29 immune-related genes pairs signature predict the prognosis of cervical cancer patients

An Immune Gene Expression Risk Score for Distant Metastases after Radiotherapy for Cervical Cancer

PURPOSE

To develop an immune-based gene expression risk score to identify patients with cervical cancer at increased risk of distant metastases (DM).

EXPERIMENTAL DESIGN

Tumor biopsies were obtained from 81 patients prior to chemoradiotherapy. Whole-transcriptome RNA sequencing was performed (Illumina NextSeq500). Beginning with 4,723 immune-related genes, a 55-gene risk score for DM was derived using Cox modeling and principal component analysis. It was validated in independent cohorts of 274 patients treated at the Norwegian Radium Hospital (NRH) and 206 patients from The Cancer Genome Atlas (TCGA).

RESULTS

The risk score was predictive of DM (HR, 2.7; P < 0.0001) and lower cause-specific survival (CSS) by univariate analysis (HR, 2.0; P = 0.0003) and multivariate analysis adjusted for clinical factors (DM HR, 3.0; P < 0.0001; CSS HR, 2.2; P = 0.0004). The risk score predicted DM (HR, 1.4; P = 0.05) and CSS (HR, 1.48; P = 0.013) in the NRH cohort and CSS (HR, 1.4; P = 0.03) in TCGA cohort. Higher risk scores were associated with lower CIBERSORT estimates of tumor-infiltrating immune cells, including CD8 T cells and M1 and M2 macrophages (all P < 0.001). Higher risk scores were associated with lower expression (all P < 0.001) of important chemokines (CXCL12, CXCR4), IFN-regulated genes (IRF1, STAT1, IDO1), and immune checkpoint regulators (PD-1, PD-L1, CTLA-4).

CONCLUSIONS

The immune metastatic risk score addresses important challenges in the treatment of cervical cancer-identifying patients at high risk of DM after radiotherapy. The findings of this study indicate that high tumor mutational burden and a "cold," immune-excluded tumor microenvironment influence distant metastatic recurrence. Further validation of the risk score is needed.

A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma

BACKGROUND

Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions.

METHODS

RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses.

RESULTS

ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients.

CONCLUSIONS

The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Cancer-Immunity Marker RNA Expression Levels across Gynecologic Cancers: Implications for Immunotherapy

Our objective was to characterize cancer-immunity marker expression in gynecologic cancers and compare immune landscapes between gynecologic tumor subtypes and with nongynecologic solid tumors. RNA expression levels of 51 cancer-immunity markers were analyzed in patients with gynecologic cancers versus nongynecologic cancers, and normalized to a reference population of 735 control cancers, ranked from 0 to 100, and categorized as low (0-24), moderate (25-74), or high (75-100) percentile rank. Of the 72 patients studied, 43 (60%) had ovarian, 24 (33%) uterine, and 5 (7%) cervical cancer. No two immune profiles were identical according to expression rank (0-100) or rank level (low, moderate, or high). Patients with cervical cancer had significantly higher expression level ranks of immune activating, proinflammatory, tumor-infiltrating lymphocyte markers, and checkpoints than patients with uterine or ovarian cancer (P < 0.001 for all comparisons). However, there were no significant differences in immune marker expression between uterine and ovarian cancers. Tumors with PD-L1 tumor proportional score (TPS) ≥1% versus 0% had significantly higher expression levels of proinflammatory markers (58 vs. 49%, P = 0.0004). Compared to patients with nongynecologic cancers, more patients with gynecologic cancers express high levels of IDO-1 (44 vs. 13%, P < 0.001), LAG3 (35 vs. 21%, P = 0.008), and IL10 (31 vs. 15%, P = 0.002.) Patients with gynecologic cancers have complex and heterogeneous immune landscapes that are distinct from patient to patient and from other solid tumors. High levels of IDO1 and LAG3 suggest that clinical trials with IDO1 inhibitors or LAG3 inhibitors, respectively, may be warranted in gynecologic cancers.

Identification of two immune subtypes and four hub immune-related genes in ovarian cancer through multiple analysis

Immune classification of ovarian cancer (OV) becomes more and more influential for its immunotherapy. However, current studies had few immune subtypes of OV. It is urgent to explore the immune subtypes and deeper hub immune-related genes (IRGs) of OV for follow-up treatment. A total number of 379 OV samples were obtained from UCSC online website. Single sample gene set enrichment analysis of 29 immune gene sets was used for identifying immune subtypes of OV and gene set variation analysis were used for exploring the hallmarks and Kyoto Encyclopedia of Genes and Genomes pathways of immune types. Two immunity subtypes (Immunity_H and Immunity_L) were identified by single sample gene set enrichment analysis. The OV patients in Immunity_H group had longer overall survival compared with those in Immunity_L group. The Immunity_H had higher stromal score, immune score and estimate score and the tumor purity had the adverse tendency. Besides, the gene set variation analysis enrichment results showed positive relationship between improved immunoreaction and pathways correlated to classical signaling pathway (PI3K/AKT/MTOR, P53, TNFA/NFkB signaling pathways) and immune responses (T/B cell receptor signaling pathways and primary immunodeficiency). Furthermore, 4 hub IRGs (CCR5, IL10RA, ITGAL and PTPRC) were jointly dug by weighted gene co-expression network construction and Cytoscape. Our team also explored the mutations of 4 hub IRGs and PTPRC showed nearly 7% amplification. Besides, 8 immune-checkpoint genes had higher expression in Immuity_H group compared with Immuity_L group, except CD276. The correlation between PD-1/PD-L1 and 4 hub IRGs were explored and gene set enrichment analysis were conducted to explore the underlying mechanisms of PTPRC in OV. Finally, western-blotting showed PTPRC could regulate immune checkpoint PD-L1 expression via JAK-STAT signaling pathway. In a word, 2 immune subtypes and 4 hub IRGs of OV were identified by multiple analysis.

HBXIP knockdown inhibits FHL2 to promote cycle arrest and suppress cervical cancer cell proliferation, invasion and migration

Hepatitis B X-interacting protein (HBXIP) and four and a half LIM domain 2 (FHL2) have been reported to serve as independent biomarkers for cervical cancer. The present study evaluated the effects of HBXIP on cervical cancer in terms of its cellular malignant characteristics. Reverse transcription-quantitative PCR and western blotting were used to assess the mRNA and protein expression levels of HBXIP and FHL2 in the human endocervical epithelial End1/E6E7 cell line and the cervical cancer HeLa, CaSki, C33A and SiHa cell lines. After knocking down HBXIP expression by transfection of small interfering RNAs targeting HBXIP, cell cycle progression was assessed using flow cytometry with PI staining. Cell Counting Kit-8, 5-ethynyl-2'-deoxyuridine staining, wound healing and Transwell assays were used to assess cell proliferation, migration and invasion, respectively. Furthermore, co-immunoprecipitation assay was used to evaluate the potential binding relationship between HBXIP and FHL2. Western blotting was used for the analysis of HBXIP and FHL2, cell cycle-associated proteins, including cyclin D1 and cyclin D2, metastasis-associated proteins, including MMP2 and MMP9, and Wnt/β-catenin signaling-associated proteins, including β-catenin and c-Myc. Both HBXIP and FHL2 were found to be highly expressed in cervical cancer cells compared with that in the human endocervical epithelial cell line. HBXIP knockdown suppressed the proliferation, invasion and migration of HeLa cells, but promoted cell cycle arrest at the G₀/G₁ phase. HBXIP was demonstrated to interact with FHL2, and HBXIP knockdown also inhibited FHL2 mRNA and protein expression. By contrast, FHL2 overexpression reversed the inhibitory effects of HBXIP knockdown on the malignant characteristics of cervical cancer cells. Furthermore, HBXIP knockdown blocked the Wnt/β-catenin signaling pathway in HeLa cells, which was also partially reversed by FHL2 overexpression; the decreased β-catenin and c-Myc expression caused by HBXIP knockdown was increased again after FHL2 was overexpressed. In conclusion, these results suggest that HBXIP knockdown suppressed the malignant characteristics of cervical cancer cells through the downregulation of FHL2 expression, indicating a promising insight into the therapeutic target of cervical cancer.

Cancer immunity marker RNA expression levels across gynecologic cancers: Implications for immunotherapy

Background: Our objective was to characterize cancer immunity marker expression in gynecologic cancers and compare immune landscapes between gynecologic tumor subtypes and with non-gynecologic solid tumors. Methods: RNA expression levels of 51 cancer-immunity markers were analyzed in patients with gynecologic cancers vs. non-gynecologic cancers, and normalized to a reference population of 735 control cancers, ranked from 0-100, and categorized as low (0-24), moderate (25-74), or high (75-100) percentile rank. Results: Of the 72 patients studied, 43 (60%) had ovarian, 24 (33%) uterine, and 5 (7%) cervical cancer. No two immune profiles were identical according to expression rank (0-100) or rank level (low, moderate, or high). Patients with cervical cancer had significantly higher expression level ranks of immune activating, pro-inflammatory, tumor infiltrating lymphocyte markers and checkpoints than patients with uterine or ovarian cancer (p<0.001 for all comparisons). However, there were no significant differences in immune marker expression between uterine and ovarian cancers. Tumors with PD-L1 TPS =>1% versus 0% had significantly higher expression levels of pro-inflammatory markers (58 vs. 49%, p=0.0004). Compared to patients with non-gynecologic cancers, more patients with gynecologic cancers express high levels of IDO-1 (44 vs. 13%, p<0.001), LAG3 (35 vs. 21%, p=0.008) and IL10 (31 vs. 15%, p=0.002.) Conclusions: Patients with gynecologic cancers have complex and heterogeneous immune landscapes that are distinct from patient to patient and from other solid tumors. High levels of IDO1 and LAG3 suggest that clinical trials with IDO1 inhibitors or LAG3 inhibitors, respectively, may be warranted in gynecologic cancers.

Eight Aging-Related Genes Prognostic Signature for Cervical Cancer

This study searched for aging-related genes (ARGs) to predict the prognosis of patients with cervical cancer (CC). All data were obtained from Molecular Signatures Database, Cancer Genome Atlas, Gene Expression Integration, and Genotype Organization Expression. The R software was used to screen out the differentially expressed ARGs (DE-ARGs) between CC and normal tissues. A protein-protein interaction network was established by the DE-ARGs. The univariate and multivariate Cox regression analyses were conducted on the first extracted Molecular Complex Detection component, and a prognostic model was constructed. The prognostic model was further validated in the testing set and GSE44001 dataset. Prognosis was analyzed by Kaplan-Meier curves, and accuracy of the prognostic model was assessed by receiver operating characteristic area under the curve analysis. An independent prognostic analysis of risk score and some clinicopathological factors of CC was also performed. The copy-number variant (CNV) and single-nucleotide variant (SNV) of prognostic ARGs were analyzed by the BioPortal database. A clinical practical nomogram was established to predict individual survival probability. Finally, we carried out cell experiment to further verify the prognostic model. An eight-ARG prognostic signature for CC was constructed. High-risk CC patients had significantly shorter overall survival than low-risk patients. The receiver operating characteristic (ROC) curve validated the good performance of the signature in survival prediction. The Figo_stage and risk score served as independent prognostic factors. The eight ARGs mainly enriched in growth factor regulation and cell cycle pathway, and the deep deletion of FN1 was the most common CNV. An eight-ARG prognostic signature for CC was successfully constructed.

A risk prediction model mediated by genes of APOD/APOC1/SQLE associates with prognosis in cervical cancer

Cervical cancer is one of the most common gynecological malignancies. Due to the high heterogeneity of cervical cancer accelerating cancer progression, it is necessary to identify new prognostic markers and treatment regimens for cervical cancer to improve patients' survival rates. We purpose to construct and verify a risk prediction model for cervical cancer patients. Based on the analysis of data from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), differences of genes in normal and cancer samples were analyzed and then used analysis of WGCNA along with consistent clustering to construct single-factor + multi-factor risk models. After regression analysis, the target genes were obtained as prognostic genes and prognostic risk models were constructed, and the validity of the risk model was confirmed using the receiver operating characteristic curve (ROC) and Kaplan-Meier curve. Subsequently, the above model was verified on the GSE44001 data validation followed by independent prognostic analysis. Enrichment analysis was conducted by grouping the high and low risks of the model. In addition, differences in immune analysis (immune infiltration, immunotherapy), drug sensitivity, and other levels were counted by the high and low risks groups. In our study, three prognostic genes including APOD, APOC1, and SQLE were obtained, and a risk model was constructed along with validation based on the above-mentioned analysis. According to the model, immune correlation and immunotherapy analyses were carried out, which will provide a theoretical basis and reference value for the exploration and treatment of cervical cancer.

A systematic assessment of stress insomnia as the high-risk factor for cervical cancer and interplay of cervicovaginal microbiome

Cervical cancer is a dreaded form of cancer in women, the fourth most common cancer, with around 0.3 million females suffering from this disease worldwide. Over the past several decades, global researches have focused on the mitigation of cervical lesions and cancers and have explored the impact of physiological and psychological stress and insomnia on cervical pathogenesis. Furthermore, disruption of the cervicovaginal microbiome profiles is identified as an added high-risk factor for the occurrence of cervical cancer. The physiological regulation of stress has an underlying mechanism controlled via hypothalamic pituitary adrenal (HPA) and sympatho-adrenal medullary (SAM) axes. Disruptions in these axes have been identified as the factors responsible for maintaining the homeostasis balance. Recent studies on microbiomes have offered novel ways to combat cervical cancer and cervix infection by exploring the interplay of the cervicovaginal microbiome. Moreover, the integration of various immune cells and microbiome diversity is known to act as an effective strategy to decipher the cervix biological activity. Cytokine profiling and the related immune competence, and physiological stress and insomnia impart to the regulatory networks underlying the mechanism which may be helpful in designing mitigation strategies. This review addressed the current progress in the research on cervical cancer, HPV infection, immune cell interaction, and physiological stress and insomnia with the cervicovaginal microbiome to decipher the disease occurrence and therapeutic management.

Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Bioinformatics and the Role of NR6A1 in the Progression of HCC

BACKGROUND AND AIMS

Generally acceptable prognostic models for hepatocellular carcinoma (HCC) are not available. This study aimed to establish a prognostic model for HCC by identifying immune-related differentially expressed genes (IR-DEGs) and to investigate the potential role of NR6A1 in the progression of HCC.

METHODS

Bioinformatics analysis using The Cancer Genome Atlas and ImmPort databases was used to identify IR-DEGs. Lasso Cox regression and multivariate Cox regression analysis were used to establish a prognostic model of HCC. Kaplan-Meier analysis and the receiver operating characteristic (ROC) curves were used to evaluate the performance of the prognostic model, which was further verified in the International Cancer Genome Consortium (ICGC) database. Gene set enrichment analysis was used to explore the potential pathways of NR6A1. Cell counting kit 8, colony formation, wound healing, and Transwell migration assays using Huh7 cells, and tumor formation models in nude mice were conducted.

RESULTS

A prognostic model established based on ten identified IR-DEGs including HSPA4, FABP6, MAPT, NDRG1, APLN, IL17D, LHB, SPP1, GLP1R, and NR6A1, effectively predicted the prognosis of HCC patients, was confirmed by the ROC curves and verified in ICGC database. NR6A1 expression was significantly up-regulated in HCC patients, and NR6A1 was significantly associated with a low survival rate. Gene set enrichment analysis showed the enrichment of cell cycle, mTOR, WNT, and ERBB signaling pathways in patients with high NR6A1 expression. NR6A1 promoted cell proliferation, invasiveness, migration, and malignant tumor formation and growth in vitro and in vivo.

CONCLUSIONS

An effective prognostic model for HCC, based on a novel signature of 10 immune-related genes, was established. NR6A1 was up-regulated in HCC and was associated with a poor prognosis of HCC. NR6A1 promoted cell proliferation, migration, and growth of HCC, most likely through the cell cycle, mTOR, WNT, and ERBB signaling pathways.

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most predominate pathological subtype of renal cell carcinoma, causing a recurrence or metastasis rate as high as 20% to 40% after operation, for which effective prognostic signature is urgently needed.

METHODS

The mRNA and miRNA profiles of ccRCC specimens were collected from the Cancer Genome Atlas. MiRNA-pair risk score (miPRS) for each miRNA pair was generated as a signature and validated by univariate and multivariate Cox proportional hazards regression analysis. Functional enrichment was performed, and immune cells infiltration, as well as tumor mutation burden (TMB), and immunophenoscore (IPS) were evaluated between high and low miPRS groups. Target gene-prediction and differentially expressed gene-analysis were performed based on databases of miRDB, miRTarBase, and TargetScan. Multivariate Cox proportional hazards regression analysis was adopted to establish the prognostic model and Kaplan-Meier survival analysis was performed.

FINDINGS

A novel 10 miRNA-pair based signature was established. Area under the time-dependent receiver operating curve proved the performance of the signature in the training, validation, and testing cohorts. Higher TMB, as well as the higher CTLA4-negative PD1-negative IPS, were discovered in high miPRS patients. A prognostic model was built based on miPRS (1 year-, 5 year-, 10 year- ROC-AUC=0.92, 0.84, 0.82, respectively).

INTERPRETATION

The model based on miPRS is a novel and valid tool for predicting the prognosis of ccRCC.

FUNDING

This study was supported by research grants from the China National Natural Scientific Foundation (81903972, 82002018, and 82170752) and Shanghai Sailing Program (19YF1406700 and 20YF1406000).

Lysophosphatidic acid protects cervical cancer HeLa cells from apoptosis induced by doxorubicin hydrochloride

Cervical cancer is one of the most common types of gynecological tumors. Lysophosphatidic acid (LPA), as a bioactive lipid medium, plays an important role in numerous physiological and pathophysiological processes, including the stimulation of cell migration and tumor cell invasion. LPA is increased in the plasma of patients with cervical cancer. Doxorubicin hydrochloride (DOX) is used as a first-line drug in the treatment of cervical cancer in clinics, however, the effect and molecular mechanism of LPA on DOX-induced apoptosis in cervical cancer cells remain unclear. Therefore, the present study aimed to explore the effect and underlying molecular mechanism of LPA on DOX-induced apoptosis in cervical cancer cells. HeLa cells were treated as a control group or with LPA (10 µmol/l), DOX (4 µmol/l) or LPA (10 µmol/l) + DOX (4 µmol/l) for 24 h. Using transmission electron microscopy the results demonstrated that LPA reduced cell death and the degree of chromatin aggregation in DOX-induced HeLa cells. Reverse transcription-quantitative PCR demonstrated that LPA significantly downregulated caspase-3 mRNA expression levels in DOX-induced HeLa cells. Moreover, western blotting demonstrated that LPA significantly reduced caspase-3 and cleaved caspase-3 protein expression levels in DOX-induced HeLa, C33A and SiHa cells. Furthermore, flow cytometry demonstrated that LPA may prevent apoptosis in DOX-induced HeLa cells (P<0.05). Using dichloro-dihydro-fluorescein diacetate assay, it was demonstrated that LPA significantly reduced the intracellular ROS levels induced by DOX. In summary, the present study indicated that LPA may protect HeLa cells from apoptosis induced by DOX. These findings have provided experimental evidence that LPA may be a potential therapeutic target for the treatment of cervical cancer.

The recurrences of cervical cancer: Possibilities of molecular prediction

The incidence of recurrence of cervical cancer ranges from 10 to 40 %. The 5-year survival rate for patients with recurrent cervical cancer is about 5–15 % against the background of current drug therapy. Clinical and morphological characteristics of the tumor process are known, which are used as markers of an unfavorable prognosis for the development of cervical cancer recurrence. The search for molecular prognostic markers of the course of cervical cancer continues.

The aim. To determine the level of immune cycle proteins in patients with cervical cancer 0–IV stages, depending on the occurrence of a relapse of the disease.

Materials and research methods. A retrospective analysis of previously obtained results of a study on the local level of immune cycle proteins in patients with cervical cancer was performed. Three years after follow-up, 2 groups were formed: group 1 – patients treated for cervical cancer without signs of disease progression (n = 83); group 2 – patients with cervical cancer with local or systemic recurrence (n = 18). Used statistical methods: non-parametric methods of statistics using the Kruskal – Wallis test; ROC-analysis for significant values in order to calculate threshold values; determination of the quality of the identified predictive markers by calculating the sensitivity, specificity, accuracy.

Results. Local initial threshold values have a predictive value for predicting the occurrence of cervical cancer recurrence: B7.2 < 10.7 pg/ml (Se = 0.87; Sp = 0.73; Ac = 0.76; AUC = 0.78), PD-L1 ≤ 5.1 pg/ml (Se = 0.87; Sp = 0.68; Ac = 0.71; AUC = 0.76), sCD27 ≥ 32.0 pg/ml (Se = 0.75; Sp = 0.78; Ac = 0.78; AUC = 0.75).

Conclusion. Determination of local levels of B7.2, PD-L1, sCD27 in patients with cervical cancer before treatment can be used to predict the development of disease recurrence during 3 years of follow-up.

Development and Validation of an Immune-Related Prognostic Signature in Cervical Cancer

Background

Cervical cancer is the fourth most frequent gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer.

Methods

Raw data and clinical information of cervical cancer samples were downloaded from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through the ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis, and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway.

Results

There were eight immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing Programmed cell death ligand-1 (PD-L1) expression and PD-1 checkpoint pathway differences between high-risk and low-risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment.

Conclusion

The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

Immune Subtype Profiling and Establishment of Prognostic Immune-Related lncRNA Pairs in Human Ovarian Cancer

This study collected immune-related genes (IRGs) and used gene expression data from TCGA database to construct a molecular subtype of ovarian cancer (OV) based on immune-related lncRNA gene pairs (IRLnc_GPs). The relationships between molecular subtypes and prognosis and clinical characteristics were further explored. IRGs were acquired from the ImmPort database, and round-robin pairing of immune-related lncRNAs was performed. The NMF algorithm was used to identify molecular subtypes, and the immune score of a single sample was calculated through ESTIMATE, TIMER, ssGSEA, MCPcounter, and CIBERSORT. The relationship between molecular subtypes and immune microenvironments was identified. A hypergeometric test was used to test the lncRNA pairs among the OV molecular subtypes (C1 and C2 subtypes). The BH method was used to screen the different lncRNA pairs, and a predictive risk model was constructed and verified. Finally, correlation analysis between the risk model, immune checkpoint genes, and chemotherapy drugs was carried out. Based on IRLnc_GP to classify 373 OV samples of TCGA, the samples were divided into two subtypes, and the prognosis between the subtypes showed significant differences. The C1 subtype with a poor prognosis was more related to the pathways of tumor occurrence and development. We identified 180 differential lncRNA pairs between subtypes and constructed a prognostic risk model based on 8 IRLnc_GPs. In the independent dataset, the distribution of subtypes in functional modules was different and highly repeatable. There were significant differences in the molecular and clinical characteristics of the subtypes and the drug sensitivity of immunotherapy/chemotherapy. In conclusion, the risk model established based on IRLnc_GP can better evaluate the prognosis of OV samples and can also assess the effects of different drug treatments in the high- and low-risk groups, providing new insights and ideas for the treatment of OV.

CDKN2A is a prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma

Cyclin dependent kinase inhibitor 2A (CDKN2A) is an essential regulator of immune cell functionality, but the mechanisms whereby it drives immune infiltration in hepatocellular carcinoma (HCC) remain unclear. In the present study, we studied the association with CDKN2A expression and immune invasion with the risk of developing HCC. A totally of 2207 different genes were found between HCC and adjacent liver tissues from TCGA and GEO databases. CDKN2A was highly expressed in HCC and associated with poorer overall survival and disease-free survival. Notably, CDKN2A expression was positively correlated with infiltrating levels into purity, B cell, CD+8 T cell, CD+4 T cell, macrophage, neutrophil, and dendritic cells in HCC. CDKN2A expression showed strong correlations between diverse immune marker sets in HCC. These findings suggest that CDKN2A expression potentially contributes to regulation of tumor-associated macrophages and can be used as a prognostic biomarker for determining prognosis and immune infiltration in HCC.

Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma

Hepatocellular carcinoma is a common malignant tumor with poor prognosis, poor treatment effect, and lack of effective biomarkers. In this study, bioinformatics analysis of immune-related genes of hepatocellular carcinoma was used to construct a multi-gene combined marker that can predict the prognosis of patients. The RNA expression data of hepatocellular carcinoma were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the IMMPORT database. Differential analysis was performed by Wilcox test to obtain differentially expressed genes. Univariate Cox regression analysis, lasso regression analysis and multivariate Cox regression analysis were performed to establish a prognostic model of immune genes, a total of 5 genes (HDAC1, BIRC5, SPP1, STC2, NR6A1) were identified to construct the models. The expression levels of 5 genes in HCC tissues were significantly different from those in paracancerous tissues. The Kaplan-Meier survival curve showed that the risk score calculated according to the prognostic model was significantly related to the overall survival (OS) of HCC. The receiver operating characteristic (ROC) curve confirmed that the prognostic model had high accuracy. Independent prognostic analysis was performed to prove that the risk value can be used as an independent prognostic factor. Then, the gene expression data of hepatocellular carcinoma in the ICGC database was used as a validation data set for the verification of the above steps. In addition, we used the CIBERSORT software and TIMER database to conduct immune infiltration research, and the results showed that the five genes of the model and the risk score have a certain correlation with the content of immune cells. Moreover, through Gene Set Enrichment Analysis (GSEA) and the construction of protein interaction networks, we found that the p53-mediated signal transduction pathway is a potentially important signal pathway for hepatocellular carcinoma and is positively regulated by certain genes in the prognostic model. In conclusion, this study provides potential targets for predicting the prognosis and treatment of hepatocellular carcinoma patients, and also provides new ideas about the correlation between immune genes and potential pathways of hepatocellular carcinoma.

Bioinformatics-guided analysis uncovers TIGIT as an epigenetically regulated immunomodulator affecting immunotherapeutic sensitivity of gastric cancer

BACKGROUND

Immunomodulatory genes play significant roles in the regulation of immunological properties of gastric cancer, but the effect of epigenetic regulation of these genes on the immune properties is unknown.

METHOD

I analyzed the methylation-expression correlation among all immunomodulators and compared with the non-immunomodulators. The association between epigenetically regulated immunomodulators (ERI) and tumor microenvironment is evaluated. A key immunomodulator TIGIT is further selected to investigate the potential value in the regulation of immunologic properties. Furthermore, the prognostic value and the immunotherapeutic potential of TIGIT are also explored.

RESULT

Four genes are identified as ERIs based on the negative correlation between expression and methylation. Association analysis shows that three ERIs participate in the regulation of the immune microenvironment of gastric cancer. Among these ERIs, TIGIT is identified as a key immunomodulator. TIGIT is found to be significantly associated with immune properties. The high TIGIT expression group tends to display an active immune landscape. TIGIT expression is also found to be associated with survival and immunotherapeutic sensitivity. High TIGIT expression group has a favorable prognosis and is more likely to respond to immunotherapy than the low expression group.

CONCLUSION

TIGIT is an epigenetically regulated immunomodulator of gastric cancer which can modify the immune activity and affect immunotherapeutic sensitivity. These findings can promote the research of epigenetic therapies and improve the survival of cancer patients by sensitizing tumors to immune therapies.

Establishment of Immune-related Gene Pair Signature to Predict Lung Adenocarcinoma Prognosis

Tumor microenvironment (TME) has critical impacts on the pathogenesis of lung adenocarcinoma (LUAD). However, the molecular mechanism of TME effects on the prognosis of LUAD patients remains unclear. Our study aimed to establish an immune-related gene pair (IRGP) model for prognosis prediction and internal mechanism investigation. Based on 702 TME-related differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) training cohort using the ESTIMATE algorithm, a 10-IRGP signature was established to predict LUAD patient prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that DEGs were significantly associated with tumor immune response. In both TCGA training and Gene Expression Omnibus validation datasets, the risk score was an independent prognostic factor for LUAD patients using Lasso-Cox analysis, and patients in the high-risk group had poorer prognosis than those in the low-risk one. In the high-risk group, M2 macrophage and neutrophil infiltrations were higher, while the levels of T cell follicular helpers were significantly lower. The gene set enrichment analysis results showed that DNA repair signaling pathways were involved. In summary, we established an IRGP signature as a potential biomarker to predict the prognosis of LUAD patients.

An Immune-Gene-Based Classifier Predicts Prognosis in Patients With Cervical Squamous Cell Carcinoma

Objective: Immunity plays a vital role in the human papilloma virus (HPV) persistent infection, and closely associates with occurrence and development of cervical squamous cell carcinoma (CSCC). Herein, we performed an integrated bioinformatics analysis to establish an immune-gene signature and immune-associated nomogram for predicting prognosis of CSCC patients. Methods: The list of immunity-associated genes was retrieved from ImmPort database. The gene and clinical information of CSCC patients were obtained from The Cancer Genome Atlas (TCGA) website. The immune gene signature for predicting overall survival (OS) of CSCC patients was constructed using the univariate Cox-regression analysis, random survival forests, and multivariate Cox-regression analysis. This signature was externally validated in GSE44001 cohort from Gene Expression Omnibus (GEO). Then, based on the established signature and the TCGA cohort with the corresponding clinical information, a nomogram was constructed and evaluated via Cox regression analysis, concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots and decision curve analyses (DCAs). Results: A 5-immune-gene prognostic signature for CSCC was established. Low expression of ICOS, ISG20 and high expression of ANGPTL4, SBDS, LTBR were risk factors for CSCC prognosis indicating poor OS. Based on this signature, the OS was significantly worse in high-risk group than in low-risk group (p-value < 0.001), the area under curves (AUCs) for 1-, 3-, 5-years OS were, respectively, 0.784, 0.727, and 0.715. A nomogram incorporating the risk score of signature and the clinical stage was constructed. The C-index of this nomogram was 0.76. AUC values were 0.811, 0.717, and 0.712 for 1-, 3-, 5-years OS. The nomogram showed good calibration and gained more net benefits than the 5-immune-gene signature and the clinical stage. Conclusion: The 5-immune-gene signature may serve as a novel, independent predictor for prognosis in patients with CSCC. The nomogram incorporating the signature risk score and clinical stage improved the predictive performance than the signature and clinical stage alone for predicting 1-year OS.

Discovery and Verification of an Immune-Related Gene Pairs Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma

The study of IRGPs to construct the prognostic signature in head and neck squamous cell carcinoma (HNSCC) has not yet elucidated. The objective of this study was to explore a novel model to predict the prognosis of HNSCC patients. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were set as training and validation cohorts, respectively. The least absolute shrinkage and selection operator (LASSO) and time-dependent ROC were employed to screen the highest frequency immune-related gene pairs (IRGPs) and their best cut-off value. Survival analysis, Cox regression analysis were applied to discover the effects of selected IRGPs signature on survival outcomes. The immune cell proportions were deconvoluted by the CIBERSORT method. After a couple of filtering, we obtained 22 highest frequency IRGPs. The overall survival time of HNSCC patients with a high score of IRGPs was shorter as compared to the ones with a low score in two independent datasets (P < 0.001). Six kinds of immune cells were found to be differentially distributed in the two different risk groups of HNSCC patients (P < 0.001). GO and GSEA analysis showed these differentially expressed genes enriched in multiple molecular functions. The new IRGPs signature probably confers a new insight into the prognosis prediction of HNSCC patients.

Immune-Inhibitory Gene Expression is Positively Correlated with Overall Immune Activity and Predicts Increased Survival Probability of Cervical and Head and Neck Cancer Patients

Background: Limited immunotherapy options are approved for the treatment of cervical cancer and only 10-25% of patients respond effectively to checkpoint inhibition monotherapy. To aid the development of novel therapeutic immune targets, we aimed to explore survival-associated immune biomarkers and co-expressed immune networks in cervical cancer. Methods: Using The Cancer Genome Atlas (TCGA) Cervical Squamous Cell Carcinoma (CESC) data (n = 304), we performed weighted gene co-expression network analysis (WGCNA), and determined which co-expressed immune-related genes and networks are associated with survival probability in CESC patients under conventional therapy. A "Pan-Immune Score" and "Immune Suppression Score" was generated based on expression of survival-associated co-expressed immune networks and immune suppressive genes, which were subsequently tested for association with survival probablity using the TCGA Head Neck Squamous Cell Carcinoma (HNSCC) data (n = 528), representing a second SCC cancer type. Results: In CESC, WGCNA identified a co-expression module enriched in immune response related genes, including 462 genes where high expression was associated with increased survival probability, and enriched for genes associated with T cell receptor, cytokine and chemokine signaling. However, a high level of expression of 43 of the genes in this module was associated with decreased survival probability but were not enriched in particular pathways. Separately, we identified 20 genes associated with immune suppression including inhibitory immune checkpoint and regulatory T cell-related genes, where high expression was associated with increased survival probability. Expression of these 20 immune suppressive genes (represented as "Immune Suppression Score") was highly correlated with expression of overall survival-associated immune genes (represented as "Pan-Immune Score"). However, high expression of seven immune suppression genes, including TWEAK-R, CD73, IL1 family and TGFb family genes, was significantly associated with decreased survival probability. Both scores also significantly associated with survival probability in HNSCC, and correlated with the previously established "Immunophenoscore." Conclusion: CESC and HNSCC tumors expressing genes predictive of T cell infiltrates (hot tumors) have a better prognosis, despite simultaneous expression of many immune inhibitory genes, than tumors lacking expression of genes associated with T cell infiltrates (cold tumors) whether or not these tumor express immune inhibitory genes.

An Innovative Immune Score-Based Prognostic Nomogram for Patients with Cervical Cancer

Background

In the past few years, the immune system and tumor immune microenvironment are becoming increasingly popular as more work has been accomplished in this field. However, nomograms based on immune-related characteristics for prognosis prediction of cervical cancer have not been fully explored to our knowledge. We constructed a novel immune score-based nomogram to predict patients with high risk and poor prognosis.

Materials and Methods

198 patients with cervical cancer from The Cancer Genome Atlas (TCGA) database were included in our study. Immune scores were generated with Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, and clinic-pathological characteristics were also included for subsequent analysis. Cox proportional hazards regression models were performed for univariate and multivariate analyses to screen the significant factors, and a prognostic nomogram was built. Bootstrap resampling analysis was used for internal validation. The calibration curve and concordance index (C-index) were used to assess the predictive performance of the nomogram.

Results

Patients were split into three subgroups based on immune scores. We found that patients with high immune scores conferred significantly better overall survival (OS) compared with those with medium and low immune scores (hazard ratio (HR), 0.305; 95% confidence interval (CI), 0.108-0.869). A nomogram with a C-index of 0.720 had a favorable performance for predicting survival rate for clinical use by combining immune scores with other clinical features. The calibration curves at 3 and 5 years suggested a good consistency between the predicted OS and the actual OS probability.

Conclusions

Our work highlights the potential clinical application significance of immune score-based nomogram in predicting the OS of cervical cancer patients.