A Prognostic Model of 15 Immune-Related Gene Pairs Associated With Tumor Mutation Burden for Hepatocellular Carcinoma

The prognostic value of a combined immune score in tumor and immune cells assessed by immunohistochemistry in triple-negative breast cancer

BACKGROUND

This study aimed to develop a novel combined immune score (CIS)-based model assessing prognosis in triple-negative breast cancer (TNBC).

METHODS

The expression of eight immune markers (PD-1, PD-L1, PD-L2, IDO, TIM3, OX40, OX40L, and H7-H2) was assessed with immunohistochemistry on the tumor cells (TCs) and immune cells (ICs) of 227 TNBC cases, respectively, and subsequently associated with selected clinicopathological parameters and survival. Data retrieved from The Cancer Genome Atlas (TCGA) were further examined to validate our findings.

RESULTS

All immune markers were often expressed in TCs and ICs, except for PD-1 which was not expressed in TCs. In ICs, the expression of all immune markers was positively correlated between one another, except between PD-L1 and OX40, also TIM3 and OX40. In ICs, PD-1, PD-L1, and OX40L positive expression was associated with a longer progression-free survival (PFS; p = 0.040, p = 0.020, and p = 0.020, respectively). In TCs, OX40 positive expression was associated with a shorter PFS (p = 0.025). Subsequently, the TNBC patients were classified into high and low combined immune score groups (CIS-H and CIS-L), based on the expression levels of a selection of biomarkers in TCs (TCIS-H or TCIS-L) and ICs (ICIS-H or ICIS-L). The TCIS-H group was significantly associated with a longer PFS (p < 0.001). Furthermore, the ICIS-H group was additionally associated with a longer PFS (p < 0.001) and overall survival (OS; p = 0.001), at significant levels. In the multivariate analysis, both TCIS-H and ICIS-H groups were identified as independent predictors of favorable PFS (p = 0.012 and p = 0.001, respectively). ICIS-H was also shown to be an independent predictor of favorable OS (p = 0.003). The analysis of the mRNA expression data from TCGA also validated our findings regarding TNBC.

CONCLUSION

Our novel TCIS and ICIS exhibited a significant prognostic value in TNBC. Additional research would be needed to strengthen our findings and identify the most efficient prognostic and predictive biomarkers for TNBC patients.

Mining Prognostic Biomarkers of Thyroid Cancer Patients Based on the Immune-Related Genes and Development of a Reliable Prognostic Risk Model

PURPOSE

Tumor immunity serves an essential role in the occurrence and development of thyroid cancer (THCA). The aim of this study is to establish an immune-related prognostic model for THCA patients by using immune-related genes (IRGs).

METHODS

Wilcox test was used to screen the differentially expressed immune-related genes (DEIRGs) in THCA and normal tissues, then the DEIRGs related to prognosis were identified using univariate Cox regression analysis. According to The Cancer Genome Atlas (TCGA) cohort, we developed a least absolute shrinkage and selection operator (LASSO) regression prognostic model and performed validation analyses regard to the predictive value of the model in internal (TCGA) and external (International Cancer Genome Consortium) cohorts respectively. Finally, we analyzed the correlation among the prognostic model, clinical variables, and immune cell infiltration.

RESULTS

Eighty-two of 2,498 IRGs were differentially expressed between THCA and normal tissues, and 18 of them were related to prognosis. LASSO Cox regression analysis identified seven DEIRGs with the greatest prognostic value to construct the prognostic model. The risk model showed high predictive value for the survival of THCA in two independent cohorts. The risk score according to the risk model was positively associated with poor survival and the infiltration levels of immune cells, it can evaluate the prognosis of THCA patients independent of any other clinicopathologic feature. The prognostic value and genetic alternations of seven risk genes were evaluated separately.

CONCLUSION

Our study established and verified a dependable prognostic model associated with immune for THCA, both the identified IRGs and immune-related risk model were clinically significant, which is conducive to promoting individualized immunotherapy against THCA.

Decoding Immune Signature to Detect the Risk for Early-Stage HCC Recurrence

Hepatocellular carcinoma (HCC) is often recognized as an inflammation-linked cancer, which possesses an immunosuppressive tumor microenvironment. Curative treatments such as surgical resection, liver transplantation, and percutaneous ablation are mainly applicable in the early stage and demonstrate significant improvement of survival rate in most patients. However, 70-80% of patients report HCC recurrence within 5 years of curative treatment, representing an important clinical issue. However, there is no effective recurrence marker after surgical and locoregional therapies, thus, tumor size, number, and histological features such as cancer cell differentiation are often considered as risk factors for HCC recurrence. Host immunity plays a critical role in regulating carcinogenesis, and the immune microenvironment characterized by its composition, functional status, and density undergoes significant alterations in each stage of cancer progression. Recent studies reported that analysis of immune contexture could yield valuable information regarding the treatment response, prognosis and recurrence. This review emphasizes the prognostic value of tumors associated with immune factors in HCC recurrence after curative treatment. In particular, we review the immune landscape and immunological factors contributing to early-stage HCC recurrence, and discuss the immunotherapeutic interventions to prevent tumor recurrence following curative treatments.

Biomarkers and factors in small cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis

OBJECTIVE

The aim of this meta-analysis was to summarize the available results of immunotherapy predictors for small cell lung cancer (SCLC) and to provide evidence-based information for their potential predictive value of efficacy.

METHODS

We searched PubMed, EMBASE, Web of Science, The Cochrane Library, and ClinicalTrials (from January 1, 1975 to November 1, 2021). The hazard ratios (HR) and its 95% confidence intervals (CIs) and tumor response rate of the included studies were extracted.

RESULTS

Eleven studies were eventually included and the pooled results showed that programmed cell death ligand 1 (PD-L1) positive: objective response rate (ORR) (relative risk [RR] = 1.39, 95% CI [0.48, 4.03], p = 0.54), with high heterogeneity (p = 0.05, I²  = 56%); disease control rate [DCR] (RR = 1.31, 95% CI [0.04, 38.57], p = 0.88), with high heterogeneity (p = 0.04, I²  = 75%); overall survival (OS) (HR = 0.89, 95% CI [0.74, 1.07], p = 0.22); and progression-free survival (PFS) (HR = 0.83, 95% CI [0.59, 1.16], p = 0.27), with high heterogeneity (p = 0.005, I²  = 73.1%). TMB-High (TMB-H): OS (HR = 0.86, 95% CI [0.74, 1.00], p = 0.05); PFS (HR = 0.71, 95% CI [0.6, 0.85], p < 0.001). Lactate dehydrogenase (LDH) >upper limit of normal (ULN): OS (HR = 0.95, 95% CI [0.81, 1.11], p = 0.511). Asian patients: OS (HR = 0.87, 95% CI [0.72, 1.04], p = 0.135); White/Non-Asian patients: OS (HR = 0.83, 95% CI [0.76, 0.90], p < 0.001). Liver metastasis patients: OS (HR = 0.93, 95% CI [0.83, 1.05], p = 0.229); PFS (HR = 0.84, 95% CI [0.67, 1.06], p = 0.141). Central nervous system (CNS) metastasis patients: OS (HR = 0.91, 95% CI [0.71, 1.17], p = 0.474); PFS (HR = 1.03, 95% CI [0.66, 1.60], p = 0.903).

CONCLUSION

The available research results do not support the recommendation of PD-L1 positive and TMB-H as predictors for the application of immune checkpoint inhibitors (ICIs) in SCLC patients. LDH, baseline liver metastasis and CNS metastasis may be used as markers/influencing factors for predicting the efficacy of ICIs in SCLC patients. Non-Asian SCLC patients had better efficacy with ICIs in our results.

An inflammation-related gene landscape predicts prognosis and response to immunotherapy in virus-associated hepatocellular carcinoma

Background

Due to the viral infection, chronic inflammation significantly increases the likelihood of hepatocellular carcinoma (HCC) development. Nevertheless, an inflammation-based signature aimed to predict the prognosis and therapeutic effect in virus-related HCC has rarely been established.

Method

Based on the integrated analysis, inflammation-associated genes (IRGs) were systematically assessed. We comprehensively investigated the correlation between inflammation and transcriptional profiles, prognosis, and immune cell infiltration. Then, an inflammation-related risk model (IRM) to predict the overall survival (OS) and response to treatment for virus-related HCC patients was constructed and verified. Also, the potential association between IRGs and tumor microenvironment (TME) was investigated. Ultimately, hub genes were validated in plasma samples and cell lines via qRT-PCR. After transfection with shCCL20 combined with overSLC7A2, morphological change of SMMC7721 and huh7 cells was observed. Tumorigenicity model in nude mouse was established.

Results

An inflammatory response-related gene signature model, containing MEP1A, CCL20, ADORA2B, TNFSF9, ICAM4, and SLC7A2, was constructed by conjoint analysis of least absolute shrinkage and selection operator (LASSO) Cox regression and gaussian finite mixture model (GMM). Besides, survival analysis attested that higher IRG scores were positively relevant to worse survival outcomes in virus-related HCC patients, which was testified by external validation cohorts (the ICGC cohort and GSE84337 dataset). Univariate and multivariate Cox regression analyses commonly proved that the IRG was an independent prognostic factor for virus-related HCC patients. Thus, a nomogram with clinical factors and IRG was also constructed to superiorly predict the prognosis of patients. Featured with microsatellite instability-high, mutation burden, and immune activation, lower IRG score verified a superior OS for sufferers. Additionally, IRG score was remarkedly correlated with the cancer stem cell index and drug susceptibility. The measurement of plasma samples further validated that CCL20 upexpression and SLC7A2 downexpression were positively related with virus-related HCC patients, which was in accord with the results in cell lines. Furthermore, CCL20 knockdown combined with SLC7A2 overexpression availably weakened the tumor growth in vivo.

Conclusions

Collectively, IRG score, serving as a potential candidate, accurately and stably predicted the prognosis and response to immunotherapy in virus-related HCC patients, which could guide individualized treatment decision-making for the sufferers.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

Radiomics signature based on CECT for non-invasive prediction of response to anti-PD-1 therapy in patients with hepatocellular carcinoma

PURPOSE

This study aimed to develop a radiomics signature (RS) based on contrast-enhanced computed tomography (CECT) and evaluate its potential predictive value in hepatocellular carcinoma (HCC) patients receiving anti-PD-1 therapy.

METHOD

CECT scans of 76 HCC patients who received anti-PD-1 therapy were obtained in this study (training group = 53 and validation group = 23). The least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics features of primary and metastatic lesions and establish a RS to predict lesion-level response. Then, a nomogram combined the mean RS (MRS) and clinical variables with patient-level response as the end point.

RESULTS

In the lesion-level analysis, the area under the curves (AUCs) of RS in the training and validation groups were 0.751 (95% CI, 0.668-0.835) and 0.734 (95% CI, 0.604-0.864), respectively. In the patient-level analysis, the AUCs of the nomogram in the training and validation groups were 0.897 (95% CI, 0.798-0.996) and 0.889 (95% CI, 0.748-1.000), respectively. The nomogram stratified patients into low- and high-risk groups, which showed a significant difference in progression-free survival (PFS) (p<0.05).

CONCLUSIONS

The RS is a noninvasive biomarker for predicting anti-PD-1 therapy response in patients with HCC. The nomogram may be of clinical use for identifying high-risk patients and formulating individualised treatments.

Identification of an Oxidative Stress-Related LncRNA Signature for Predicting Prognosis and Chemotherapy in Patients With Hepatocellular Carcinoma

Background: Oxidative stress plays a critical role in oncogenesis and tumor progression. However, the prognostic role of oxidative stress-related lncRNA in hepatocellular carcinomas (HCC) has not been fully explored. Methods: We used the gene expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify oxidative stress-related differentially expressed lncRNAs (DElncRNAs) by pearson correlation analysis. A four-oxidative stress-related DElncRNA signature was constructed by LASSO regression and Cox regression analyses. The predictive signature was further validated by Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) curves, nomogram and calibration plots, and principal component analysis (PCA). Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the signature and immune status. Finally, the correlation between the signature and chemotherapeutic response of HCC patients was analyzed. Results: In our study, the four-DElncRNA signature was not only proved to be a robust independent prognostic factor for overall survival (OS) prediction, but also played a crucial role in the regulation of progression and chemotherapeutic response of HCC. ssGSEA showed that the signature was correlated with the infiltration level of immune cells. HCC patients in high-risk group were more sensitive to the conventional chemotherapeutic drugs including Sorafenib, lapatinib, Nilotinib, Gefitinib, Erlotinib and Dasatinib, which pave the way for targeting DElncRNA-associated treatments for HCC patients. Conclusion: Our study has originated a prognostic signature for HCC based on oxidative stress-related DElncRNAs, deepened the understanding of the biological role of four key DElncRNAs in HCC and laid a theoretical foundation for the choice of chemotherapy.

Mutational and transcriptional alterations and clinicopathological factors predict the prognosis of stage I hepatocellular carcinoma

Background

The prognosis of hepatocellular carcinoma (HCC) has been extensively studied. However, the impact on prognosis of stage I HCC has not been well studied at clincopathological, mutational and transcriptional levels.

Methods

Here we first characterized the influencing factors of prognosis of stage I HCC patients by downloading and analyzing the whole-exome somatic mutation data, messenger ribonucleic acid (mRNA) transcription data, along with demographic and clinical information of 163 stage I HCC patients from the TCGA database. The relationship between the influencing factors and HCC prognosis was studied in detail, and a prediction Nomogram model was established. Figures and tables were plotted using the R software.

Results

TP53, CTNNB1, TTN, MUC16 and ALB were the top mutated genes in stage I HCC. A series of co-mutations and mutually exclusive mutations were identified. Twenty-nine genes with significant stratification on prognosis were identified, including highly mutated LRP1B, ARID1A and PTPRQ. Patients with wild type (WT) genes unanimously exhibited significantly better overall survival rate than those with mutants. Patients with the top 10% tumor mutational burden (TMB) exhibited significantly worse prognosis than the rest 90%. Further characterization of transcriptional profile revealed that membrane functions, cell skeleton proteins, ion channels, receptor function and cell cycle were comprehensively altered in stage I HCC. Univariate and multivariate analyses were performed at clinicopathological, mutational and transcriptional levels. The combined analysis revealed sex, race, TMB, neoplasm histologic grade, Child–Pugh grade, MMRN1, OXT and COX6A2 transcription as independent risk factors. These factors were used to establish a Nomogram model to predict the prognosis of individual HCC patients.

Conclusions

The influencing factors of prognosis of stage I HCC have been characterized for the first time at clinicopathological, mutational and transcriptional levels. A Nomogram model has been established to predict the prognosis. Further validation is needed to confirm the effectiveness and reliability of the model.

Risk predictive model based on three immune-related gene pairs to assess prognosis and therapeutic sensitivity for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) as a common tumor has a poor prognosis. Recently, a combination of atezolizumab and bevacizumab has been recommended as the preferred regimen for advanced HCC. However, the overall response rate of this therapy is low. There is an urgent need to identify sensitive individuals for this precise therapy among HCC patients.

METHODS

The Wilcox test was used to screen the differentially expressed immune-related genes by combining the TCGA cohort and the Immunology Database. Univariate and multivariate Cox regression analysis were used to screen the immune gene pairs concerning prognosis. A predictive model was constructed using LASSO Cox regression analysis, and correlation analysis was conducted between the signature and clinical characteristics. ICGC cohort and GSE14520 were applied for external validations of the predictive risk model. The relationship between immune cell infiltration, TMB, MSI, therapeutic sensitivity of immune checkpoint inhibitors, targeted drugs, and the risk model were assessed by bioinformatics analysis in HCC patients.

RESULTS

A risk predictive model consisting of 3 immune-related gene pairs was constructed and the risk score was proved as an independent prognostic factor for HCC patients combining the TCGA cohort. This predictive model exhibited a positive correlation with tumor size (p < 0.01) and tumor stage (TNM) (p < 0.001) in the chi-square test. The predictive power was verified by external validations (ICGC and GSE14520). The risk score clearly correlated with immune cell infiltration, MSI, immune checkpoints, and markers of angiogenesis.

CONCLUSIONS

Our research established a risk predictive model based on 3 immune-related gene pairs and explored its relationship with immune characteristics, which might help to assess the prognosis and treatment sensitivity to immune and targeted therapy of HCC patients.

The advanced development of molecular targeted therapy for hepatocellular carcinoma

Hepatocellular carcinoma (HCC), one of the most common malignant tumors in China, severely threatens the life and health of patients. In recent years, precision medicine, clinical diagnoses, treatments, and innovative research have led to important breakthroughs in HCC care. The discovery of new biomarkers and the promotion of liquid biopsy technologies have greatly facilitated the early diagnosis and treatment of HCC. Progress in targeted therapy and immunotherapy has provided more choices for precise HCC treatment. Multiomics technologies, such as genomics, transcriptomics, and metabolomics, have enabled deeper understanding of the occurrence and development mechanisms, heterogeneity, and genetic mutation characteristics of HCC. The continued promotion and accurate typing of HCC, accurate guidance of treatment, and accurate prognostication have provided more treatment opportunities and prolonged survival timelines for patients with HCC. Innovative HCC research providing an in-depth understanding of the biological characteristics of HCC will be translated into accurate clinical practices for the diagnosis and treatment of HCC.

The advanced development of molecular targeted therapy for hepatocellular carcinoma

Hepatocellular carcinoma (HCC), one of the most common malignant tumors in China, severely threatens the life and health of patients. In recent years, precision medicine, clinical diagnoses, treatments, and innovative research have led to important breakthroughs in HCC care. The discovery of new biomarkers and the promotion of liquid biopsy technologies have greatly facilitated the early diagnosis and treatment of HCC. Progress in targeted therapy and immunotherapy has provided more choices for precise HCC treatment. Multiomics technologies, such as genomics, transcriptomics, and metabolomics, have enabled deeper understanding of the occurrence and development mechanisms, heterogeneity, and genetic mutation characteristics of HCC. The continued promotion and accurate typing of HCC, accurate guidance of treatment, and accurate prognostication have provided more treatment opportunities and prolonged survival timelines for patients with HCC. Innovative HCC research providing an in-depth understanding of the biological characteristics of HCC will be translated into accurate clinical practices for the diagnosis and treatment of HCC.

Construction and Validation of Two Hepatocellular Carcinoma-Progression Prognostic Scores Based on Gene Set Variation Analysis

Background: Liver hepatocellular carcinoma (LIHC) remains a global health challenge with a low early diagnosis rate and high mortality. Therefore, finding new biomarkers for diagnosis and prognosis is still one of the current research priorities.

Methods: Based on the variation of gene expression patterns in different stages, the LIHC-development genes (LDGs) were identified by differential expression analysis. Then, prognosis-related LDGs were screened out to construct the LIHC-unfavorable gene set (LUGs) and LIHC-favorable gene set (LFGs). Gene set variation analysis (GSVA) was conducted to build prognostic scoring models based on the LUGs and LFGs. ROC curve analysis and univariate and multivariate Cox regression analysis were carried out to verify the diagnostic and prognostic utility of the two GSVA scores in two independent datasets. Additionally, the key LCGs were identified by the intersection analysis of the PPI network and univariate Cox regression and further evaluated their performance in expression level and prognosis prediction. Single-sample GSEA (ssGSEA) was performed to understand the correlation between the two GSVA enrichment scores and immune activity.

Result: With the development of LIHC, 83 LDGs were gradually upregulated and 247 LDGs were gradually downregulated. Combining with LIHC survival analysis, 31 LUGs and 32 LFGs were identified and used to establish the LIHC-unfavorable GSVA score (LUG score) and LIHC-favorable GSVA score (LFG score). ROC curve analysis and univariate/multivariate Cox regression analysis suggested the LUG score and LFG score could be great indicators for the early diagnosis and prognosis prediction. Four genes (ESR1, EHHADH, CYP3A4, and ACADL) were considered as the key LCGs and closely related to good prognosis. The frequency of TP53 mutation and copy number variation (CNV) were high in some LCGs. Low-LFG score patients have active metabolic activity and a more robust immune response. The high-LFG score patients characterized immune activation with the higher infiltration abundance of type I T helper cells, DC, eosinophils, and neutrophils, while the high-LUG score patients characterized immunosuppression with the higher infiltration abundance of type II T helper cells, TRegs, and iDC. The high- and low-LFG score groups differed significantly in immunotherapy response scores, immune checkpoints expression, and IC50 values of common drugs.

Conclusion: Overall, the LIHC-progression characteristic genes can be great diagnostic and prognostic signatures and the two GSVA score systems may become promising indices for guiding the tumor treatment of LIHC patients.

Comprehensive characterization of m6A methylation and its impact on prognosis, genome instability, and tumor microenvironment in hepatocellular carcinoma

Background

N6-methyladenosine (m6A) RNA regulation was recently reported to be important in carcinogenesis and cancer development. However, the characteristics of m6A modification and its correlations with clinical features, genome instability, tumor microenvironments (TMEs), and immunotherapy responses in hepatocellular carcinoma (HCC) have not been fully explored.

Methods

We systematically analyzed the m6A regulator-based expression patterns of 486 patients with HCC from The Cancer Genome Atlas and Gene Expression Omnibus databases, and correlated these patterns with clinical outcomes, somatic mutations, TME cell infiltration, and immunotherapy responses. The m6A score was developed by principal component analysis to evaluate m6A modifications in individual patients.

Results

M6A regulators were dysregulated in HCC samples, among which 18 m6A regulators were identified as risk factors for prognosis. Three m6A regulator-based expression patterns, namely m6A clusters, were determined among HCC patients by m6A regulators with different m6A scores, somatic mutation counts, and specific TME features. Additionally, three distinct m6A regulator-associated gene-based expression patterns were also identified based on prognosis-associated genes that were differentially expressed among the three m6A clusters, showing similar properties as the m6A regulator-based expression patterns. Higher m6A scores were correlated with older age, advanced stages, lower overall survival, higher somatic mutation counts, elevated PD-L1 expression levels, and poorer responses to immune checkpoint inhibitors. The m6A score was validated as an independent and valuable prognostic factor for HCC.

Conclusion

M6A modification is correlated with genome instability and TME in HCC. Evaluating m6A regulator-based expression patterns and the m6A score of individual tumors may help identify candidate biomarkers for prognosis prediction and immunotherapeutic strategy selection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01207-x.

Development and Validation of a Tumor Mutation Burden-Related Immune Prognostic Signature for Ovarian Cancers

Ovarian cancer (OC), one of the most common malignancies of the female reproductive system, is characterized by high incidence and poor prognosis. Tumor mutation burden (TMB), as an important biomarker that can represent the degree of tumor mutation, is emerging as a key indicator for predicting the efficacy of tumor immunotherapy. In our study, the gene expression profiles of OC were downloaded from TCGA and GEO databases. Subsequently, we analyzed the prognostic value of TMB in OC and found that a higher TMB score was significantly associated with a better prognosis (p = 0.004). According to the median score of TMB, 9 key TMB related immune prognostic genes were selected by LASSO regression for constructing a TMB associated immune risk score (TMB-IRS) signature, which can effectively predict the prognosis of OC patients (HR = 2.32, 95% CI = 1.68–3.32; AUC = 0.754). Interestingly, TMB-IRS is also closely related to the level of immune cell infiltration and immune checkpoint molecules (PD1, PD-L1, CTLA4, PD-L2) in OC. Furthermore, the nomogram combined with TMB-IRS and a variety of clinicopathological features can more comprehensively evaluate the prognosis of patients. In conclusion, we explored the relationship between TMB and prognosis and validated the TMB-IRS signature based on TMB score in an independent database (HR = 1.60, 95% CI = 1.13–2.27; AUC = 0.639), which may serve as a novel biomarker for predicting OC prognosis as well as possible therapeutic targets.

Identification of a nucleotide metabolism-related signature to predict prognosis and guide patient care in hepatocellular carcinoma

Background: Hepatocellular carcinoma is a highly malignant tumor with significant heterogeneity. Metabolic reprogramming plays an essential role in the progression of hepatocellular carcinoma. Among them, nucleotide metabolism needs further investigation. Methods: Based on the bioinformatics approach, eleven prognosis-related nucleotide metabolism genes of hepatocellular carcinoma were screened in this study. Based on the Lasso-Cox regression method, we finally identified a prognostic model containing six genes and calculated the risk score for each patient. In addition, a nomogram was constructed on the basis of pathological stage and risk score. Results: Patients with high-risk score had worse prognosis than those with low-risk. The predictive efficiency of the model was efficient in both the TCGA dataset and the ICGC dataset. The risk score is an independent prognostic factor that can be used to screen chemotherapy drugs. In addition, the risk score can be useful in guiding patient care at an early stage. Conclusion: Nucleotide metabolism-related prognostic model can more accurately predict the prognosis of patients with hepatocellular carcinoma. As a novel prediction model, it is expected to help clinical staff to provide targeted treatment and nursing to patients.

Identification and Validation of an Immune-related Prognostic Signature for Hepatocellular Carcinoma

BACKGROUND AND AIMS

The immune system plays vital roles in hepatocellular carcinoma (HCC) initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients.

METHODS

Gene expression data were retrieved from The Cancer Genome Atlas database. The IRPS was established via least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset.

RESULTS

A total of 62 genes were identified as candidate immune-related prognostic genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in the ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be independent risk factors influencing prognosis of HCC patients. The relationships between the IRPS and infiltration of immune cells demonstrated that the IRPS was associated with immune cell infiltration. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.

CONCLUSIONS

The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

Construction of an m6A-related lncRNA pair prognostic signature and prediction of the immune landscape in head and neck squamous cell carcinoma

Background

Mounting evidence indicates that aberrantly expressed N6‐methylandenosine (m6A) modification regulators and long noncoding RNA (lncRNA) influence the development of head and neck squamous cell carcinoma (HNSCC). However, the prognosis of m6A‐related lncRNA (mrlncRNA) in HNSCC has not yet been evaluated.

Methods

We retrieved transcriptome, somatic mutation, and clinical information from The Cancer Genome Atlas database and established a differently expressed mrlncRNA (DEmrlncRNA) pair signature based on least absolute shrinkage and selection operator Cox regression and multivariate Cox analyses. Each sample's risk score was computed premised on the signature, which accurately classified patients into low‐ and high‐risk group by the cut‐off point. The signature was evaluated from the perspective of survival, clinicopathological characteristics, tumor mutation burden (TMB), immune cell infiltration, efficacy of chemotherapeutics, tumor immune microenvironment, and immune checkpoint inhibitor (ICI)‐related genes.

Results

11 DEmrlncRNA pairs were identified and were used to construct the prediction signature. Kaplan–Meier plotter revealed a worse prognosis in high‐risk patients over low‐risk patients (log rank p < 0.001). According to multivariate Cox regression analysis, the hazard ratio of the risk score and 95% confidence interval of 1.722 and (1.488–1.992) (p < 0.001) were obtained. Furthermore, an increased risk score was associated with aggressive clinicopathological features, specific tumor immune infiltration status, increased expression of ICI‐related genes, higher TMB, and higher chemotherapeutics sensitivity (all p < 0.05).

Conclusion

This research demonstrated that the signature premised on DEmrlncRNA pairs was an efficient independent prognostic indicator and may provide a rationale for research on immunotherapeutic and chemotherapeutics strategies for HNSCC patients.

Development of a prognostic signature of patients with esophagus adenocarcinoma by using immune-related genes

Background

Esophageal adenocarcinoma (EAC) is an aggressive malignancy with a poor prognosis. The immune-related genes (IRGs) are crucial to immunocytes tumor infiltration. This study aimed to construct a IRG-related prediction signature in EAC.

Methods

The related data of EAC patients and IRGs were obtained from the TCGA and ImmPort database, respectively. The cox regression analysis constructed the prediction signature and explored the transcription factors regulatory network through the Cistrome database. TIMER database and CIBERSORT analytical tool were utilized to explore the immunocytes infiltration analysis.

Results

The prediction signature with 12 IRGs (ADRM1, CXCL1, SEMG1, CCL26, CCL24, AREG, IL23A, UCN2, FGFR4, IL17RB, TNFRSF11A, and TNFRSF21) was constructed. Overall survival (OS) curves indicate that the survival rate of the high-risk group is significantly shorter than the low-risk group (P = 7.26e−07), and the AUC of 1-, 3- and 5- year survival prediction rates is 0.871, 0.924, and 0.961, respectively. Compared with traditional features, the ROC curve of the risk score in the EAC patients (0.967) is significant than T (0.57), N (0.738), M (0.568), and Stage (0.768). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are accurate by the combined analysis of the risk score, Sex, M stage, and Stage (The AUC of 1- and 3-years are 0.911, and 0.853).

Conclusion

The 12 prognosis-related IRGs might be promising therapeutic targets for EAC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04456-2.

Novel Hypoxia-Related Gene Signature for Risk Stratification and Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common form of liver cancer with limited therapeutic options and low survival rate. The hypoxic microenvironment plays a vital role in progression, metabolism, and prognosis of malignancies. Therefore, this study aims to develop and validate a hypoxia gene signature for risk stratification and prognosis prediction of HCC patients. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases were used as a training cohort, and one Gene Expression Omnibus database (GSE14520) was served as an external validation cohort. Our results showed that eight hypoxia-related genes (HRGs) were identified by the least absolute shrinkage and selection operator analysis to develop the hypoxia gene signature and demarcated HCC patients into the high- and low-risk groups. In TCGA, ICGC, and GSE14520 datasets, patients in the high-risk group had worse overall survival outcomes than those in the low-risk group (all log-rank P < 0.001). Besides, the risk score derived from the hypoxia gene signature could serve as an independent prognostic factor for HCC patients in the three independent datasets. Finally, a nomogram including the gene signature and tumor-node-metastasis stage was constructed to serve clinical practice. In the present study, a novel hypoxia signature risk model could reflect individual risk classification and provide therapeutic targets for patients with HCC. The prognostic nomogram may help predict individualized survival.

Construction and Validation of a Universal Applicable Prognostic Signature for Gastric Cancer Based on Seven Immune-Related Gene Correlated With Tumor Associated Macrophages

Background

Tumor-associated macrophages (TAMs) play a critical role in the progression of malignant tumors, but the detailed mechanism of TAMs in gastric cancer (GC) is still not fully explored.

Methods

We identified differentially expressed immune-related genes (DEIRGs) between GC samples with high and low macrophage infiltration in The Cancer Genome Atlas datasets. A risk score was constructed based on univariate Cox analysis and Lasso penalized Cox regression analysis in the TCGA cohort (n=341). The optimal cutoff determined by the 5-year time-dependent receiver operating characteristic (ROC) curve was considered to classify patients into groups with high and low risk. We conducted external validation of the prognostic signature in four independent cohorts (GSE84437, n=431; GSE62254, n=300; GSE15459, n=191; and GSE26901, n=109) from the Gene Expression Omnibus (GEO) database.

Results

The signature consisting of 7 genes (FGF1, GRP, AVPR1A, APOD, PDGFRL, CXCR4, and CSF1R) showed good performance in predicting overall survival (OS) in the 5 independent cohorts. The risk score presented an obviously positive correlation with macrophage abundance (cor=0.7, p<0.001). A significant difference was found between the high- and low-risk groups regarding the overall survival of GC patients. The high-risk group exhibited a higher infiltration level of M2 macrophages estimated by the CIBERSORT algorithm. In the five independent cohorts, the risk score was highly positively correlated with the stromal cell score, suggesting that we can also evaluate the infiltration of stromal cells in the tumor microenvironment according to the risk score.

Conclusion

Our study developed and validated a general applicable prognostic model for GC from the perspective of TAMs, which may help to improve the precise treatment strategy of GC.

Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer

Recently, growing evidence has revealed the significant effect of reprogrammed metabolism on pancreatic cancer in relation to carcinogenesis, progression, and treatment. However, the prognostic value of metabolism-related genes in pancreatic cancer has not been fully revealed. We identified 379 differentially expressed metabolic-related genes (DEMRGs) by comparing 178 pancreatic cancer tissues with 171 normal pancreatic tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx) databases. Then, we used univariate Cox regression analysis together with Lasso regression for constructing a prognostic model consisting of 15 metabolic genes. The unified risk score formula and cutoff value were taken into account to divide patients into two groups: high risk and low risk, with the former exhibiting a worse prognosis compared with the latter. The external validation results of the International Cancer Genome Consortium (IGCC) cohort and the Gene Expression Omnibus (GEO) cohort further confirm the effectiveness of this prognostic model. As shown in the receiver operating characteristic (ROC) curve, the area under curve (AUC) values of the risk score for overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were 0.871, 0.885, and 0.886, respectively. Based on the Gene Set Enrichment Analysis (GSEA), the 15-gene signature can affect some important biological processes and pathways of pancreatic cancer. In addition, the prognostic model was significantly correlated with the tumor immune microenvironment (immune cell infiltration, and immune checkpoint expression, etc.) and clinicopathological features (pathological stage, lymph node, and metastasis, etc.). We also built a nomogram based on three independent prognostic predictors (including individual neoplasm status, lymph node metastasis, and risk score) for the prediction of 1-, 3-, and 5-year OS of pancreatic cancer, which may help to further improve the treatment strategy of pancreatic cancer.

Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

Background

An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated.

Methods

Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300; GSE15459, n = 191; and GSE26901, n = 109). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort (n = 600) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432; GSE84437, n = 431; and TCGA, n = 336). Immune cell infiltration (ICI) was quantified by the CIBERSORT method.

Results

A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort (AUC > 0.7). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC (p < 0.001). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group (p < 0.001), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor (p = 0.011). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score (p = 0.00085). The patients' risk score increased with the progression of the clinicopathological stage.

Conclusion

In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

Background

Growing attention have been paid to the relationship between TP53 and tumor immunophenotype, but there are still lacking enough search on the field of gastric cancer (GC).

Materials and methods

We identified differential expressed immune-related genes (DEIRGs) between the TP53-altered GC samples (n = 183) and without TP53-altered GC samples (n = 192) in The Cancer Genome Atlas and paired them. In the TCGA cohort (n = 350), a risk score was determined through univariate and multivariate cox regression and Lasso regression analysis. Patients were divided into two groups, high-risk and low-risk, based on the median risk score. Four independent cohorts (GSE84437,n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26901, n = 100) from the Gene Expression Omnibus (GEO) database were used to validate the reliability and universal applicability of the model.

Results

The signature contained 11 gene pairs showed good performance in predicting progression-free survival (PFS), disease-free survival (DFS), disease special survival (DSS), and the overall survival (OS) for GC patients in the TCGA cohort. The subgroup analysis showed that the signature was suitable for GC patients with different characteristics. The signature could capable of distinguish GC patients with good prognosis and poor prognosis in all four independent external validation cohorts. The high- and low-risk groups differed significantly in the proportion of several immune cell infiltration, especially for the T cells memory resting, T cells memory activated and follicular helper, and Macrophage M0, which was also related to the prognosis of GC patients.

Conclusion

The present work proposed an innovative system for evaluating the prognosis of gastric cancer. Considering its stability and general applicability, which may become a widely used tool in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02846-x.

Development and Validation of a Metabolic-related Prognostic Model for Hepatocellular Carcinoma

BACKGROUND AND AIMS

Growing evidence suggests that metabolic-related genes have a significant impact on the occurrence and development of hepatocellular carcinoma (HCC). However, the prognostic value of metabolic-related genes for HCC has not been fully revealed.

METHODS

mRNA sequencing and clinical data were obtained from The Cancer Genome Atlas and the GTEx Genotype-Tissue Expression comprehensive database. Differentially expressed metabolic-related genes in tumor tissues (n=374) and normal tissues (n=160) were identified by the Wilcoxon test. Time-dependent receiver operating characteristic curve analysis, univariate multivariate Cox regression analysis and Kaplan-Meier survival analysis were used to evaluate the predictive effectiveness and independence of the prognostic model. Two independent cohorts (International Cancer Genome Consortiums and GSE14520) were applied to verify the prognostic model.

RESULTS

Our study included a total of 793 patients with HCC. We constructed a risk score consisting of five metabolic-genes (BDH1, RRM2, CYP2C9, PLA2G7, and TXNRD1). For the overall survival rate, the low-risk group had a considerably higher rate than the high-risk group. Univariate and multivariate Cox regression analyses indicated that the risk score was an independent predictor for the prognosis of HCC.

CONCLUSIONS

We constructed and validated a novel prognostic model, which may provide support for the precise treatment of HCC.

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Purpose

Hepatocellular carcinoma (HCC) is a common solid-tumor malignancy with high heterogeneity, and accurate prognostic prediction in HCC remains difficult. This analysis was performed to find a novel prognostic multigene signature.

Methods

The TCGA-LIHC dataset was analyzed for differentially coexpressed genes through weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. A protein-protein interaction (PPI) network and univariate Cox regression analysis of overall survival (OS) were utilized to identify their prognostic value. Next, we used least absolute shrinkage and selection operator (LASSO) Cox regression to establish a prognostic module. Subsequently, the ICGC-LIRI-JP dataset was applied for further validation. Based on this module, HCC cases were stratified into high-risk and low-risk groups, and differentially expressed genes (DEGs) were identified. Functional enrichment analyses of these DEGs were conducted. Finally, single-sample gene set enrichment analysis (ssGSEA) was performed to explore the correlation between the prognostic signature and immune status.

Results

A total of 393 differentially coexpressed genes were obtained. Forty differentially coexpressed hub genes were identified using the CytoHubba plugin, and 38 of them were closely correlated with OS. Afterward, we established the four-gene prognostic signature with an acceptable accuracy (area under the curve [AUC] of 1-year survival: 0.739). The ICGC-LIRI-JP dataset also supported the acceptable accuracy (AUC of 1-year survival:0.752). Compared with low-risk cohort, HCC cases in the high-risk cohort had shorter OS, higher tumor grades, and higher T stages. The risk scores of this signature still act as independent predictors of OS (P<0.001). Functional enrichment analyses suggest that it was mainly organelle fission and nuclear division that were enriched. Finally, ssGSEA revealed that this signature is strongly associated with the immune status of HCC patients.

Conclusions

The proposed prognostic signature of four differentially coexpressed hub genes has satisfactory prognostic ability, providing important insight into the prediction of HCC prognosis.

Response Stratification in the First-Line Combined Immunotherapy of Hepatocellular Carcinoma at Genomic, Transcriptional and Immune Repertoire Levels

BACKGROUND

Immunotherapy combined with VEGF inhibitor has become the new first-line therapy for advanced or metastatic hepatocellular carcinoma (HCC). However, the biomarkers for response and prognosis stratification of HCC first-line combined immunotherapy have not been clarified.

METHODS

Here, we obtained the genomic alteration data from pre-therapeutic samples of 103 HCC patients using a 605-gene NGS test, and obtained the transcriptional and T cell receptor (TCR) diversity data from 18 patients who underwent the first-line combined immunotherapy using RNAseq and TCR sequencing, respectively. Patients received sorafenib/sintilimab or lenvatinib/sintilimab combined first-line therapy and the response was assessed at 3-6 cycles of therapy.

RESULTS

No stratification of response was found in high-frequency key driver gene mutations, including TP53 and TERT. However, significantly higher ratio of progression (PD) was found in patients carrying MDM4 amplification. Similarly, FGF/3/4/19 amplifications could also result in high ratio of PD. The mRNA and lncRNA levels of eight genes related to hepatic metabolism and immune microenvironment exhibited significant differences between PR/SD and PD group, including DGKI, TNFSF14, CHST4, ACTIN1, PFKP, SLC51B, LCK and ERN1, suggesting stratification of response. Furthermore, moderate correlation was identified between the stratification genes (CHST4, SLC51B and ERN1) and immune factors (TIGIT, CD34, ICAM1, CCL5, CXCL9 and CXCL10), suggesting potential roles of these factors in immunoregulation. Strong linear correlation was found between any two of the three indexes for TCR CDR3 diversity, including Shannon-Wiener Index, Simpson index and evenness. However, no significant difference was found in the three indexes between the PR/SD and PD group, suggesting no stratification of response by these indexes.

CONCLUSION

We identified several potential biomarkers for response stratification in the first-line combined immunotherapy. MDM4 was capable of predicting disease progression, and a panel mRNA and lncRNA of eight genes may also predict the response. Further validation is needed to verify these biomarkers.