmed cell death, has become a potential therapeutic target for solid tumors. Nevertheless, the potential roles of necroptosis-related genes (NRGs) in gastric cancer (GC) remain unknown. The objective of the present study was to create a necroptosis-related prognostic signature that can provide more accurate assessment of prognosis in GC. Using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data, we identified differentially expressed NRGs. Univariate analysis and Lasso regression were performed to determine the prognostic signature. Risk scores were calculated and all GC patients were divided into high- and low-risk score group according to the median risk score value. The robustness of this signature was externally validated with data from GSE84437 cohort (n = 431). Survival analysis revealed high-risk score patients had a worse prognosis. Results evidenced that the signature was an independent prognosis factor for survival. Single-sample sequence set enrichment analysis (ssGSEA) exhibited different enrichment of immune cells and immune-related pathways in the two risk groups. Furthermore, a predictive nomogram was generated and showed excellent predictive performance based on discrimination and calibration. In addition, the risk score positively correlated with tumor mutational burden and was associated with sensitivity to multiple anti-cancer drugs. Overall, our work demonstrates a close relationship between necroptosis and the prognosis of GC. The signature we constructed with potential clinical application value, can be used for prognosis prediction and being a potential therapeutic responses indicator in GC patients.

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

Human leukocyte antigen G (HLA-G) is a potential checkpoint molecule that plays a key role in cervical carcinogenesis. The purpose of this study was to construct and validate a prognostic risk model to predict the overall survival (OS) of cervical cancer patients, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven differentially expressed genes (DEGs) were obtained from two cervical carcinoma cell lines, namely, SiHa and HeLa, with stable overexpression of HLA-G by RNA sequencing (RNA-seq). The biological functions of these HLA-G-driven DEGs were analysed by GO enrichment and KEGG pathway using the “clusterProfiler” package. The protein-protein interactions (PPIs) were assessed using the STRING database. The prognostic relevance of each DEG was evaluated by univariate Cox regression using the TCGA-CESC dataset. After the TCGA-CESC cohort was randomly divided into training set and testing set, and a prognostic risk model was constructed by LASSO and stepwise multivariate Cox regression analysis in training set and validated in testing set or in different types of cervical cancer set. The predictive ability of the prognostic risk model or nomogram was evaluated by a series of bioinformatics methods. A total of 1108 candidate HLA-G-driven DEGs, including 391 upregulated and 717 downregulated genes, were obtained and were enriched mostly in the ErbB pathway, steroid biosynthesis, and MAPK pathway. Then, an HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. Multivariate Cox regression analysis showed that this signature is an independent risk factor for the overall survival of CESC patients. Kaplan-Meier survival analysis showed that the 5-year overall survival rate is 23.0% and 84.6% for the high-risk and low-risk patients, respectively (P<0.001). The receiver operating characteristic (ROC) curve of this prognostic model with an area under the curve (AUC) was 0.896 for 5 years, which was better than that of other clinical traits. This prognostic risk model was also successfully validated in different subtypes of cervical cancer, including the keratinizing squamous cell carcinoma, non-keratinizing squamous cell carcinoma, squamous cell neoplasms, non-squamous cell neoplasms set. Single-sample gene set enrichment (ssGSEA) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis confirmed that this signature influence tumour microenvironment and immune checkpoint blockade. A nomogram that integrated risk score, age, clinical stage, histological grade, and pathological type was then built to predict the overall survival of CESC patients and evaluated by calibration curves, AUC, concordance index (C-index) and decision curve analysis (DCA). To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.

CD3D Is an Independent Prognostic Factor and Correlates With Immune Infiltration in Gastric Cancer

The protein encoded by CD3D is part of the T-cell receptor/CD3 complex (TCR/CD3 complex) and is involved in T-cell development and signal transduction. Previous studies have shown that CD3D is associated with prognosis and treatment response in breast, colorectal, and liver cancer. However, the expression and clinical significance of CD3D in gastric cancer are not clear. In this study, we collected 488 gastric cancer tissues and 430 paired adjacent tissues to perform tissue microarrays (TMAs). Then, immunohistochemical staining of CD3D, CD3, CD4, CD8 and PD-L1 was conducted to investigate the expression of CD3D in gastric cancer and the correlation between the expression of CD3D and tumor infiltrating lymphocytes (TILs) and PD-L1. The results showed that CD3D was highly expressed in gastric cancer tissues compared with paracancerous tissues (P<0.000). Univariate and multivariate analyses showed that CD3D was an independent good prognostic factor for gastric cancer (P=0.004, HR=0.677, 95%CI: 0.510-0.898 for univariate analyses; P=0.046, HR=0.687, 95%CI: 0.474-0.994 for multivariate analyses). In addition, CD3D was negatively correlated with the tumor location, Borrmann type and distant metastasis (P=0.012 for tumor location; P=0.007 for Borrmann type; P=0.027 for distant metastasis). In addition, the expression of CD3D was highly positively correlated with the expression of CD3, CD4, CD8, and PD-L1, and the combination of CD3D with CD3, CD4, CD8 and PD-L1 predicted the best prognosis (P=0.043). In summary, CD3D may play an important regulatory role in the tumor immune microenvironment of gastric cancer and may serve as a potential indicator of prognosis and immunotherapy response.

Transcriptomic Correlates of Immunologic Activation in Head and Neck and Cervical Cancer

Targeting the immune system has emerged as an effective therapeutic strategy for the treatment of various tumor types, including Head and Neck Squamous Cell Carcinoma (HNSCC) and Non-small-Cell Lung Cancer (NSCLC), and checkpoint inhibitors have shown to improve patient survival in these tumor types. Unfortunately, not all cancers respond to these agents, making it necessary to identify responsive tumors. Several biomarkers of response have been described and clinically tested. As of yet what seems to be clear is that a pre-activation state of the immune system is necessary for these agents to be efficient. In this study, using established transcriptomic signatures, we identified a group of gene combination associated with favorable outcome in HNSCC linked to a higher presence of immune effector cells. CD2, CD3D, CD3E, and CXCR6 combined gene expression is associated with improved outcome of HNSCC patients and an increase of infiltrating immune effector cells. This new signature also identifies a subset of cervical squamous cell carcinoma (CSCC) patients with favorable prognosis, who show an increased presence of immune effector cells in the tumor, which outcome shows similarities with the HP-positive HNSCC cohort of patients. In addition, CD2, CD3D, CD3E, and CXCR6 signature is able to predict the best favorable prognosis in terms of overall survival of CSSC patients. Of note, these findings were not reproduced in other squamous cell carcinomas like esophageal SCC or lung SCC. Prospective confirmatory studies should be employed to validate these findings.

Characterization of Exosome-Related Gene Risk Model to Evaluate the Tumor Immune Microenvironment and Predict Prognosis in Triple-Negative Breast Cancer

Background

As a kind of small membrane vesicles, exosomes are secreted by most cell types from multivesicular endosomes, including tumor cells. The relationship between exosomes and immune response plays a vital role in the occurrence and development of tumors. Nevertheless, the interaction between exosomes and the microenvironment of tumors remains unclear. Therefore, we set out to study the influence of exosomes on the triple-negative breast cancer (TNBC) microenvironment.

Method

One hundred twenty-one exosome-related genes were downloaded from ExoBCD database, and IVL, CXCL13, and AP2S1 were final selected because of the association with TNBC prognosis. Based on the sum of the expression levels of these three genes, provided by The Cancer Genome Atlas (TCGA), and the regression coefficients, an exosome risk score model was established. With the median risk score value, the patients in the two databases were divided into high- and low-risk groups. R clusterProfiler package was employed to compare the different enrichment ways between the two groups. The ESTIMATE and CIBERSORT methods were employed to analyze ESTIMATE Score and immune cell infiltration. Finally, the correlation between the immune checkpoint-related gene expression levels and exosome-related risk was analyzed. The relationship between selected gene expression and drug sensitivity was also detected.

Results

Different risk groups exhibited distinct result of TNBC prognosis, with a higher survival rate in the low-risk group than in the high-risk group. The two groups were enriched by immune response and biological process pathways. A better overall survival (OS) was demonstrated in patients with high scores of immune and ESTIMATE rather than ones with low scores. Subsequently, we found that CD4⁺-activated memory T cells and M1 macrophages were both upregulated in the low-risk group, whereas M2 macrophages and activated mast cell were downregulated in the low-risk group in patients from the TCGA and GEO databases, respectively. Eventually, four genes previously proposed to be targets of immune checkpoint inhibitors were evaluated, resulting in the expression levels of CD274, CTLA4, LAG3, and TIM3 being higher in the low-risk group than high-risk group.

Conclusion

The results of our study suggest that exosome-related risk model was related to the prognosis and ratio of immune cell infiltration in patients with TNBC. This discovery may make contributions to improve immunotherapy for TNBC.

Integrated Analysis of Stemness-Related LncRNAs Helps Predict the Immunotherapy Responsiveness of Gastric Cancer Patients

The immune microenvironment plays a critical role in tumor biology. As a critical feature of cancers, stemness is acknowledged as a contributor to the development of drug resistance in gastric cancers (GCs). Long non-coding RNAs (lncRNAs) have been revealed to participate in this process. In this study, we aimed to develop a stemness-related lncRNA signature (SRLncSig) with guiding significance for immunotherapy. Three cohorts (TCGA, Zhongshan, and IMvigor210) were enrolled for analysis. A list of stemness-related lncRNAs (SRlncRNAs) was collected by co-expression strategy under the threshold of coefficient value >0.35 and p-value < 0.05. Cox and Lasso regression analysis was further applied to find out the SRlncRNAs with prognosis-predictive value to establish the SRLncSig in the TCGA cohort. IPS and TIDE algorithms were further applied to predict the efficacy of SRLncSig in TCGA and Zhongshan cohorts. IMvigor210 was composed of patients with clinical outcomes of immunotherapy. The results indicated that SRLncSig not only was confirmed as an independent risk factor for GCs but also identified as a robust indicator for immunotherapy. The patient with a lower SRLncSig score was more likely to benefit from immunotherapy, and the results were highly consistent in three cohorts. In conclusion, our study not only could clarify the correlations between stemness and immunotherapy in GC patients but also provided a model to guide the applications of immunotherapy in clinical practice.