Identification of a prognostic and therapeutic immune signature associated with hepatocellular carcinoma

Cuproptosis-Related lncRNA Predict Prognosis and Immune Response of LUAD

Background

Lung cancer is the leading cause of cancer deaths worldwide, primarily due to lung adenocarcinoma (LUAD). However, the heterogeneity of programmed cell death results in varied prognostic and predictive outcomes. This study aimed to develop an LUAD evaluation marker based on cuproptosis-related lncRNAs.

Methods

First, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate, LASSO, and multivariate Cox regression analyses were conducted to construct cuproptosis-associated lncRNA models. LUAD patients were categorized into high-risk and low-risk groups using prognostic risk values. Kaplan-Meier analysis, PCA, GSEA, and nomograms were employed to evaluate and validate the results.

Results

7 cuproptosis-related lncRNAs were identified, and a risk model was created. High-risk tumors exhibited cuproptosis-related gene alterations in 95.54% of cases, while low-risk tumors showed alterations in 85.65% of cases, mainly involving TP53. The risk value outperformed other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival. The cuproptosis-related lncRNA-based risk model demonstrated high validity for LUAD evaluation, potentially influencing individualized treatment approaches. Expression analysis of four candidate cuproptosis-related lncRNAs (AL606834.1, AL161431.1, AC007613.1, and LINC02835) in LUAD tissues and adjacent normal tissues revealed significantly higher expression levels of AL606834.1 and AL161431.1 in LUAD tissues, positively correlating with tumor stage, lymph node metastasis, and histopathological grade. Conversely, AC007613.1 and LINC02835 exhibited lower expression levels, negatively correlating with these factors. High expression of AL606834.1 and AL161431.1 indicated poor prognosis, while low expression of AC007613.1 and LINC02835 was associated with unfavorable outcomes. Univariate and multivariate analyses confirmed these lncRNAs as independent risk factors for LUAD prognosis.

Conclusion

The 4 cuproptosis-related (lncRNAsAL606834.1, AL161431.1, AC007613.1, and LINC02835) can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

Potential molecular mechanisms of Huangqin Tang for liver cancer treatment by network pharmacology and molecular dynamics simulations

OBJECTIVE

This study aims to investigate the mechanism of Huangqin Tang in treating liver cancer.

METHODS

Active ingredients and corresponding targets of Huangqin Tang were obtained from the Traditional Chinese Medicine Systems Pharmacology Database. Differentially expressed genes in liver cancer were identified from mRNA expression data. A protein-protein interaction (PPI) network was constructed using differentially expressed genes and Huangqin Tang targets. Random walk with restart (RWR) analysis was performed on the PPI network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted. A drug-active ingredient-gene interaction network was established, and molecular docking and molecular dynamics simulations were performed. Finally, the stability of binding between CDK1 and oroxylin was tested according to cellular thermal shift assay (CETSA).

RESULTS

160 active ingredients, 239 targets, and 1093 differentially expressed genes were identified. RWR analysis identified 10 potential targets for liver cancer. Enrichment analysis revealed protein kinase regulator activity and Steroid hormone biosynthesis as significant pathways. Molecular docking suggested a stable complex between oroxylin A and CDK1. CETSA demonstrated that the combination of oroxylin A and CDK1 increased the stability of CDK1, and the combination efficiency was high.

CONCLUSION

Huangqin Tang may treat liver cancer by targeting CDK1 with oroxylin A. Protein kinase regulator activity and Steroid hormone biosynthesis pathways may play a role in liver cancer treatment with Huangqin Tang. This study provides insight into the mechanistic basis of Huangqin Tang for liver cancer treatment.

Single-cell profiling reveals the metastasis-associated immune signature of hepatocellular carcinoma

AIM

Metastasis is the leading cause of mortality in hepatocellular carcinoma (HCC). The metastasis-associated immune signature in HCC is worth exploring.

METHODS

Bioinformatic analysis was conducted based on the single-cell transcriptome data derived from HCC patients in different stages. Cellular composition, pseudotime state transition, and cell-cell interaction were further analyzed and verified.

RESULTS

Generally, HCC with metastasis exhibited suppressive immune microenvironment, while HCC without metastasis exhibited active immune microenvironment. Concretely, effector regulatory T cells (eTregs) were found to be enriched in HCC with metastasis. PHLDA1 was identified as one of exhaustion-specific genes and verified to be associated with worse prognosis in HCC patients. Moreover, A novel cluster of CCR7+ dendritic cells (DCs) was identified with high expression of maturation and migration marker genes. Pseudotime analysis showed that inhibition of differentiation occurred in CCR7+ DCs rather than cDC1 in HCC with metastasis. Furthermore, interaction analysis showed that the reduction of CCR7+ DCs lead to impaired CCR7/CCL19 interaction in HCC with metastasis.

CONCLUSIONS

HCC with metastasis exhibited upregulation of exhaustion-specific genes of eTregs and inhibition of CCL signal of a novel DC cluster, which added new dimensions to the immune landscape and provided new immune therapeutic targets in advanced HCC.

Solute carrier family 16 member 1 as a potential prognostic factor for pancreatic ductal adenocarcinoma and its influence on tumor immunity

Background

Solute carrier family 16 member 1 (SLC16A1) serves as a biomarker in numerous types of cancer. Tumor immune infiltration has drawn increasing attention in cancer progression and treatment. The objective of our study was to explore the association between SLC16A1 and the tumor immune microenvironment in pancreatic ductal adenocarcinoma (PDAC).

Methods

Data were obtained from The Cancer Genome Atlas. The xCell web tool was used to calculate the proportion of immune cells according to SLC16A1 expression. To further explore the mechanism of SLC16A1, immunity-related genes were screened from differentially expressed genes through weighted gene coexpression network analysis, examined via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, and filtrated using univariate Cox regression and least absolute shrinkage and selection operator regression model combined correlation analysis (P<0.05). Next, CIBERSORT was used to analyze the correlation between immune cells and five important genes. SLC16A1 expression and its clinical role in pancreatic cancer was clarified via immunohistochemical staining experiments. Finally, the effects of SLC16A1 on the results of cancer immunity were evaluated by in vitro experiments.

Results

SLC16A1 was overexpressed in PDAC tissues and could be an independent prognostic factor. SLC16A1 was significantly negatively correlated with overall survival and suppressed the tumor immunity of PDAC. In clinic, SLC16A1 expression was significantly positively correlated with tumor progression and poor prognosis. We also found that SLC16A1 could suppress the antitumor ability of CD8+ T cells.

Conclusions

SLC16A1 is a biomarker for the prognosis of PDAC and can influence the immune environment of PDAC. These findings provide new insights into the treatment of PDAC.

Cathepsin L promotes oesophageal squamous cell carcinoma development and may be associated with tumour-associated macrophages

Background

Oesophageal squamous cell carcinoma (ESCC) is a leading cause of cancer-related deaths worldwide because existing treatments are often insufficient. Therefore, new, reliable biomarkers must be identified. CTSL overexpression is closely associated with tumour progression and poor prognosis. However, the role and mechanism of CTSL as an oncogene in ESCC remain unclear.

Methods

Genome-wide association study (GWAS) data were used for Mendelian randomization analysis to investigate the possible relationships between CTSL and ESCC. The correlation between CTSL expression and prognosis was analysed using GEO, TCGA, and GEPIA data. We compared CTSL expression among the cell types using single-cell sequencing. Correlations between CTSL and the tumour microenvironment, immune cell infiltration, tumour mutational load, immunological checkpoints, and treatment sensitivity in patients with ESCC were investigated. Finally, using mouse models and cellular investigations, we assessed the effects of CTSL on the growth, apoptosis, and metastasis of ESCC tumour cells.

Results

CTSL was overexpressed in ESCC and correlated with prognosis. We also discovered its close association with cell immunity, especially with tumour-associated macrophages and immune checkpoints in the tumour microenvironment. CTSL may play a key role in ESCC development by affecting M2 macrophage polarisation. CTSL and the M2 macrophage marker genes showed significant positive correlations. Mendelian randomization analysis confirmed a relationship between CTSL and ESCC. Finally, our in vitro and in vivo experiments demonstrated that CTSL promoted the proliferation and migration of ESCC cells, validating our bioinformatic analysis.

Conclusion

CTSL emerged as a crucial gene in ESCC that influences patient prognosis and immunity, particularly in association with M2 macrophages. Therefore, targeting or modulating CTSL levels may provide new therapeutic strategies for patients with ESCC.

Predictive three-biomarker panel in peripheral blood mononuclear cells for detecting hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks among the most prevalent cancers and accounts for a significant proportion of cancer-associated deaths worldwide. This disease, marked by multifaceted etiology, often poses diagnostic challenges. Finding a reliable and non-invasive diagnostic method seems to be necessary. In this study, we analyzed the gene expression profiles of 20 HCC patients, 12 individuals with chronic hepatitis, and 15 healthy controls. Enrichment analysis revealed that platelet aggregation, secretory granule lumen, and G-protein-coupled purinergic nucleotide receptor activity were common biological processes, cellular components, and molecular function in HCC and chronic hepatitis B (CHB) compared to healthy controls, respectively. Furthermore, pathway analysis demonstrated that "estrogen response" was involved in the pathogenesis of HCC and CHB conditions, while, "apoptosis" and "coagulation" pathways were specific for HCC. Employing computational feature selection and logistic regression classification, we identified candidate genes pivotal for diagnostic panel development and evaluated the performance of these panels. Subsequent machine learning evaluations assessed these panels' performance in an independent cohort. Remarkably, a 3-marker panel, comprising RANSE2, TNF-α, and MAP3K7, demonstrated the best performance in qRT-PCR-validated experimental data, achieving 98.4% accuracy and an area under the curve of 1. Our findings highlight this panel's promising potential as a non-invasive approach not only for detecting HCC but also for distinguishing HCC from CHB patients.

Integrating single-cell and bulk sequencing data to identify glycosylation-based genes in non-alcoholic fatty liver disease-associated hepatocellular carcinoma

Background

The incidence of non-alcoholic fatty liver disease (NAFLD) associated hepatocellular carcinoma (HCC) has been increasing. However, the role of glycosylation, an important modification that alters cellular differentiation and immune regulation, in the progression of NAFLD to HCC is rare.

Methods

We used the NAFLD-HCC single-cell dataset to identify variation in the expression of glycosylation patterns between different cells and used the HCC bulk dataset to establish a link between these variations and the prognosis of HCC patients. Then, machine learning algorithms were used to identify those glycosylation-related signatures with prognostic significance and to construct a model for predicting the prognosis of HCC patients. Moreover, it was validated in high-fat diet-induced mice and clinical cohorts.

Results

The NAFLD-HCC Glycogene Risk Model (NHGRM) signature included the following genes: SPP1, SOCS2, SAPCD2, S100A9, RAMP3, and CSAD. The higher NHGRM scores were associated with a poorer prognosis, stronger immune-related features, immune cell infiltration and immunity scores. Animal experiments, external and clinical cohorts confirmed the expression of these genes.

Conclusion

The genetic signature we identified may serve as a potential indicator of survival in patients with NAFLD-HCC and provide new perspectives for elucidating the role of glycosylation-related signatures in this pathologic process.

Multi-omics analysis revealed the regulation mode of intratumor microorganisms and microbial signatures in gastrointestinal cancer

OBJECTIVE

Gastrointestinal cancer is one of the most common malignant tumors in the world, and its incidence rate is always high. In recent years, research has shown that microorganisms may play a broad role in the diagnosis, pathogenesis, and treatment of cancer.

METHODS

In this study, samples were first classified according to the microbial expression data of Gastrointestinal cancer, followed by functional enrichment and Immunoassay. In order to better understand the role of intratumor microorganisms in the prognosis, we screened gene signatures and constructed risk model through univariate cox and lasso regression and multivariable cox, then screened microbial signatures using zero-inflated model regression model and constructed risk index (RI), and finally predicted the immunotherapeutic effect of the risk model.

RESULTS

The results indicate that the composition of tumor microorganisms in the C3 subtype is closely related to tumor angiogenesis, and there is a significant difference in the proportion of innate and acquired immune cells between the C2 and C1 subtypes, as well as differences in the physiological functions of immune cells. There are significant differences in the expression of microbial signatures between high and low risk subtypes, with 9 microbial signatures upregulated in high risk subtypes and 15 microbial signatures upregulated in low risk subtypes. These microbial signatures were significantly correlated with the prognosis of patients. The results of immunotherapy indicate that immunotherapy for high-risk subtypes is more effective.

CONCLUSION

Overall, we analyze from the perspective of microorganisms within tumors, pointing out new directions for the diagnosis and treatment of cancer.

The anti-hepatocellular carcinoma effect of Aidi injection was related to the synergistic action of cantharidin, formononetin, and isofraxidin through BIRC5, FEN1, and EGFR

ETHNOPHARMACOLOGICAL RELEVANCE

Aidi injection (ADI) is a popular anti-tumor Chinese patent medicine, widely used in clinics for the treatment of hepatocellular carcinoma (HCC) with remarkable therapeutic effects through multiple targets and pathways. However, the scientific evidence of the synergistic role of the complex chemical component system and the potential mechanism for treating diseases are ignored and remain to be elucidated.

AIM OF THE STUDY

This study aimed to elucidate and verify the cooperative association between the potential active ingredient of ADI, which is of significance to enlarge our understanding of its anti-HCC molecular mechanisms.

MATERIALS AND METHODS

Firstly, the anti-HCC effect of ADI was evaluated in various HCC cells and the zebrafish xenograft model. Subsequently, a variety of bioinformatic technologies, including network pharmacology, weighted gene co-expression network analysis (WGCNA), meta-analysis of gene expression profiles, and pathway enrichment analysis were performed to construct the competitive endogenous RNA (ceRNA) network of ADI intervention in HCC and to establish the relationship between the critical targets/pathways and the key corresponding components, which were involved in ADI against HCC in a synergistic way and were validated by molecular biology experiments.

RESULTS

ADI exerted remarkable anti-HCC in vitro cells and in vivo zebrafish model, especially that the Hep 3B2.1-7 cell showed substantial sensibility to ADI. The ceRNA network revealed that the EGFR/PI3K/AKT signaling pathway was identified as the promising pathway. Furthermore, the meta-analysis also demonstrated the critical role of BIRC5 and FEN1 as key targets. Finally, the synergistic effect of ADI was revealed by discovering the inhibitory effect of cantharidin on BIRC5, formononetin on FEN1 and EGFR, as well as isofraxidin on EGFR.

CONCLUSION

Our study unveiled that the incredible protective effect of ADI on HCC resulted from the synergistic inhibition effect of cantharidin, formononetin, and isofraxidin on multiple targets/pathways, including BIRC5, FEN1, and EGFR/PI3K/AKT, respectively, providing a scientific interpretation of ADI against HCC and a typical example of pharmacodynamic evaluation of other proprietary Chinese patent medicine.

Unleashing the potential of gene signatures as prognostic and predictive tools: A step closer to personalized medicine in hepatocellular carcinoma (HCC)

Hepatocellular carcinoma (HCC) is one of the growing malignancies globally, affecting a myriad of people and causing numerous cancer-related deaths. Despite therapeutic improvements in treatment strategies over the past decades, HCC still remains one of the leading causes of person-years of life lost. Numerous studies have been conducted to assess the characteristics of HCC with the aim of predicting its prognosis and responsiveness to treatment. However, the identified biomarkers have shown limited sensitivity, and the translation of these findings into clinical practice has faced challenges. The development of sequencing techniques has facilitated the exploration of a wide range of genes, leading to the emergence of gene signatures. Although several studies assessed differentially expressed genes in normal and HCC tissues to find the unique gene signature with prognostic value, to date, no study has reviewed the task, and to the best of our knowledge, this review represents the first comprehensive analysis of relevant studies in HCC. Most gene signatures focused on immune-related genes, while others investigated genes related to metabolism, autophagy, and apoptosis. Even though no identical gene signatures were found, NDRG1, SPP1, BIRC5, and NR0B1 were the most extensively studied genes with prognostic value. Finally, despite challenges such as the lack of consistent patterns in gene signatures, we believe that comprehensive analysis of pertinent gene signatures will bring us a step closer to personalized medicine in HCC, where treatment strategies can be tailored to individual patients based on their unique molecular profiles.

Inflammation-related prognostic markers in resected hepatocellular carcinoma

Hepatocellular carcinoma is usually detected late and therapeutic options are unsatisfactory. Despite marked progress in patient care, HCC remains among the deadliest cancers world-wide. While surgical resection remains a key option for early-stage HCC, the 5-year survival rates after surgical resection are limited. One reason for limited outcomes is the lack of reliable prognostic biomarkers to predict HCC recurrence. HCC prognosis has been shown to correlate with different systemic and pathological markers which are associated with patient survival and HCC recurrence. Liver inflammatory processes offer a large variety of systemic and pathological markers which may be exploited to improve the reliability of prognosis and decision making of liver surgeons and hepatologists. The following review aims to dissect the potential tools, targets and prognostic meaning of inflammatory markers in patients with resectable HCC. We analyze changes in circulant cellular populations and assess inflammatory biomarkers as a surrogate of impaired outcomes and provide an overview on predictive gene expression signatures including inflammatory transcriptional patterns, which are representative of poor survival in these patients.

Identification of potential pseudogenes for predicting the prognosis of hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) remains a highly deadly malignant tumor with high recurrence and metastasis rates. Cancer stem cells (CSCs) are involved in tumor metastasis, recurrence, and resistance to drugs, which have attracted widespread attention in recent years. Research has shown that pseudogenes may regulate stemness to promote the progression of HCC, but its specific mechanisms and impact on prognosis remain unclear.

METHODS

In this study, clinical prognosis information of HCC was first downloaded from The Cancer Genome Atlas (TCGA) database. Then we calculated the mRNA expression-based stemness index (mRNAsi) of HCC. We also screened the differentially expressed pseudogene (DEPs) and conducted univariate Cox regression analysis to investigate their effect on the prognosis of HCC. Further, genomic mutation frequency analysis and weighted gene co-expression network analysis (WGCNA) were performed to compare the role of pseudogenes and stemness in promoting the progression of HCC. Finally, we conducted the correlation analysis to examine the potential mechanism of pseudogenes regulating stemness to promote the progression of HCC and detected the possible pathways through the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.

RESULTS

Herein, we revealed that the high stemness of HCC correlated with an unfavorable prognosis. We obtained 31 up-regulated and 8 down-regulated DEPs in HCC and screened CTB-63M22.1, a poor prognostic indicator of HCC. In addition, CTB-63M22.1 had a mutation frequency similar to mRNAsi and acted in a module similar to that of mRNAsi on HCC. We then screened two RNA-binding proteins (RBPs) LIN28B and NOP56 with the highest correlation with stemness. We also discovered that they were primarily enriched in the biological process as examples of mitotic nuclear division and cell cycle.

CONCLUSIONS

Collectively, these results revealed that pseudogenes CTB-63M22.1 may regulate cancer stemness by regulating RBPs, suggesting that CTB-63M22.1 may serve as an innovative therapeutic target and a reliable prognostic marker for HCC.

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients' drug sensitivity was predicted by the pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study. Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.

Construction and validation of a nomogram for patients with multiple hepatocellular carcinoma: A SEER-based study

BACKGROUND

American Joint Committee on Cancer (AJCC)-TNM system doesn't accurately predict prognosis. Our study was designed to identify prognostic factors in patients with multiple hepatocellular carcinoma (MHCC), establish and validate a nomogram model to predict the risk and overall survival (OS) of MHCC patients.

METHODS

We selected eligible HCC patients from the Surveillance, Epidemiology, and End Results (SEER) database, used univariate and multivariate COX regression to determine prognostic factors in MHCC patients, and used these factors to build a nomogram. The accuracy of the prediction was evaluated using the C-index, receiver operating characteristic (ROC) and calibration curve. Decision curve analysis (DCA), net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to compare the nomogram with AJCC-TNM staging system. Finally, the prognosis of different risks was analyzed using Kaplan-Meier (K-M) method.

RESULTS

4950 eligible patients with MHCC were enrolled in our study and randomly assigned to the training cohort and test cohort in a 7:3 ratio. After COX regression analysis, age, sex, histological grade, AJCC-TNM stage, tumor size, alpha-fetoprotein (AFP), surgery, radiotherapy and chemotherapy in total 9 factors could be used to independently determine OS of patients. the above factors were used to construct a nomogram, and the consistency C-index was 0.775. C-index, DCA, NRI and IDI showed that our nomogram was superior to the AJCC-TNM staging system. K-M plots for OS were performed using the log-rank test, the P-value of which was <0.001.

CONCLUSIONS

The practical nomogram can provide more accurate prognostic prediction for multiple hepatocellular carcinoma patients.

EXOSC10 is a novel hepatocellular carcinoma prognostic biomarker: a comprehensive bioinformatics analysis and experiment verification

Background

Hepatocellular carcinoma (HCC) is a common malignant tumor. There are few studies on EXOSC10 (exosome component 10) in HCC; however, the importance of EXOSC10 for HCC remains unclear.

Methods

In the study, the prognosis value of EXOSC10 and the immune correlation were explored by bioinformatics. The expression of EXOSC10 was verified by tissue samples from clinical patients and in vitro experiment (liver cancer cell lines HepG2, MHCC97H and Huh-7; normal human liver cell line LO2). Immunohistochemistry (IHC) was used to detect EXOSC10 protein expression in clinical tissue from HCC. Huh-7 cells with siEXOSC10 were constructed using lipofectamine 3000. Cell counting kit 8 (CCK-8) and colony formation were used to test cell proliferation. The wound healing and transwell were used to analyze the cell migration capacity. Mitochondrial membrane potential, Hoechst 33342 dye, and flow cytometer were used to detect the change in cell apoptosis, respectively. Differential expression genes (DEGs) analysis and gene set enrichment analysis (GSEA) were used to investigate the potential mechanism of EXOSC10 and were verified by western blotting.

Results

EXOSC10 was highly expressed in tissues from patients with HCC and was an independent prognostic factor for overall survival (OS) in HCC. Increased expression of EXOSC10 was significantly related to histological grade, T stage, and pathological stage. Multivariate analysis indicated that the high expression level of EXOSC10 was correlated with poor overall survival (OS) in HCC. GO and GSEA analysis showed enrichment of the cell cycle and p53-related signaling pathway. Immune analysis showed that EXOSC10 expression was a significant positive correlation with immune infiltration in HCC. In vitro experiments, cell proliferation and migration were inhibited by the elimination of EXOSC10. Furthermore, the elimination of EXOSC10 induced cell apoptosis, suppressed PARP, N-cadherin and Bcl-2 protein expression levels, while increasing Bax, p21, p53, p-p53, and E-cadherin protein expression levels.

Conclusions

EXOSC10 had a predictive value for the prognosis of HCC and may regulate the progression of HCC through the p53-related signaling pathway.

Model for predicting prognosis and immunotherapy based on CD+8 T cells infiltration in neuroblastoma

BACKGROUND

Neuroblastoma (NBL) is an extracranial malignant tumor in children deriving from the neural crest in the sympathetic nervous system. Although various immunotherapy interventions have made significant breakthroughs in many adult cancers, the efficacy of these immunotherapies was still limited in NBL. NBL has low immunogenicity which results in a lack of tumor-infiltrating T lymphocytes in the tumor microenvironment (TME). Moreover, tumor cells can wield many immune evasion strategies both in the TME and systemically to impede lymphocyte infiltration and activation. All these factors hamper the anti-tumor effects of CD8+ T cells during immunotherapy and the levels of infiltrating CD8+ T cells correlate with therapy response.

MATERIALS AND METHODS

In this study, we utilized multidimensional bioinformatic methods to establish a risk model based on CD8+ T cells -related genes (CD8+ TRGs).

RESULTS

We obtained 33 CD8+ TRGs with well-predictive ability for prognosis in both GSE49711 and E-MTAB-8248 cohorts. Then, 12 CD8+ TRGs including HK2, RP2, HPSE, ELL2, GFI1, SLC22A16, FCGR3A, CTSS, SH2D1A, RBP5, ATF5, and ADAM9 were finally identified for risk model construction and validation. This model revealed a stable performance in prognostic prediction of the overall survival (OS) and event-free survival (EFS) in patients with NBL. Additionally, our research indicated that the immune and stromal scores, immune-related pathways, immune cell infiltration, the expression of major histocompatibility complex (MHC) and immune checkpoint molecules, immunotherapy response, and drug susceptibility revealed significant differences between high and low-risk groups.

CONCLUSIONS

According to our analyses, the constructed CD8+ TRGs-based risk model may be promising for the clinical prediction of anti-tumor therapy responses and prognoses in NBL.

Molecular subtypes and scoring tools related to Foxo signaling pathway for assessing hepatocellular carcinoma prognosis and treatment responsiveness

Background: Transcription factors in Foxo signaling pathway influence hepatocellular carcinoma metastasis through epithelial mesenchymal transition-related pathways. Prognostic factors in the Foxo signaling pathway are feasible for HCC prognosis and therapeutic management. Methods: Based on the differentially expressed genes and Foxo signaling pathway genes in HCC, the ConsensusClusterPlus package was conducted to identify Foxo signaling pathway-related molecular subtypes in HCC. Based on the DEGs in the FMSs, the optimal prognostic factors in HCC were screened by cox and least absolute shrinkage and selection operator (LASSO) cox analysis to form the Foxo prognosis score (FPS). The prognostic predictive effectiveness of FPS was assessed by Kaplan Meier (K-M) analysis and Receiver Operating Characteristic (ROC) analysis. Additionally, tumor microenvironment (TME) score, tumor mutation burden (TMB) and treatment sensitivity differences in FMSs and FPS groups were also evaluated. Results: There were low, medium and high Foxo signaling pathway activity molecular subtypes in HCC named FMS 1, FMS 2 and FMS 3, respectively. FMS 1 with lowest Foxo signaling pathway activity presented an excellent survival advantage, while FMS 3 with highest Foxo signaling pathway activity exhibited an inhibitory TME status. According to FPS grouping, low FPS exhibited favorable survival, low TMB and anti-tumor activity. Patients in the low FPS group were mostly in the early stage of cancer. Moreover, we found that patients with high and low FPS exhibited different sensitivity to chemotherapy, and patients with low FPS were more sensitive to immunotherapy. Conclusion: We revealed a novel molecular subtype and prognostic tool based on Foxo signaling pathway signature, which could potentially provide a direction for accurate and effective assessment of potential personalized treatment options and prognostic management for HCC patients.

Deconvolution of bulk gene expression profiles reveals the association between immune cell polarization and the prognosis of hepatocellular carcinoma patients

BACKGROUND

Many studies have utilized computational methods, including cell composition deconvolution (CCD), to correlate immune cell polarizations with the survival of cancer patients, including those with hepatocellular carcinoma (HCC). However, currently available cell deconvolution estimated (CDE) tools do not cover the wide range of immune cell changes that are known to influence tumor progression.

RESULTS

A new CCD tool, HCCImm, was designed to estimate the abundance of tumor cells and 16 immune cell types in the bulk gene expression profiles of HCC samples. HCCImm was validated using real datasets derived from human peripheral blood mononuclear cells (PBMCs) and HCC tissue samples, demonstrating that HCCImm outperforms other CCD tools. We used HCCImm to analyze the bulk RNA-seq datasets of The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples. We found that the proportions of memory CD8+ T cells and Tregs were negatively associated with patient overall survival (OS). Furthermore, the proportion of naïve CD8+ T cells was positively associated with patient OS. In addition, the TCGA-LIHC samples with a high tumor mutational burden had a significantly high abundance of nonmacrophage leukocytes.

CONCLUSIONS

HCCImm was equipped with a new set of reference gene expression profiles that allowed for a more robust analysis of HCC patient expression data. The source code is provided at https://github.com/holiday01/HCCImm.

Signature construction and molecular subtype identification based on immune-related genes for better prediction of prognosis in hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) immunotherapy is a focus of current research. We established a model that can effectively predict the prognosis and efficacy of HCC immunotherapy by analyzing the immune genes of HCC.

METHODS

Through the data mining of hepatocellular carcinoma in The Cancer Genome Atlas (TCGA), the immune genes with differences in tumor and normal tissues are screened, and then the univariate regression analysis is carried out to screen the immune genes with differences related to prognosis. The prognosis model of immune related genes is constructed by using the minimum absolute contraction and selection operator (lasso) Cox regression model in the TCGA training set data, The risk score of each sample was calculated, and the survival was compared with the Kaplan Meier curve and the receiver operating characteristic (ROC) curve to evaluate the predictive ability. Data sets from ICGC and TCGA were used to verify the reliability of signatures. The correlation between clinicopathological features, immune infiltration, immune escape and risk score was analyzed.

RESULTS

Seven immune genes were finally determined as the prognostic model of liver cancer. According to these 7 genes, the samples were divided into the high and low risk groups, and the results suggested that the high-risk group had a poorer prognosis, lower risk of immune escape, and better immunotherapy effect. In addition, the expression of TP53 and MSI was positively correlated in the high-risk group. Consensus clustering was performed to identify two main molecular subtypes (named clusters 1 and 2) based on the signature. It was found that compared with cluster 1, better survival outcome was observed in cluster 2.

CONCLUSION

Signature construction and molecular subtype identification of immune-related genes could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC immunotherapy.

Basement membrane-related regulators for prediction of prognoses and responses to diverse therapies in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains a global health threat. Finding a novel biomarker for assessing the prognosis and new therapeutic targets is vital to treating this patient population. Our study aimed to explore the contribution of basement membrane-related regulators (BMR) to prognostic assessment and therapeutic response prediction in HCC.

MATERIAL AND METHODS

The RNA sequencing and clinical information of HCC were downloaded from TCGA-LIHC, ICGC-JP, GSE14520, GSE104580, and CCLE datasets. The BMR signature was created by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and used to separate HCC patients into low- and high-risk groups. We conducted analyses using various R 4.1.3 software packages to compare prognoses and responses to immunotherapy, transcatheter arterial chemoembolization (TACE), and chemotherapeutic drugs between the groups. Additionally, stemness indices, molecular functions, and somatic mutation analyses were further explored in these subgroups.

RESULTS

The BMR signature included 3 basement membrane-related genes (CTSA, P3H1, and ADAM9). We revealed that BMR signature was an independent risk contributor to poor prognosis in HCC, and high-risk group patients presented shorter overall survival. We discovered that patients in the high-risk group might be responsive to immunotherapy, while patients in the low-risk group may be susceptible to TACE therapy. Over 300 agents were screened to identify effective drugs for the two subgroups.

CONCLUSION

Overall, basement membrane-related regulators represent novel biomarkers in HCC for assessing prognosis, response to immunotherapy, the effectiveness of TACE therapy, and drug susceptibility.

Establishment and validation of exhausted CD8+ T cell feature as a prognostic model of HCC

Objectives

The exhausted CD8+T (Tex) cells are a unique cell population of activated T cells that emerges in response to persistent viral infection or tumor antigens. Tex cells showed the characteristics of aging cells, including weakened self-renewal ability, effector function inhibition, sustained high expression of inhibitory receptors including PD-1, TIGIT, TIM-3, and LAG-3, and always accompanied by metabolic and epigenetic reprogramming. Tex cells are getting more and more attention in researching immune-related diseases and tumor immunotherapy. However, studies on Tex-related models for tumor prognosis are still lacking. We hope to establish a risk model based on Tex-related genes for HCC prognosis.

Methods

Tex-related GEO datasets from different pathologic factors (chronic HBV, chronic HCV, and telomere shortening) were analyzed respectively to acquire differentially expressed genes (DEGs) by the ‘limma’ package of R. Genes with at least one intersection were incorporated into Tex-related gene set. GO, KEGG, and GSEA enrichment analyses were produced. Hub genes and the PPI network were established and visualized by the STRING website and Cytoscape software. Transcription factors and targeting small molecules were predicted by the TRUST and CLUE websites. The Tex-related HCC prognostic model was built by Cox regression and verified based on different datasets. Tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms tested immunotherapy sensitivity. Finally, qRT-PCR and Flow Cytometry was used to confirm the bioinformatic results.

Results

Hub genes such as AKT1, CDC6, TNF and their upstream transcription factor ILF3, Regulatory factor X-associated protein, STAT3, JUN, and RELA/NFKB1 were identified as potential motivators for Tex. Tex-related genes SLC16A11, CACYBP, HSF2, and ATG10 built the HCC prognostic model and helped with Immunotherapy sensitivity prediction.

Conclusion

Our study demonstrated that Tex-related genes might provide accurate prediction for HCC patients in clinical decision-making, prognostic assessment, and immunotherapy. In addition, targeting the hub genes or transcription factors may help to reverse T cell function and enhance the effect of tumor immunotherapy.

Constructing a novel mitochondrial-related gene signature for evaluating the tumor immune microenvironment and predicting survival in stomach adenocarcinoma

BACKGROUND

The incidence and mortality of gastric cancer ranks fifth and fourth worldwide among all malignancies, respectively. Accumulating evidences have revealed the close relationship between mitochondrial dysfunction and the initiation and progression of stomach cancer. However, rare prognostic models for mitochondrial-related gene risk have been built up in stomach cancer.

METHODS

In current study, the expression and prognostic value of mitochondrial-related genes in stomach adenocarcinoma (STAD) patients were systematically analyzed to establish a mitochondrial-related risk model based on available TCGA and GEO databases. The tumor microenvironment (TME), immune cell infiltration, tumor mutation burden, and drug sensitivity of gastric adenocarcinoma patients were also investigated using R language, GraphPad Prism 8 and online databases.

RESULTS

We established a mitochondrial-related risk prognostic model including NOX4, ALDH3A2, FKBP10 and MAOA and validated its predictive power. This risk model indicated that the immune cell infiltration in high-risk group was significantly different from that in the low-risk group. Besides, the risk score was closely related to TME signature genes and immune checkpoint molecules, suggesting that the immunosuppressive tumor microenvironment might lead to poor prognosis in high-risk groups. Moreover, TIDE analysis demonstrated that combined analysis of risk score and immune score, or stromal score, or microsatellite status could more effectively predict the benefit of immunotherapy in STAD patients with different stratifications. Finally, rapamycin, PD-0325901 and dasatinib were found to be more effective for patients in the high-risk group, whereas AZD7762, CEP-701 and methotrexate were predicted to be more effective for patients in the low-risk group.

CONCLUSIONS

Our results suggest that the mitochondrial-related risk model could be a reliable prognostic biomarker for personalized treatment of STAD patients.

S100A8 and S100A9 in Cancer

S100A8 and S100A9 are Ca²⁺ binding proteins that belong to the S100 family. Primarily expressed in neutrophils and monocytes, S100A8 and S100A9 play critical roles in modulating various inflammatory responses and inflammation-associated diseases. Forming a common heterodimer structure S100A8/A9, S100A8 and S100A9 are widely reported to participate in multiple signaling pathways in tumor cells. Meanwhile, S100A8/A9, S100A8, and S100A9, mainly as promoters, contribute to tumor development, growth and metastasis by interfering with tumor metabolism and the microenvironment. In recent years, the potential of S100A8/A9, S100A9, and S100A8 as tumor diagnostic or prognostic biomarkers has also been demonstrated. In addition, an increasing number of potential therapies targeting S100A8/A9 and related signaling pathways have emerged. In this review, we will first expound on the characteristics of S100A8/A9, S100A9, and S100A8 in-depth, focus on their interactions with tumor cells and microenvironments, and then discuss their clinical applications as biomarkers and therapeutic targets. We also highlight current limitations and look into the future of S100A8/A9 targeted anti-cancer therapy.

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.

An anoikis-based signature for predicting prognosis in hepatocellular carcinoma with machine learning

Background: Hepatocellular carcinoma (HCC) is a common malignancy with high mortality worldwide. Despite advancements in diagnosis and treatment in recent years, there is still an urgent unmet need to explore the underlying mechanisms and novel prognostic markers. Anoikis has received considerable attention because of its involvement in the progression of human malignancies. However, the potential mechanism of anoikis-related genes (ANRGs) involvement in HCC progression remains unclear. Methods: We use comprehensive bioinformatics analyses to determine the expression profile of ANRGs and their prognostic implications in HCC. Next, a risk score model was established by least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Then, the prognostic value of the risk score in HCC and its correlation with clinical characteristics of HCC patients were further explored. Additionally, machine learning was utilized to identify the outstanding ANRGs to the risk score. Finally, the protein expression of DAP3 was examined on a tissue microarray (TMA), and the potential mechanisms of DAP3 in HCC was explored. Results: ANRGs were dysregulated in HCC, with a low frequency of somatic mutations and associated with prognosis of HCC patients. Then, nine ANRGs were selected to construct a risk score signature based on the LASSO model. The signature presented a strong ability of risk stratification and prediction for overall survival in HCC patients.Additionally, high risk scores were closely correlated with unfavorable clinical features such as advanced pathological stage, poor histological differentiation and vascular invasion. Moreover, The XGBoost algorithm verified that DAP3 was an important risk score contributor. Further immunohistochemistry determined the elevated expression of DAP3 in HCC tissues compared with nontumor tissues. Finally, functional analyses showed that DAP3 may promote HCC progression through multiple cancer-related pathways and suppress immune infiltration. Conclusion: In conclusion, the anoikis-based signature can be utilized as a novel prognostic biomarker for HCC, and DAP3 may play an important role in the development and progression of HCC.

RTP4, a Biomarker Associated with Diagnosing Pulmonary Tuberculosis and Pan-Cancer Analysis

Background

Pulmonary tuberculosis (PTB) is a global epidemic of infectious disease; the purpose of our study was to explore new potential biomarkers for the diagnosis of pulmonary tuberculosis and to use the biomarkers for further pan-cancer analysis.

Methods

Four microarray gene expression sets were downloaded from the GEO public databases and conducted for further analysis. Healthy control (HC) samples and samples of pulmonary tuberculosis (PTB) were calculated with enrichment scores in folate biosynthesis pathways. The scores acted as a new phenotype combined with clinical information (control or PTB) for subsequent analysis. Weight gene coexpression network analysis (WGCNA) was used to seek the modules mostly related to PTB and folate biosynthesis in training sets. Twenty-nine coexistence genes were screened by intersecting the genes in the green-yellow module of GSE28623 and the brown module of GSE83456. We used the protein-protein interaction network analysis to narrow the gene range to search for hub genes. Then, we downloaded the unified and standardized pan-cancer data set from the UCSC database for correlations between biomarkers and prognosis and tumor stage differences.

Results

Eventually, RTP4 was selected as a biomarker. To verify the reliability of this biomarker, an area under the ROC (AUC) was calculated in gene sets (GSE28623, GSE83456, and GSE34608). Lastly, to explore the difference in RTP4 expression before and after antituberculosis treatment, the GSE31348 gene set was enrolled to compare the expressions in weeks 0 and 26. The results showed significant differences between these two time points (p < 0.001). RTP4 was significantly upregulated in the pulmonary tuberculosis group compared to the healthy control group in three gene sets and downregulated after antituberculosis therapy in one gene set. These results suggest that RTP4 can be used as a potential biomarker in diagnosing tuberculosis. The results of pan-cancer analysis showed that high expression of RTP4 in 4 tumor types was positively correlated with poor prognosis and high expression of RTP4 in 6 tumor types was negatively correlated with poor prognosis. We found significant differences in the expression of the RTP4 gene at different stages in 5 types of tumors.

Conclusion

RTP4 might be a new potential biomarker for diagnosing pulmonary tuberculosis.

Genes Modulating Butyrate Metabolism for Assessing Clinical Prognosis and Responses to Systematic Therapies in Hepatocellular Carcinoma

Butyrate, one of the major products of the gut microbiota, has played notable roles in diverse therapies for multiple tumors. Our study aimed to determine the roles of genes that modulate butyrate metabolism (BM) in predicting the clinical prognosis and responses to systemic therapies in hepatocellular carcinoma (HCC). The genes modulating BM were available from the GeneCard database, and gene expression and clinical information were obtained from TCGA-LIHC, GEO, ICGC-JP, and CCLE databases. Candidate genes from these genes that regulate BM were then identified by univariate Cox analysis. According to candidate genes, the patients in TCGA were grouped into distinct subtypes. Moreover, BM- related gene signature (BMGs) was created via the LASSO Cox algorithm. The roles of BMGs in identifying high-risk patients of HCC, assessing the prognoses, and predicting systematic therapies were determined in various datasets. The statistical analyses were fulfilled with R 4.1.3, GraphPad Prism 8.0 and Perl 5.30.0.1 software. In the TCGA cohort, most butyrate-related genes were over-expressed in the B cluster, and patients in the B cluster showed worse prognoses. BMGs constructed by LASSO were composed of eight genes. BMGs exhibited a strong performance in evaluating the prognoses of HCC patients in various datasets, which may be superior to 33 published biomarkers. Furthermore, BMGs may contribute to the early surveillance of HCC, and BMGs could play active roles in assessing the effectiveness of immunotherapy, TACE, ablation therapy, and chemotherapeutic drugs for HCC. BMGs may be served as novel promising biomarkers for early identifying high-risk groups of HCC, as well as assessing prognoses, drug sensitivity, and the responses to immunotherapy, TACE, and ablation therapy in patients with HCC.

Molecular subtyping and IMScore based on immune-related pathways, oncogenic pathways, and DNA damage repair pathways for guiding immunotherapy in hepatocellular carcinoma patients

Background

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. Although immunotherapy provides hope for advanced HCC patients, the outcomes are not satisfactory and vary by individual case. In this study, we sought to establish novel molecular subtypes and a stable model based on tumor-related pathways for guiding the immunotherapy in HCC patients.

Methods

A total of 15 pathways including immune pathways, stromal pathways, oncogenic pathways, and DNA damage repair pathways were used to construct molecular subtypes through consensus clustering. Immune characteristics, gene mutations, and genomic alterations including copy number variations and homologous recombination deficiency (HRD) were analyzed in different clusters. The Tumor Immune Dysfunction and Exclusion (TIDE) framework was used to predict the response to immunotherapy. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were employed to screen prognostic genes for constructing a risk model.

Results

Three clusters/subtypes were constructed including Immune-E, Immune-D and Stromal-E. Immune-D had the worst prognosis and high enrichment of HRD pathways. Immune-E had higher immune infiltration, higher expression of major histocompatibility complex (MHC)-related genes, and higher expression of PD1, PDL1, CTLA4, and LAG3. TP53 alterations frequently occurred in Immune-D. Immune-E had a relatively high response to immunotherapy and was sensitive to chemotherapeutic drugs. Moreover, we constructed an IMScore model that was effective to classify HCC patients into different risk groups, and the IMScore had a better performance than the TIDE score.

Conclusions

This study revealed the complex interaction among the tumor microenvironment (TME), genomic alterations, and tumor-related pathways by exploring the molecular difference of 3 subtypes. The IMScore model has potential to provide guidance for immunotherapy in HCC patients.

A panel of seven immune-related genes can serve as a good predictive biomarker for cervical squamous cell carcinoma

Objective: Cervical cancer is one of the most common gynecological malignancies. The interaction between tumor microenvironment and immune infiltration is closely related to the progression of cervical squamous cell carcinoma (CSCC) and patients' prognosis. Herein, a panel of immune-related genes was established for more accurate prognostic prediction. Methods: The transcriptome information of tumor and normal samples were obtained from TCGA-CSCC and GTEx. Differentially expressed genes (DEGs) were defined from it. Immune-related genes (IRGs) were retrieved from the ImmPort database. After removing the transcriptome data which not mentioned in GSE44001, IR-DEGs were preliminarily identified. Then, TCGA-CSCC samples were divided into training and testing set (3:1) randomly. Univariate Cox analysis, LASSO regression analysis and multivariate Cox analysis were used in turn to construct the signature to predict the overall survival (OS) and disease-free survival (DFS). External validation was performed in GSE44001, and initial clinical validation was performed by qRT-PCR. Function enrichment analysis, immune infiltration analysis and establishment of nomogram were conducted as well. Results: A prognostic prediction signature consisting of seven IR-DEGs was established. High expression of NRP1, IGF2R, SERPINA3, TNF and low expression of ICOS, DES, HCK suggested that CSCC patients had shorter OS (POS<0.001) and DFS (PDFS<0.001). AUC values of 1-, 3-, five- year OS were 0.800, 0.831 and 0.809. Analyses in other validation sets showed good consistency with the results in training set. The signature can serve as an independent prognostic factor for OS (HR = 1.166, p < 0.001). AUC values of 1-, 3-, five- year OS based on the nomogram were 0.769, 0.820 and 0.807. Functional enrichment analysis suggested that these IR-DEGs were associated with receptor interaction and immune cell activity. Immune infiltration analysis indicated that patients in high-risk group had lower immune infiltration, weaker immune function, and were more likely to benefit from immune checkpoint inhibitor therapy. Through qRT-PCR on clinical samples, expression of NRP1, IGF2R, SERPINA3 and TNF were significantly upregulated in tumor tissue, while ICOS and DES were significantly downregulated. Conclusion: To conclude, the immune-related signature can provide strong support for exploration of immune infiltration, prediction of prognosis and response to immunotherapy through stratify CSCC patients into subgroups.

A novel stemness-hypoxia-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma

Background

The specific differentiation potential, unlimited proliferation, and self-renewal capacity of cancer stem cells (CSCs) are closely related to the occurrence, recurrence, and drug resistance of hepatocellular carcinoma (HCC), as well as hypoxia. Therefore, an in-depth analysis of the relationship between HCC stemness, oxygenation status, and the effectiveness of immunotherapy is necessary to improve the poor prognosis of HCC patients.

Methods

The weighted gene co-expression network analysis (WGCNA) was utilized to find hypoxia-related genes, and the stemness index (mRNAsi) was evaluated using the one-class logistic regression (OCLR) technique. Based on stemness-hypoxia-related genes (SHRGs), population subgroup categorization using NMF cluster analysis was carried out. The relationship between SHRGs and survival outcomes was determined using univariate Cox regression. The LASSO-Cox regression strategy was performed to investigate the quality and establish the classifier associated with prognosis. The main effect of risk scores on the tumor microenvironment (TME) and its response to immune checkpoint drugs was also examined. Finally, qRT-PCR was performed to explore the expression and prognostic value of the signature in clinical samples.

Results

After identifying tumor stemness- and hypoxia-related genes through a series of bioinformatics analyses, we constructed a prognostic stratification model based on these SHRGs, which can be effectively applied to the prognostic classification of HCC patients and the prediction of immune checkpoint inhibitors (ICIs) efficacy. Independent validation of the model in the ICGC cohort yielded good results. In addition, we also constructed hypoxic cell models in Herp3B and Huh7 cells to verify the expression of genes in the prognostic model and found that C7, CLEC1B, and CXCL6 were not only related to the tumor stemness but also related to hypoxia. Finally, we found that the constructed signature had a good prognostic value in the clinical sample.

Conclusions

We constructed and validated a stemness-hypoxia-related prognostic signature that can be used to predict the efficacy of ICIs therapy. We also verified that C7, CLEC1B, and CXCL6 are indeed associated with stemness and hypoxia through a hypoxic cell model, which may provide new ideas for individualized immunotherapy.

A novel risk score based on immune-related genes for hepatocellular carcinoma as a reliable prognostic biomarker and correlated with immune infiltration

BACKGROUND

Immunological-related genes (IRGs) play a critical role in the immune microenvironment of tumors. Our study aimed to develop an IRG-based survival prediction model for hepatocellular carcinoma (HCC) patients and to investigate the impact of IRGs on the immune microenvironment.

METHODS

Differentially expressed IRGs were obtained from The Genomic Data Commons Data Portal (TCGA) and the immunology database and analysis portal (ImmPort). The univariate Cox regression was used to identify the IRGs linked to overall survival (OS), and a Lasso-regularized Cox proportional hazard model was constructed. The International Cancer Genome Consortium (ICGC) database was used to verify the prediction model. ESTIMATE and CIBERSORT were used to estimate immune cell infiltration in the tumor immune microenvironment (TIME). RNA sequencing was performed on HCC tissue specimens to confirm mRNA expression.

RESULTS

A total of 401 differentially expressed IRGs were identified, and 63 IRGs were found related to OS on the 237 up-regulated IRGs by univariate Cox regression analyses. Finally, five IRGs were selected by the LASSO Cox model, including SPP1, BIRC5, STC2, GLP1R, and RAET1E. This prognostic model demonstrated satisfactory predictive value in the ICGC dataset. The risk score was an independent predictive predictor for OS in HCC patients. Immune-related analysis showed that the immune infiltration level in the high-risk group was higher, suggesting that the 5-IRG signature may play an important role in mediating immune escape and immune resistance in the TIME of HCC. Finally, we confirmed the 5-IRG signature is highly expressed in 65 HCC patients with good predictive power.

CONCLUSION

We established and verified a new prognosis model for HCC patients based on survival-related IRGs, and the signature could provide new insights into the prognosis of HCC.

STING pathway contributes to the prognosis of hepatocellular carcinoma and identification of prognostic gene signatures correlated to tumor microenvironment

Background

Hepatocellular carcinoma (HCC) is one of the most malignant solid tumors worldwide. Recent evidence shows that the stimulator of interferon genes (STING) pathway is essential for anti-tumor immunity via inducing the production of downstream inflammatory cytokines. However, its impact on the prognosis and tumor microenvironment of HCC was still limited known.

Methods

We obtained gene expression profiles of HCC from GEO, TCGA, and ICGC databases, and immune-related genes (IRGs) from the ImmPort database. Multivariate Cox regression was performed to identify independent prognostic factors. Nomogram was established to predict survival probability for individual patients. Kaplan–Meier curve was used to evaluate the survival difference. Afterward, ESTIMATE, TISCH, and TIMER databases were combined to assess the immune cell infiltration. Furthermore, the qPCR, western blotting, and immunohistochemistry were done to evaluate gene expression, and in vitro cell models were built to determine cell migratory ability.

Results

We found that gene markers of NLRC3, STING1, TBK1, TRIM21, and XRCC6 within STING pathway were independent prognostic factors in HCC patients. Underlying the finding, a predictive nomogram was constructed in TCGA-training cohort and further validated in TCGA-all and ICGC datasets, showing credible performance. Experimentally, up-regulated TBK1 promotes the ability of HCC cell migration. Next, the survival-related immune-related co-expressed gene signatures (IRCGS) (VAV1, RHOA, and ZC3HAV1) were determined in HCC cohorts and their expression was verified in human HCC cells and clinical samples. Furthermore, survival-related IRCGS was associated with the infiltration of various immune cell subtypes in HCC, the transcriptional expression of prominent immune checkpoints, and immunotherapeutic response.

Conclusion

Collectively, we constructed a novel prognostic nomogram model for predicting the survival probability of individual HCC patients. Moreover, an immune-related prognostic gene signature was determined. Both might function as potential therapeutic targets for HCC treatment in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02734-4.

Evaluate the immune-related eRNA models and signature score to predict the response to immunotherapy in thyroid carcinoma

BACKGROUND

The functional alterations of eRNAs have been reported to be correlated with tumorigenesis. However, the roles of eRNAs in thyroid cancer (THCA) remain still unclear. This study aimed to construct an immune-related eRNA prognostic signature that could effectively predict the survival and prognosis for THCA.

METHODS

The Weighted Gene Co-Expression Network Analysis (WGCNA) was performed to identify THCA-specific immune-related hub genes and immune-related eRNAs were obtained using Pearson correlation analysis. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression were conducted to construct an immune-related eRNA prognostic signature in training cohort, and the predictive capability was verified in test cohort and entire cohort. Kaplan-Meier analysis, principal component analysis (PCA), receiver operating characteristic (ROC) curves, and nomogram were used to validate the risk signature. Furthermore, CIBERSORT, ESTIMATE and ssGSEA were analyzed to explore the tumor immune microenvironment (TIME) of the risk signature, and the response of potential immunotherapeutic were also discussed.

RESULTS

A total of 125 immune-related eRNAs were obtained and 16 immune-related eRNAs were significantly correlated with overall survival (OS). A 9-immune-related eRNA prognostic signature was constructed, and the risk score was identified as an independent predictor. High-risk groups were associated with a poorer OS. Immune microenvironment analysis indicated that low risk score was correlated with higher immuneScore, high immune cell infiltration, and the better response of immunotherapy. Additionally, we also detected 9 immune-related eRNA expression levels in sixty-two matched tumorous and non-tumorous tissues using qRT-PCR analysis.

CONCLUSION

Our immune-related eRNA risk signature that was an independent prognostic factor was strongly correlated with the immune microenvironment and may be promising for the clinical prediction of prognosis and immunotherapeutic responses in THCA patients.

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.

Study on the Prognostic Values of TTC36 Correlated with Immune Infiltrates and Its Methylation in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) remains an incurable disease with a very poor clinical outcome. The purpose of this article was to investigate whether the expression or methylation of tetrapeptide repeat domain 36 (TTC36) could be used as a prognostic marker in hepatocellular carcinoma. TCGA database was used to obtain information on HCC gene expression and the associated clinical features of HCC patients. Differentially expressed genes (DEGs) were screened between 374 HCC specimens and 50 nontumor specimens. The expression and prognostic value of TTC36 were analyzed. The correlations between TTC36 and cancer immune infiltrates were investigated via TIMER. In this study, HCC specimens and nontumor specimens were compared and 35 DEGs were found between them. Among the 35 DEGs, the expression of TTC36 was significantly reduced in HCC samples compared with nontumor samples. Survival tests revealed that patients with low TTC36 expression had a shorter overall survival than patients with high TTC36 expression. TTC36 was found to be an independent predictive factor for HCC in both univariate and multivariate regression analyses. TTC36 was negatively regulated by TTC36 methylation, leading to its low expression in HCC tissues. Immune analysis revealed that TTC36 expression has significant correlations with B cell, T cell CD4+, neutrophil, macrophage, and myeloid dendritic cell. Finally, TTC36 expression was dramatically reduced in HCC cells, and overexpression greatly suppressed HCC cell proliferation and invasion, according to our experimental results. Overall, our data suggested that TTC36 could be applied as a prognostic marker for predicting outcome and immune infiltration in HCC.

Prospective Proteomic Study Identifies Potential Circulating Protein Biomarkers for Colorectal Cancer Risk

Simple Summary

Studies on circulating protein for colorectal cancer risk in a prospective study design is lacking. The aim of the present study was to scan and identify the protein markers by using proteomics technologies in a two-stage case-control study nested within the Shanghai Women’s Health Study (SWHS), a population-based prospective cohort study. In the discovery set, we found 27 circulating proteins with a nominally significant association. Six of them, including CD79B, DDR1, EFNA4, FLRT2, LTA4H, and NCR1, were validated in the validation phase of the study. This study is the first to evaluate over 1000 circulating proteins in prediagnostic blood samples for their associations with CRC risk in East Asians.

Abstract

Background: Proteomics-based technologies are emerging tools used for cancer biomarker discovery. Limited prospective studies have been conducted to evaluate the role of circulating proteins in colorectal cancer (CRC) development. Methods: A two-stage case-control proteomics study nested in the Shanghai Women’s Health Study was conducted. A total of 1104 circulating proteins were measured in the discovery phase, consisting of 100 incident CRC cases and 100 individually matched controls. An additional 60 case-control pairs were selected for validation. Protein profiling at both stages was completed using the Olink platforms. Conditional logistic regression was used to evaluate the associations between circulating proteins and CRC risk. The elastic net method was employed to develop a protein score for CRC risk. Results: In the discovery set, 27 proteins showed a nominally significant association with CRC risk, among which 22 were positively and 5 were inversely associated. Six of the 27 protein markers were significantly associated with CRC risk in the validation set. In the analysis of pooled discovery and validation sets, odds ratios (ORs) per standard deviation (SD) increase in levels of these proteins were 1.54 (95% confidence interval (CI): 1.15–2.06) for CD79B; 1.71 (95% CI: 1.24–2.34) for DDR1; 2.04 (95% CI: 1.39–3.01) for EFNA4; 1.54 (95% CI: 1.16–2.02) for FLRT2; 2.09 (95% CI: 1.47–2.98) for LTA4H and 1.88 (95% CI: 1.35–2.62) for NCR1. Sensitivity analyses showed consistent associations for all proteins with the exclusion of cases diagnosed within the first two years after the cohort enrollment, except for CD79B. Furthermore, a five-protein score was developed based on the six proteins identified and showed significant associations with CRC risk in both discovery and validation sets (Discovery: OR_1-SD = 2.46, 95% CI: 1.53–3.95; validation: OR_1-SD = 4.16, 95% CI: 1.92–8.99). Conclusions: A panel of five protein markers was identified as potential biomarkers for CRC risk. Our findings provide novel insights into the etiology of CRC and may facilitate the risk assessment of the malignancy.

Colocalized targeting of TGF-β and PD-L1 by bintrafusp alfa elicits distinct antitumor responses

Background

Bintrafusp alfa (BA) is a bifunctional fusion protein designed for colocalized, simultaneous inhibition of two immunosuppressive pathways, transforming growth factor-β (TGF-β) and programmed death-ligand 1 (PD-L1), within the tumor microenvironment (TME). We hypothesized that targeting PD-L1 to the tumor by BA colocalizes the TGF-β trap (TGF-βRII) to the TME, enabling it to sequester TGF-β in the tumor more effectively than systemic TGF-β blockade, thereby enhancing antitumor activity.

Methods

Multiple technologies were used to characterize the TGF-β trap binding avidity. BA versus combinations of anti-PD-L1 and TGF-β trap or the pan-TGF-β antibody fresolimumab were compared in proliferation and two-way mixed lymphocyte reaction assays. Immunophenotyping of tumor-infiltrating lymphocytes (TILs) and RNA sequencing (RNAseq) analysis assessing stromal and immune landscape following BA or the combination therapy were performed in MC38 tumors. TGF-β and PD-L1 co-expression and their associated gene signatures in MC38 tumors and human lung carcinoma tissue were studied with single-cell RNAseq (scRNAseq) and immunostaining. BA-induced internalization, degradation, and depletion of TGF-β were investigated in vitro.

Results

BA and fresolimumab had comparable intrinsic binding to TGF-β1, but there was an ~80× avidity-based increase in binding affinity with BA. BA inhibited cell proliferation in TGF-β-dependent and PD-L1-expressing cells more potently than TGF-β trap or fresolimumab. Compared with the combination of anti-PD-L1 and TGF-β trap or fresolimumab, BA enhanced T cell activation in vitro and increased TILs in MC38 tumors, which correlated with efficacy. BA induced distinct gene expression in the TME compared with the combination therapy, including upregulation of immune-related gene signatures and reduced activities in TGF-β-regulated pathways, such as epithelial-mesenchymal transition, extracellular matrix deposition, and fibrosis. Regulatory T cells, macrophages, immune cells of myeloid lineage, and fibroblasts were key PD-L1/TGF-β1 co-expressing cells in the TME. scRNAseq analysis suggested BA modulation of the macrophage phenotype, which was confirmed by histological assessment. PD-L1/TGF-β1 co-expression was also seen in human tumors. Finally, BA induced TGF-β1 internalization and degradation in the lysosomes.

Conclusion

BA more effectively blocks TGF-β by targeting TGF-β trap to the tumor via PD-L1 binding. Such colocalized targeting elicits distinct and superior antitumor responses relative to single agent combination therapy.

Immune cell infiltration and immunotherapy in hepatocellular carcinoma

Hepatocellular carcinoma is a highly malignant tumor and patients yield limited benefits from the existing treatments. The application of immune checkpoint inhibitors is promising but the results described in the literature are not favorable. It is therefore urgent to systematically analyze the immune microenvironment of HCC and screen the population best suited for the application of immune checkpoint inhibitors to provide a basis for clinical treatment. In this study, we collected The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC)-related data sets to evaluate the immune microenvironment and immune cell infiltration (ICI) in HCC. Three independent ICI subtypes showing significant differences in survival were identified. Further, TCGA-LIHC immunophenoscore (IPS) was used to identify the differentially expressed genes between high- and low-IPS in HCC, so as to identify the immune gene subtypes in HCC tumors. The ICI score model for HCC was constructed, whereby we divided HCC samples into high- and low-score groups based on the median ICI score. The differences between these groups in genomic mutation load and immunotherapy benefit in HCC were examined in detail to provide theoretical support for accurate immunotherapy strategy in HCC. Finally, four genes were screened, which could accurately predict the subtype based on the tumor immune infiltration score. The findings may provide a basis and simplify the process for screening clinical drugs suitable for relevant subgroups.

Comprehensive Analysis of Tumor Immune Microenvironment Characteristics for the Prognostic Prediction and Immunotherapy of Oral Squamous Cell Carcinoma

Background: Oral squamous cell carcinoma (OSCC) is the most common cancer of oral and maxillofacial region. A recent clinical research has shown that tumor immune microenvironment (TIME)cells are closely related to immunotherapy sensitivity and OSCC prognosis. Nonetheless, a comprehensive analysis of TIME in OSCC has not been reported.

Methods: Bioinformatics and computational algorithms were employed to determine the significance of TIME cells in 257 OSCC patients. TIME scores were measured by three TIME models, and then used to evaluate the prognosis of OSCC patients.

Results: High TIME score was characterized by better prognosis in OSCC patients less than 60 years old, overexpression of immunotherapy targets (e.g., PD-1 and CLTA-4), and higher T-cell activity to inhibit tumor growth. Besides, poor prognosis was associated with low time score.

Conclusion: TIME score exhibited potential as a prognostic biomarker and an indicator in predict immunotherapeutic outcomes. Through the understanding of TIME model, this study can provide a better scheme for immunotherapy as the effective treatment of OSCC patients in the future.

Comprehensive analysis of the correlations of S100B with hypoxia response and immune infiltration in hepatocellular carcinoma

S100B has been found to be dysregulated in many cancers including hepatocellular carcinoma (HCC). However, the functions of S100B and its underlying mechanisms in HCC remain poorly understood, especially in the tumor microenvironment. In this study, functions enrichment analysis indicated that S100B expression was correlated with hypoxia and immune responses. We found that hypoxia could induce S100B expression in an HIF-1α-dependent manner in HepG2 cells. Luciferase reporter and ChIP-qRCR assays demonstrated that HIF-1α regulates S100B transcription by directly binding to hypoxia-response elements (HREs) of the S100B promoter. Functionally, knockdown of S100B reduces hypoxia-induced HepG2 cell invasion and migration. Furthermore, GSVA enrichment results displayed that S100B and its co-expressed genes were positively correlated with EMT pathway in HCC. Additionally, GO/KEGG cluster analysis results indicated that co-expressed genes of S100B were involved in biological processes of immune response and multiple tumor immune-related signaling pathways in HCC. S100B expression was positively correlated with multiple immune cells tumor infiltration and associated with chemokines/chemokine receptors and immune checkpoint genes. Moreover, S100B is predominantly expressed in immune cells, especially NK (Natural Killer) cell. In addition, the hub genes of S100B co-expression and hypoxia response in HepG2 cell were also associated with immune cells infiltration in HCC. Taken together, these findings provide a new insight into the complex networks between hypoxia response and immune cells infiltration in tumor microenvironment of liver cancer. S100B maybe serve as a novel target for future HCC therapies.

SPINKs in Tumors: Potential Therapeutic Targets

The serine protease inhibitor Kazal type (SPINK) family includes SPINK1-14 and is the largest branch in the serine protease inhibitor family. SPINKs play an important role in pancreatic physiology and disease, sperm maturation and capacitation, Nager syndrome, inflammation and the skin barrier. Evidence shows that the unregulated expression of SPINK1, 2, 4, 5, 6, 7, and 13 is closely related to human tumors. Different SPINKs exhibit various regulatory modes in different tumors and can be used as tumor prognostic markers. This article reviews the role of SPINK1, 2, 4, 5, 6, 7, and 13 in different human cancer processes and helps to identify new cancer treatment targets.

Tumor Mutation Burden-Associated LINC00638/miR-4732-3p/ULBP1 Axis Promotes Immune Escape via PD-L1 in Hepatocellular Carcinoma

Tumor mutation burden (TMB) is associated with immune infiltration, while its underlying mechanism in hepatocellular carcinoma (HCC) remains unclear. A long noncoding RNA (lncRNA)-related competitive endogenous RNA (ceRNA) network can regulate various tumor behaviors, and research about its correlation with TMB and immune infiltration is warranted. Data were downloaded from TCGA and ArrayExpress databases. Cox analysis and machine learning algorithms were employed to establish a lncRNA-based prognostic model for HCC. We then developed a nomogram model to predict overall survival and odds of death for HCC patients. The association of this prognostic model with TMB and immune infiltration was also analyzed. In addition, a ceRNA network was constructed by using DIANA-LncBasev2 and the starBase database and verified by luciferase reporter and colocalization analysis. Multiplex immunofluorescence was applied to determine the correlation between ULBP1 and PD-L1. An eight-lncRNA (SLC25A30-AS1, HPN-AS1, LINC00607, USP2-AS1, HCG20, LINC00638, MKLN1-AS and LINC00652) prognostic score model was constructed for HCC, which was highly associated with TMB and immune infiltration. Next, we constructed a ceRNA network, LINC00638/miR-4732-3p/ULBP1, that may be responsible for NK cell infiltration in HCC with high TMB. However, patients with high ULBP1 possessed a poorer prognosis. Using multiplex immunofluorescence, we found a significant correlation between ULBP1 and PD-L1 in HCC, and patients with high ULBP1 and PD-L1 had the worst prognosis. In brief, the eight-lncRNA model is a reliable tool to predict the prognosis of HCC patients. The LINC00638/miR-4732-3p/ULBP1 axis may regulate immune escape via PD-L1 in HCC with high TMB.

Biomarkers and Future Perspectives for Hepatocellular Carcinoma Immunotherapy

Hepatocellular cancer is the sixth most frequently diagnosed malignant disease worldwide, and was responsible for tens of millions of deaths in 2020; however, treatment options for patients with advanced hepatocellular carcinoma remain limited. Immunotherapy has undergone rapid development over recent years, especially in the field of immune checkpoint inhibitors (ICIs). These drugs aim to activate and enhance antitumor immunity and represent a new prospect for the treatment of patients with advanced cancer. Nevertheless, only a small proportion of liver cancer patients currently benefit from ICI-based treatment, highlighting the need to better understand how ICIs and tumors interact, as well as identify predictive biomarkers for immunotherapeutic responses. In this review, we highlight clinical trials and basic research in hepatocellular carcinoma, with a particular focus on predictive biomarkers for the therapeutic efficacy of ICIs. Predictive biomarkers for immune-related adverse events are also discussed.

Identification of the Real Hub Gene and Construction of a Novel Prognostic Signature for Pancreatic Adenocarcinoma Based on the Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator Algorithms

Background: Pancreatic adenocarcinoma (PAAD) has a considerably bad prognosis, and its pathophysiologic mechanism remains unclear. Thus, we aimed to identify real hub genes to better explore the pathophysiology of PAAD and construct a prognostic panel to better predict the prognosis of PAAD using the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithms. Methods: WGCNA identified the modules most closely related to the PAAD stage and grade based on the Gene Expression Omnibus. The module genes significantly associated with PAAD progression and prognosis were considered as the real hub genes. Eligible genes in the most significant module were selected for construction and validation of a multigene prognostic signature based on the LASSO-Cox regression analysis in The Cancer Genome Atlas and the International Cancer Genome Consortium databases, respectively. Results: The brown module identified by WGCNA was most closely associated with the clinical characteristics of PAAD. Scaffold attachment factor B (SAFB) was significantly associated with PAAD progression and prognosis, and was identified as the real hub gene of PAAD. Moreover, both transcriptional and translational levels of SAFB were significantly lower in PAAD tissues compared with normal pancreatic tissues. In addition, a novel multigene-independent prognostic signature consisting of SAFB, SP1, and SERTAD3 was identified and verified. The predictive accuracy of our signature was superior to that of previous studies, especially for predicting 3- and 5-year survival probabilities. Furthermore, a prognostic nomogram based on independent prognostic variables was developed and validated using calibration curves. The predictive ability of this nomogram was also superior to the well-established AJCC stage and histological grade. The potential mechanisms of different prognoses between the high- and low-risk subgroups were also investigated using functional enrichment analysis, GSEA, ssGSEA, immune checkpoint analysis, and mutation profile analysis. Conclusion: SAFB was identified as the real hub gene of PAAD. A novel multigene-independent prognostic signature was successfully identified and validated to better predict PAAD prognosis. An accurate nomogram was also developed and verified to aid in the accurate treatment of tumors, as well as in early intervention.

Xanthine dehydrogenase as a prognostic biomarker related to tumor immunology in hepatocellular carcinoma

Background

Xanthine dehydrogenase (XDH) is a critical enzyme involved in the oxidative metabolism of purines, pterin and aldehydes and a central component of the innate immune system. However, the prognostic value of XDH in predicting tumor-infiltrating lymphocyte abundance, the immune response, and survival in different cancers, including hepatocellular carcinoma (HCC), is still unclear.

Methods

XDH expression was analyzed in multiple databases, including Oncomine, the Tumor Immune Estimation Resource (TIMER), the Kaplan–Meier plotter database, the Gene Expression Profiling Interactive Analysis (GEPIA) database, and The Cancer Genome Atlas (TCGA). XDH-associated transcriptional profiles were detected with an mRNA array, and the levels of infiltrating immune cells were validated by immunohistochemistry (IHC) of HCC tissues. A predictive signature containing multiple XDH-associated immune genes was established using the Cox regression model.

Results

Decreased XDH mRNA expression was detected in human cancers originating from the liver, bladder, breast, colon, bile duct, kidney, and hematolymphoid system. The prognostic potential of XDH mRNA expression was also significant in certain other cancers, including HCC, breast cancer, kidney or bladder carcinoma, gastric cancer, mesothelioma, lung cancer, and ovarian cancer. In HCC, a low XDH mRNA level predicted poorer overall survival, disease-specific survival, disease-free survival, and progression-free survival. The prognostic value of XDH was independent of the clinical features of HCC patients. Indeed, XDH expression in HCC activated several immune-related pathways, including the T cell receptor, PI3K-AKT, and MAPK signaling pathways, which induced a cytotoxic immune response. Importantly, the microenvironment of XDH^high HCC tumors contained abundant infiltrating CD8 + T cells but not exhausted T cells. A risk prediction signature based on multiple XDH-associated immune genes was revealed as an independent predictor in the TCGA liver cancer cohort.

Conclusion

These findings suggest that XDH is a valuable prognostic biomarker in HCC and other cancers and indicate that it may function in tumor immunology. Loss of XDH expression may be an immune evasion mechanism for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02173-7.

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.