An immunogenomic signature for molecular classification in hepatocellular carcinoma

Molecular mechanism of radiation tolerance in lung adenocarcinoma cells using single-cell RNA sequencing

The efficacy of radiotherapy, a cornerstone in the treatment of lung adenocarcinoma (LUAD), is profoundly undermined by radiotolerance. This resistance not only poses a significant clinical challenge but also compromises patient survival rates. Therefore, it is important to explore this mechanism for the treatment of LUAD. Multiple public databases were used for single-cell RNA sequencing (scRNA-seq) data. We filtered, normalized and downscaled scRNA-seq data based on the Seurat package to obtain different cell subpopulations. Subsequently, the ssGSEA algorithm was used to assess the enrichment scores of the different cell subpopulations, and thus screen the cell subpopulations that are most relevant to radiotherapy tolerance based on the Pearson method. Finally, pseudotime analysis was performed, and a preliminary exploration of gene mutations in different cell subpopulations was performed. We identified HIST1H1D+ A549 and PIF1+ A549 as the cell subpopulations related to radiotolerance. The expression levels of cell cycle-related genes and pathway enrichment scores of these two cell subpopulations increased gradually with the extension of radiation treatment time. Finally, we found that the proportion of TP53 mutations in patients who had received radiotherapy was significantly higher than that in patients who had not received radiotherapy. We identified two cellular subpopulations associated with radiotherapy tolerance, which may shed light on the molecular mechanisms of radiotherapy tolerance in LUAD and provide new clinical perspectives.

Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma

Glucose metabolism, as a novel theory to explain tumor cell behavior, has been intensively studied in various tumors. The present study explored the long non-coding RNAs (lncRNAs) related to glycolysis in grade II-III glioma, aiming to provide a promising target for further research. Pearson correlation analysis was used to identify glycolysis-related lncRNAs. Univariate/multivariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator algorithm were applied to identify glycolysis-related lncRNAs to construct a prognosis prediction model. Subsequently, multi-dimensional evaluations were used to verify whether the risk model could predict the prognosis and survival rate of patients with grade II-III glioma. Finally, it was verified by functional experiments. The present study finally identified seven glycolysis-related lncRNAs (CRNDE, AC022034.1, RHOQ-AS1, AL159169.2, AL133215.2, AC007098.1 and LINC02587) to construct a prognosis prediction model. The present study further investigated the underlying immune microenvironment, somatic landscape and functional enrichment pathways. Additionally, individualized immunotherapeutic strategies and candidate compounds were identified to guide clinical treatment. The experimental results demonstrated that CRNDE could increase the proliferation of SHG-44 cells. In conclusion, a large sample of human grade II-III glioma in The Cancer Genome Atlas database was used to construct a risk model using glycolysis-related lncRNAs to predict the prognosis of patients with grade II-III glioma.

Integrative multi-omics characterization of hepatocellular carcinoma in Hispanic patients

Background

The incidence and mortality rates of hepatocellular carcinoma (HCC) among Hispanics in the United States are much higher than those of non-Hispanic whites. We conducted comprehensive multi-omics analyses to understand molecular alterations in HCC among Hispanic patients.

Methods

Paired tumor and adjacent non-tumor samples were collected from 31 Hispanic HCC in South Texas (STX-Hispanic) for genomic, transcriptomic, proteomic, and metabolomic profiling. Additionally, serum lipids were profiled in 40 Hispanic and non-Hispanic patients with or without clinically diagnosed HCC.

Results

Exome sequencing revealed high mutation frequencies of AXIN2 and CTNNB1 in STX Hispanic HCCs, suggesting a predominant activation of the Wnt/β-catenin pathway. The TERT promoter mutation frequency was also remarkably high in the Hispanic cohort. Cell cycles and liver functions were identified as positively- and negatively-enriched, respectively, with gene set enrichment analysis. Gene sets representing specific liver metabolic pathways were associated with dysregulation of corresponding metabolites. Negative enrichment of liver adipogenesis and lipid metabolism corroborated with a significant reduction in most lipids in the serum samples of HCC patients. Two HCC subtypes from our Hispanic cohort were identified and validated with the TCGA liver cancer cohort. The subtype with better overall survival showed higher activity of immune and angiogenesis signatures, and lower activity of liver function-related gene signatures. It also had higher levels of immune checkpoint and immune exhaustion markers.

Conclusions

Our study revealed some specific molecular features of Hispanic HCC and potential biomarkers for therapeutic management of HCC and provides a unique resource for studying Hispanic HCC.

A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer

BACKGROUND

The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI.

METHODS

Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes.

RESULTS

This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells.

CONCLUSION

We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

Single-cell RNA sequencing explores the evolution of the ecosystem from leukoplakia to head and neck squamous cell carcinoma

It has been found that progression from leukoplakia to head and neck squamous cell carcinoma (HNSCC) is a long-term process that may involve changes in the multicellular ecosystem. We acquired scRNA-seq samples information from gene expression omnibus and UCSC Xena database. The BEAM function was used to construct the pseudotime trajectory and analyze the differentially expressed genes in different branches. We used the ssGSEA method to explore the correlation between each cell subgroup and survival time, and obtained the cell subgroup related to prognosis. During the progression from leukoplakia to HNSCC, we found several prognostic cell subgroups, such as AURKB + epithelial cells, SFRP1 + fibroblasts, SLC7A8 + macrophages, FCER1A + CD1C + dendritic cells, and TRGC2 + NK/T cells. All cell subgroups had two different fates, one tending to cell proliferation, migration, and enhancement of angiogenesis capacity, and the other tending to inflammatory immune response, leukocyte chemotaxis, and T cell activation. Tumor-promoting genes such as CD163 and CD209 were highly expressed in the myeloid cells, and depletion marker genes such as TIGIT, LAG3 were highly expressed in NK/T cells. Our study may provide a reference for the molecular mechanism of HNSCC and theoretical basis for the development of new therapeutic strategies.

Integrative single-cell analysis of LUAD: elucidating immune cell dynamics and prognostic modeling based on exhausted CD8+ T cells

Background

The tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD.

Method

Utilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2.

Results

Our study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD's microenvironment. We underscored pDCs' role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2's function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment.

Conclusion

The creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.

Development and Validation of the novel Cuproptosis- and Immune-related Signature for Predicting Prognosis in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma often results in late-stage diagnosis, leading to decreased treatment success. To improve prognosis, this study integrates cuproptosis with immune risk scoring models for HCC patients. Method: We identified differentially expressed genes connected to cuproptosis and immune responses using Pearson correlation. A risk signature was then constructed via LASSO regression, and its robustness was validated in the International Cancer Genome Consortium dataset. Additionally, qPCR confirmed findings in tumor and normal tissues. Results: Eight genes emerged as key prognostic markers from the 110 differentially expressed genes linked to cuproptosis and immunity. A risk-scoring model was developed using gene expression, effectively categorizing patients into low- or high-risk groups. Validated in the ICGC dataset, high-risk patients had significantly reduced survival times. Multivariate Cox regression affirmed the risk signature's independent predictive capability. A clinical nomogram based on the risk signature was generated. Notably, low-risk patients might benefit more from immune checkpoint inhibitors. qPCR and western blotting results substantiated our bioinformatics findings. Conclusions: The genetic risk signature linked to cuproptosis and immunity holds potential as a vital prognostic biomarker for Hepatocellular carcinoma, providing avenues for tailored therapeutic strategies.

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.

Two lncRNA signatures with cuproptosis as a novel prognostic model and clinicopathological value for endometrioid endometrial adenocarcinoma

OBJECTIVE

Cuproptosis may contribute to tumorigenesis. However, the predictive value and therapeutic significance of cuproptosis-related lncRNAs (CRLs) in endometrioid endometrial adenocarcinoma (EEA) remains unknown.

METHODS

We obtained RNA-seq data from TCGA database and searched the Literature to identify cuproptosis-related genes. Using machine learning models, we identified prognostic lncRNAs for cuproptosis. Immune properties and drug sensitivity were investigated based on these signatures. Further, a ceRNA network was constructed by bioinformatics and in vitro experiments were performed.

RESULTS

We determined two cuproptosis-related signatures to build the prognostic model in EEA. Afterward, the risk scores of two cuproptosis-related signatures were associated with clinicopathological molecular typing and as independent prognostic factors for EEA. In addition, we observed significant differences in immune function, checkpoints, and CD8+ T lymphocyte infiltration between the two risk groups. Furthermore, chemotherapy drugs such as AKT inhibitors exhibited lower IC50 values in the high-risk group. We speculate that ACOXL-AS1 can be served as an endogenous 'sponge' to regulate the expression of MTF1 by miR-421. Through in vitro experiments, we preliminarily validated the ceRNA network relationship in the cellular model.

CONCLUSION

In EEAs, this study proposed a broad molecular signature of CRLs are promising biomarkers for predicting clinical outcomes and therapeutic responses.

WIPF1 promotes gastric cancer progression by regulating PI3K/Akt signaling in a myocardin-dependent manner

Wiskott-Aldrich syndrome protein-interacting protein family member 1 (WIPF1) is associated with malignant tumor progression. However, molecular links between WIPF1 and gastric cancer (GC) remain elusive. The expression of WIPF1 was detected in GC tissues and cells. WIPF1 was overexpressed in GC tissues and cells and high expression of WIPF1 was an independent risk factor for a poor prognosis in patients with GC. Further experiments indicated that WIPF1 promoted the proliferation, invasion, and migration of GC cells in vivo and in vitro. WIPF1-regulated genes were closely related to cell proliferation and migration in GC, and silencing WIPF1 significantly repressed PI3K/AKT signaling pathway activation. WIPF1 was activated by myocardin (MYOCD) translation. Rescue experiments confirmed that MYOCD promotes the proliferation, invasion, and migration of GC cells in a WIPF1-dependent manner and activates the PI3K/AKT signaling pathway. MYOCD may transactivate WIPF1 and facilitate GC cell growth and metastasis by activating the PI3K/AKT signaling pathway.

Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics

BACKGROUND

Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis.

METHODS

We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed.

RESULTS

We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways.

CONCLUSION

We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.

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.

Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy

Background: Uveal melanoma (UVM) is a primary intraocular malignancy that poses a significant threat to patients' visual function and life. The basement membrane (BM) is critical for establishing and maintaining cell polarity, adult function, embryonic and organ morphogenesis, and many other biological processes. Some basement membrane protein genes have been proven to be prognostic biomarkers for various cancers. This research aimed to develop a novel risk assessment system based on BMRGs that would serve as a theoretical foundation for tailored and accurate treatment. Methods: We used gene expression profiles and clinical data from the TCGA-UVM cohort of 80 UVM patients as a training set. 56 UVM patients from the combined cohort of GSE84976 and GSE22138 were employed as an external validation dataset. Prognostic characteristics of basement membrane protein-related genes (BMRGs) were characterized by Lasso, stepwise multifactorial Cox. Multivariate analysis revealed BMRGs to be independent predictors of UVM. The TISCH database probes the crosstalk of BMEGs in the tumor microenvironment at the single-cell level. Finally, we investigated the function of ITGA5 in UVM using multiple experimental techniques, including CCK8, transwell, wound healing assay, and colony formation assay. Results: There are three genes in the prognostic risk model (ADAMTS10, ADAMTS14, and ITGA5). After validation, we determined that the model is quite reliable and accurately forecasts the prognosis of UVM patients. Immunotherapy is more likely to be beneficial for UVM patients in the high-risk group, whereas the survival advantage may be greater for UVM patients in the low-risk group. Knockdown of ITGA5 expression was shown to inhibit the proliferation, migration, and invasive ability of UVM cells in vitro experiments. Conclusion: The 3-BMRGs feature model we constructed has excellent predictive performance which plays a key role in the prognosis, informing the individualized treatment of UVM patients. It also provides a new perspective for assessing pre-immune efficacy.

Prognostic and immunological roles of IL18RAP in human cancers

Across several cancers, IL18 receptor accessory protein (IL18RAP) is abnormally expressed, and this abnormality is related to tumor immunity and heterogeneous clinical outcomes. In this study, based on bioinformatics analysis, we discovered that IL18RAP is related to the human tumor microenvironment and promotes various immune cells infiltration. Additionally, the multiple immunofluorescence staining revealed that with the increased expression of IL18RAP, the number of infiltrated M1 macrophages increased. This finding was confirmed by coculture migration analysis using three human cancer cell lines (MDA-MB-231, U251, and HepG2) with IL18RAP knockdown. We discovered a positive link between IL18RAP and the majority of immunostimulators, immunoinhibitors, major histocompatibility complex (MHC) molecules, chemokines, and chemokine receptor genes using Spearman correlation analysis. Additionally, functional IL18RAP's gene set enrichment analysis (GSEA) revealed that it is related to a variety of immunological processes, such as positive regulation of interferon gamma production and positive regulation of NK cell-mediated immunity. Moreover, we used single-cell RNA sequencing analysis to detect that IL18RAP was mainly expressed in immune cells, and HALLMARK analysis confirmed that the INF-γ gene set expression was upregulated in CD8Tex cells. In addition, in human and mouse cancer cohorts, we found that the level of IL18RAP can predict the immunotherapy response. In short, our study showed that IL18RAP is a new tumor biomarker and may become a potential immunotherapeutic target in cancer.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on NK cell marker genes to predict prognosis and immunotherapy response in hepatocellular carcinoma

BACKGROUND

Prognostic modeling of NK cell marker genes in patients with hepatocellular carcinoma based on single cell sequencing and transcriptome data analysis.

METHODS

Marker genes of NK cells were analyzed according to single cell sequencing data of hepatocellular carcinoma. Univariate Cox regression, lasso regression analysis, and multivariate Cox regression were performed to estimate the prognostic value of NK cell marker genes. TCGA, GEO and ICGC transcriptomic data were applied to build and validate the model. Patients were divided into high and low risk groups based on the median risk score. XCELL, timer, quantitative sequences, MCP counter, EPIC, CIBERSORT and CIBERSORT-abs were performed to explore the relationship between risk score and tumor microenvironment in hepatocellular carcinoma. Finally the sensitivity of the model to chemotherapeutic agents was predicted.

RESULTS

Single-cell sequencing identified 207 marker genes for NK cells in hepatocellular carcinoma. Enrichment analysis suggested that NK cell marker genes were mainly involved in cellular immune function. Eight genes were selected for prognostic modeling after multifactorial COX regression analysis. The model was validated in GEO and ICGC data. Immune cell infiltration and function were higher in the low-risk group than in the high-risk group. The low-risk group was more suitable for ICI and PD-1 therapy. Half-maximal inhibitory concentrations of Sorafenib, Lapatinib, Dabrafenib, and Axitinib were significantly different on the two risk groups.

CONCLUSION

A new signature of hepatocyte NK cell marker genes possesses a powerful ability to predict prognosis and immunotherapeutic response in patients with hepatocellular carcinoma.

Prognosis and therapeutic benefits prediction based on NK cell marker genes through single-cell RNA-seq with integrated bulk RNA-seq analysis for hepatocellular carcinoma

Tumor-infiltrating immune cells greatly participate in regulating tumorigenesis and metastasis of hepatocellular carcinoma (HCC). Natural killer cell, as an important role of innate immunity, plays an indispensable role in antitumor immunity and regulate tumor development. In this study, we firstly identified 251 NK cell marker genes of HCC based on single-cell RNA sequencing data. Subsequently, an NK cell marker genes-related prognostic signature (NKPS) was developed in the cancer genome atlas (TCGA) cohort for risk stratification and prognosis prediction. The predictive value of the NKPS in prognosis was well validated in different clinical subgroups and three external datasets (ICGC-LIHC cohort, GSE14520 cohort and Guilin cohort). Moreover, multivariate analysis revealed the independent prognostic value of NKPS for OS in HCC. Further functional analysis indicated the NKPS was associated with basic cellular processes, that may contribute to the development and progression of HCC. Thereafter, immune characteristics as well as the therapeutic benefits in NKPS risk score-defined subgroups were analyzed. Patients with low-risk score exhibited immune-active status, manifested as higher immune scores, more infiltration of CD8+ T cells and macrophage M1, and higher T-cell receptor (TCR) richness and diversity. Remarkably, the NKPS was negatively correlated with immunotherapy response-related signatures. In addition, the low-risk group exhibited significantly improved therapeutic benefits, either from immunotherapy or traditional chemotherapy and target therapy. Overall, the NKPS showed an excellent predictive value for prognosis and therapeutic responses for HCC, which might also provide novel insights into better HCC management strategies.

A Novel Prognostic Signature of comprising Nine NK Cell signatures Based on Both Bulk RNA Sequencing and Single-Cell RNA Sequencing for Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) has limited prognostic prediction due to its heterogeneity. Understanding the role of natural killer (NK) cells in HCC is vital for prognosis and immunotherapy guidance. We aimed to identify NK cell marker genes through scRNA-seq and develop a prognostic signature for HCC. Methods: We analyzed scRNA-seq data (GSE149614) from 10 patients and bulk RNA-seq data from 786 patients with clinicopathological information. NK cell marker genes were identified using clustering and marker finding functions. A predictive risk signature was constructed using LASSO-COX algorithm. Functional annotations and immune cell infiltration analysis were performed, and the nomogram's performance was evaluated. Results: We identified 79 NK cell marker genes associated with NK cell-mediated cytotoxicity, apoptosis, and immune response. The multigene signature significantly correlated with overall survival (OS) in TCGA-LIHC cohort and was validated in other cohorts. Low-risk patients exhibited higher immune cell infiltration, including CD8+ T cells. The risk signature was an independent prognostic factor for OS (HR > 1, p < 0.001). The nomogram combining the risk signature and clinical predictors demonstrated robust prognostic ability. Conclusion: We developed a nine-gene signature prognostic model based on NK cell marker genes to accurately assess the prognostic risk of HCC. This model can be a valuable tool for personalized evaluation post-surgery. Our study underscores the potential of NK cells in HCC prognosis and highlights the importance of scRNA-seq analysis in identifying prognostic markers.

Construction of molecular typing in LIHC microenvironment based on lipid metabolism-related genes

Consensus on the stage of liver hepatocellular carcinoma (LIHC) in patients is difficult, which restricts the diagnosis and treatment of liver cancer. Molecular typing based on genes related to the lipid metabolism pathways can reflect deeper characteristics of liver cancer and complement the deficiency of the clinical staging system. In this study, we constructed and verified two cell subtypes: C1 and C2 in LIHC, based on six lipid metabolic pathway-associated genes identified in two independent external validation cohorts comprising single-cell RNA-sequencing technology (scRNA-Seq) data and bulk RNA-seq data downloaded from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The C2 subtype showed poorer prognosis, higher immune scores, and greater correlation with pathways associated with tumor progression as compared to the C1 subtype. Moreover, the sensitivity of many tested targeted drugs in C1 was relative to C2. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed several significantly enriched oncological signatures and metabolic processes, which might help elucidate the underlying molecular mechanisms. At the same time, we identified there were significantly different metabolites in C1 and C2 subtypes using 11 LIHC tissue samples. In conclusion, we constructed two molecular subtypes based on the lipid metabolism-associated genes, which may provide valuable information to further study the pathogenesis and devise clinical management strategies for LIHC.

A novel prognostic model for hepatocellular carcinoma based on pyruvate metabolism-related genes

Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer, accounting for over 90% of cases. As pyruvate metabolic pathways are often dysregulated in cancer cells, investigating pyruvate metabolism-related genes may help identify prognostic gene signature and develop potential strategies for the management of patients with HCC. The mRNA expression profile, gene mutation data, and clinical information of HCC were obtained from open-source databases. A list of pyruvate metabolism-related genes was downloaded from the MSigDB dataset. Our findings revealed that certain pyruvate metabolism-related genes had copy number variations and single nucleotide variations in patients with liver cancer. Based on pyruvate metabolism-related genes, we stratified patients with HCC into three subtypes with different prognoses, clinical features, mutation profiles, functional annotation, and immune infiltration status. Next, we identified 13 key pyruvate metabolism-related genes significantly correlated with the prognosis of HCC using six machine learning algorithms and constructed a risk model. We also observed that the risk score was positively associated with a worse prognosis and increased immune infiltration. In summary, our study established a prognostic risk model for HCC based on pyruvate metabolism-related genes, which may contribute to the identification of potential prognostic targets and the development of new clinical management strategies for HCC.

Identifying a novel cuproptosis-related necroptosis gene subtype-related signature for predicting the prognosis, tumor microenvironment, and immunotherapy of hepatocellular carcinoma

Background: Cuproptosis and necroptosis represent two distinct programmed cell death modalities implicated in neoplastic progression; however, the role of combining cuproptosis and necroptosis in hepatocellular carcinoma (HCC) remains to be elucidated. Methods: A total of 29 cuproptosis-related necroptosis genes (CRNGs) were identified, followed by an extensive analysis of their mutational characteristics, expression patterns, prognostic implications, and associations with the tumor microenvironment (TME). Subsequently, a CRNG subtype-related signature was developed, and its value of prognostic prediction, TME, and therapeutic responses in HCC were thoroughly investigated. Last, quantitative real-time PCR and Western blotting were employed for investigating the signature gene expression in 15 paired clinical tissue samples. Results: Two distinct CRNG subtypes were discerned, demonstrating associations between CRNG expression patterns, clinicopathological attributes, prognosis, and the TME. A CRNG subtype-related prognostic signature, subjected to external validation, was constructed, serving as an independent prognostic factor for HCC patients, indicating poor prognosis for high-risk individuals. Concurrently, the signature's correlations with an immune-suppressive TME, mutational features, stemness properties, immune checkpoint genes, chemoresistance-associated genes, and drug sensitivity were observed, signifying its utility in predicting treatment responses. Subsequently, highly accurate and clinically convenient nomograms were developed, and the signature genes were validated via quantitative real-time PCR and Western blotting, further substantiating the stability and dependability of the CRNG subtype-related prognostic signature. Conclusion: Overall, this investigation presented an extensive panorama of CRNGs and developed the CRNG subtype-related prognostic signature, which holds potential for implementation in personalized treatment strategies and prognostic forecasting for HCC patients.

Application of single-cell RNA sequencing analysis of novel breast cancer phenotypes based on the activation of ferroptosis-related genes

Ferroptosis is distinct from classic apoptotic cell death characterized by the accumulation of reactive oxygen species (ROS) and lipid peroxides on the cell membrane. Increasing findings have demonstrated that ferroptosis plays an important role in cancer development, but the exploration of ferroptosis in breast cancer is limited. In our study, we aimed to establish a ferroptosis activation-related model based on the differentially expressed genes between a group exhibiting high ferroptosis activation and a group exhibiting low ferroptosis activation. By using machine learning to establish the model, we verified the accuracy and efficiency of our model in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) set and gene expression omnibus (GEO) dataset. Additionally, our research innovatively utilized single-cell RNA sequencing data to systematically reveal the microenvironment in the high and low FeAS groups, which demonstrated differences between the two groups from comprehensive aspects, including the activation condition of transcription factors, cell pseudotime features, cell communication, immune infiltration, chemotherapy efficiency, and potential drug resistance. In conclusion, different ferroptosis activation levels play a vital role in influencing the outcome of breast cancer patients and altering the tumor microenvironment in different molecular aspects. By analyzing differences in ferroptosis activation levels, our risk model is characterized by a good prognostic capacity in assessing the outcome of breast cancer patients, and the risk score can be used to prompt clinical treatment to prevent potential drug resistance. By identifying the different tumor microenvironment landscapes between the high- and low-risk groups, our risk model provides molecular insight into ferroptosis in breast cancer patients.

A comprehensive analysis of the hub genes for oxidative stress in ischemic stroke

Ischemic stroke (IS), resulting from the occlusion of the cerebral artery and subsequent interruption of blood flow, represents a major and critical threat to public health. Oxidative stress (OS) has been confirmed to play a role in the IS pathological process and neural death. Understanding the essential role of OS-related genes in ischemic stroke is critical to understanding the current perception of the pathophysiological process in IS. Herein, by integrating three IS datasets (GSE16561, GSE22255, and GSE58294), we divided IS samples into the low- and high-OS groups by calculating the OS score identified by the oxidative stress gene set. The functional enrichment analysis of differentially expressed genes (DEGs) between the low- and high-OS groups indicated that DEGs were associated with hypoxia, the inflammatory response, and oxidative phosphorylation pathways. Furthermore, nine hub genes (namely TLR1, CXCL1, MMP9, TLR4, IL1R2, EGR1, FOS, CXCL10, and DUSP1) were identified through the Girvan-Newman algorithm and cytoHubba algorithms. Nine hub genes were highly expressed in IS samples and positively related to neutrophils and macrophages. Drug-sensitive analysis targeting hub genes defined allopurinol and nickel sulfate as potential candidates for impairing the neural death caused by oxidative stress in IS. Finally, we employed five machine learning methods to check the efficacy of the predictive model identified by nine hub genes. The results showed that our model had superior power for predicting the OS activity of IS patients. TLR4 was found to have excellent diagnostic value and a wide-spectrum interaction with other hub genes. Our research emphasized the impact of oxidative stress on ischemic stroke, which supports the idea that antioxidants hold great promise in ischemic stroke therapy.

Deciphering the immune heterogeneity dominated by natural killer cells with prognostic and therapeutic implications in hepatocellular carcinoma

Belonging to type 1 innate lymphoid cells (ILC1), natural killer (NK) cells play an important role not only in fighting microbial infections but also in anti-tumor response. Hepatocellular carcinoma (HCC) represents an inflammation-related malignancy and NK cells are enriched in the liver, making them an essential component of the HCC immune microenvironment. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis to identify the NK cell marker genes (NKGs) and uncovered 80 prognosis-related ones by the TCGA-LIHC dataset. Based on prognostic NKGs, HCC patients were categorized into two subtypes with distinct clinical outcomes. Subsequently, we conducted LASSO-COX and stepwise regression analysis on prognostic NKGs to establish a five-gene (UBB, CIRBP, GZMH, NUDC, and NCL) prognostic signature-NKscore. Different mutation statuses of the two risk groups stratified by NKscore were comprehensively characterized. Besides, the established NKscore-integrated nomogram presented enhanced predictive performance. Single sample gene set enrichment analysis (ssGSEA) analysis was used to uncover the landscape of the tumor immune microenvironment (TIME) and the high-NKscore risk group was characterized with an immune-exhausted phenotype while the low-NKscore risk group held relatively strong anti-cancer immunity. T cell receptor (TCR) repertoire, tumor inflammation signature (TIS), and Immunophenoscore (IPS) analyses revealed differences in immunotherapy sensitivity between the two NKscore risk groups. Taken together, we developed a novel NK cell-related signature to predict the prognosis and immunotherapy efficacy for HCC patients.

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

Background

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

Method

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

Results

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

Conclusions

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

Roles of IL-7R Induced by Interactions between Cancer Cells and Macrophages in the Progression of Esophageal Squamous Cell Carcinoma

High infiltration of tumor-associated macrophages (TAMs), which contribute to the progression of several cancer types, is correlated with poor prognosis of esophageal squamous cell carcinoma (ESCC). In addition to the previously reported increase in migration and invasion, ESCC cells co-cultured directly with macrophages exhibited enhanced survival and growth. Furthermore, interleukin-related molecules are associated with ESCC; however, the precise mechanism underlying this association is unclear. Therefore, we explored the role of interleukin-related molecules in ESCC progression. A cDNA microarray analysis of monocultured and co-cultured ESCC cells revealed that the interleukin 7 receptor (IL-7R) was upregulated in ESCC cells co-cultured with macrophages. Overexpression of IL-7R promoted the survival and growth of ESCC cells by activating the Akt and Erk1/2 signaling pathways. The IL-7/IL-7R axis also contributed to the promotion of ESCC cell migration via the Akt and Erk1/2 signaling pathways. Furthermore, immunohistochemistry showed that ESCC patients with high IL-7R expression in cancer nests exhibited a trend toward poor prognosis in terms of disease-free survival, and showed significant correlation with increased numbers of infiltrating macrophages and cancer-associated fibroblasts. Therefore, IL-7R, which is upregulated when directly co-cultured with macrophages, may contribute to ESCC progression by promoting the development of various malignant phenotypes in cancer cells.

Biological impact and therapeutic perspective of targeting PI3K/Akt signaling in hepatocellular carcinoma: Promises and Challenges

Cancer progression results from activation of various signaling networks. Among these, PI3K/Akt signaling contributes to proliferation, invasion, and inhibition of apoptosis. Hepatocellular carcinoma (HCC) is a primary liver cancer with high incidence rate, especially in regions with high prevalence of viral hepatitis infection. Autoimmune disorders, diabetes mellitus, obesity, alcohol consumption, and inflammation can also lead to initiation and development of HCC. The treatment of HCC depends on the identification of oncogenic factors that lead tumor cells to develop resistance to therapy. The present review article focuses on the role of PI3K/Akt signaling in HCC progression. Activation of PI3K/Akt signaling promotes glucose uptake, favors glycolysis and increases tumor cell proliferation. It inhibits both apoptosis and autophagy while promoting HCC cell survival. PI3K/Akt stimulates epithelial-to-mesenchymal transition (EMT) and increases matrix-metalloproteinase (MMP) expression during HCC metastasis. In addition to increasing colony formation capacity and facilitating the spread of tumor cells, PI3K/Akt signaling stimulates angiogenesis. Therefore, silencing PI3K/Akt signaling prevents aggressive HCC cell behavior. Activation of PI3K/Akt signaling can confer drug resistance, particularly to sorafenib, and decreases the radio-sensitivity of HCC cells. Anti-cancer agents, like phytochemicals and small molecules can suppress PI3K/Akt signaling by limiting HCC progression. Being upregulated in tumor tissues and clinical samples, PI3K/Akt can also be used as a biomarker to predict patients' response to therapy.

Prognostic prediction and immune infiltration analysis based on ferroptosis and EMT state in hepatocellular carcinoma

Background

Ferroptosis is one of the main mechanisms of sorafenib against hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) plays an important role in the heterogeneity, tumor metastasis, immunosuppressive microenvironment, and drug resistance of HCC. However, there are few studies looking into the relationship between ferroptosis and EMT and how they may affect the prognosis of HCC collectively.

Methods

We downloaded gene expression and clinical data of HCC patients from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases for prognostic model construction and validation respectively. The Least absolute shrinkage and selection operator (LASSO) Cox regression was used for model construction. The predictive ability of the model was assessed by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve. We performed the expression profiles analysis to evaluate the ferroptosis and EMT state. CIBERSORT and single-sample Gene Set Enrichment Analysis (ssGSEA) methods were used for immune infiltration analysis.

Results

A total of thirteen crucial genes were identified for ferroptosis-related and EMT-related prognostic model (FEPM) stratifying patients into two risk groups. The high-FEPM group had shorter overall survivals than the low-FEPM group (p<0.0001 in the TCGA cohort and p<0.05 in the ICGC cohort). The FEPM score was proved to be an independent prognostic risk factor (HR>1, p<0.01). Furthermore, the expression profiles analysis suggested that the high-FEPM group appeared to have a more suppressive ferroptosis status and a more active EMT status than the low- FEPM group. Immune infiltration analysis showed that the myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs) were highly enriched in the high-FEPM group. Finally, a nomogram enrolling FEPM score and TNM stage was constructed showing outstanding predictive capacity for the prognosis of patients in the two cohorts.

Conclusion

In conclusion, we developed a ferroptosis-related and EMT-related prognostic model, which could help predict overall survival for HCC patients. It might provide a new idea for predicting the response to targeted therapies and immunotherapies in HCC patients.

Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine

Background: Although immune microenvironment-related chemokines, extracellular matrix (ECM), and intrahepatic immune cells are reported to be highly involved in hepatitis B virus (HBV)-related diseases, their roles in diagnosis, prognosis, and drug sensitivity evaluation remain unclear. Here, we aimed to study their clinical use to provide a basis for precision medicine in hepatocellular carcinoma (HCC) via the amalgamation of artificial intelligence. Methods: High-throughput liver transcriptomes from Gene Expression Omnibus (GEO), NODE (https://www.bio.sino.org/node), the Cancer Genome Atlas (TCGA), and our in-house hepatocellular carcinoma patients were collected in this study. Core immunosignals that participated in the entire diseases course of hepatitis B were explored using the "Gene set variation analysis" R package. Using ROC curve analysis, the impact of core immunosignals and amino acid utilization related gene on hepatocellular carcinoma patient's clinical outcome were calculated. The utility of core immunosignals as a classifier for hepatocellular carcinoma tumor tissue was evaluated using explainable machine-learning methods. A novel deep residual neural network model based on immunosignals was constructed for the long-term overall survival (LS) analysis. In vivo drug sensitivity was calculated by the "oncoPredict" R package. Results: We identified nine genes comprising chemokines and ECM related to hepatitis B virus-induced inflammation and fibrosis as CLST signals. Moreover, CLST was co-enriched with activated CD4+ T cells bearing harmful factors (aCD4) during all stages of hepatitis B virus pathogenesis, which was also verified by our hepatocellular carcinoma data. Unexpectedly, we found that hepatitis B virus-hepatocellular carcinoma patients in the CLST^highaCD4^high subgroup had the shortest overall survival (OS) and were characterized by a risk gene signature associated with amino acids utilization. Importantly, characteristic genes specific to CLST/aCD4 showed promising clinical relevance in identifying patients with early-stage hepatocellular carcinoma via explainable machine learning. In addition, the 5-year long-term overall survival of hepatocellular carcinoma patients can be effectively classified by CLST/aCD4 based GeneSet-ResNet model. Subgroups defined by CLST and aCD4 were significantly involved in the sensitivity of hepatitis B virus-hepatocellular carcinoma patients to chemotherapy treatments. Conclusion: CLST and aCD4 are hepatitis B virus pathogenesis-relevant immunosignals that are highly involved in hepatitis B virus-induced inflammation, fibrosis, and hepatocellular carcinoma. Gene set variation analysis derived immunogenomic signatures enabled efficient diagnostic and prognostic model construction. The clinical application of CLST and aCD4 as indicators would be beneficial for the precision management of hepatocellular carcinoma.

Plasma cell subtypes analyzed using artificial intelligence algorithm for predicting biochemical recurrence, immune escape potential, and immunotherapy response of prostate cancer

Background

Plasma cells as an important component of immune microenvironment plays a crucial role in immune escape and are closely related to immune therapy response. However, its role for prostate cancer is rarely understood. In this study, we intend to investigate the value of a new plasma cell molecular subtype for predicting the biochemical recurrence, immune escape and immunotherapy response in prostate cancer.

Methods

Gene expression and clinicopathological data were collected from 481 prostate cancer patients in the Cancer Genome Atlas. Then, the immune characteristics of the patients were analyzed based on plasma cell infiltration fractions. The unsupervised clustering based machine learning algorithm was used to identify the molecular subtypes of the plasma cell. And the characteristic genes of plasma cell subtypes were screened out by three types of machine learning models to establish an artificial neural network for predicting plasma cell subtypes. Finally, the prediction artificial neural network of plasma cell infiltration subtypes was validated in an independent cohort of 449 prostate cancer patients from the Gene Expression Omnibus.

Results

The plasma cell fraction in prostate cancer was significantly decreased in tumors with high T stage, high Gleason score and lymph node metastasis. In addition, low plasma cell fraction patients had a higher risk of biochemical recurrence. Based on the differential genes of plasma cells, plasma cell infiltration status of PCa patients were divided into two independent molecular subtypes(subtype 1 and subtype 2). Subtype 1 tends to be immunosuppressive plasma cells infiltrating to the PCa region, with a higher likelihood of biochemical recurrence, more active immune microenvironment, and stronger immune escape potential, leading to a poor response to immunotherapy. Subsequently, 10 characteristic genes of plasma cell subtype were screened out by three machine learning algorithms. Finally, an artificial neural network was constructed by those 10 genes to predict the plasma cell subtype of new patients. This artificial neural network was validated in an independent validation set, and the similar results were gained.

Conclusions

Plasma cell infiltration subtypes could provide a potent prognostic predictor for prostate cancer and be an option for potential responders to prostate cancer immunotherapy.

Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine

Background

The N7-methylguanosine modification (m7G) of the 5' cap structure in the mRNA plays a crucial role in gene expression. However, the relation between m7G and tumor immune remains unclear. Hence, we intended to perform a pan-cancer analysis of m7G which can help explore the underlying mechanism and contribute to predictive, preventive, and personalized medicine (PPPM / 3PM).

Methods

The gene expression, genetic variation, clinical information, methylation, and digital pathological section from 33 cancer types were downloaded from the TCGA database. Immunohistochemistry (IHC) was used to validate the expression of the m7G regulator genes (m7RGs) hub-gene. The m7G score was calculated by single-sample gene-set enrichment analysis. The association of m7RGs with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. CellProfiler was used to extract pathological section characteristics. XGBoost and random forest were used to construct the m7G score prediction model. Single-cell transcriptome sequencing (scRNA-seq) was used to assess the activation state of the m7G in the tumor microenvironment.

Results

The m7RGs were highly expressed in tumors and most of the m7RGs are risk factors for prognosis. Moreover, the cellular pathway enrichment analysis suggested that m7G score was closely associated with invasion, cell cycle, DNA damage, and repair. In several cancers, m7G score was significantly negatively correlated with MSI and TMB and positively correlated with TIDE, suggesting an ICB marker potential. XGBoost-based pathomics model accurately predicts m7G scores with an area under the ROC curve (AUC) of 0.97. Analysis of scRNA-seq suggests that m7G differs significantly among cells of the tumor microenvironment. IHC confirmed high expression of EIF4E in breast cancer. The m7G prognostic model can accurately assess the prognosis of tumor patients with an AUC of 0.81, which was publicly hosted at https://pan-cancer-m7g.shinyapps.io/Panca-m7g/.

Conclusion

The current study explored for the first time the m7G in pan-cancer and identified m7G as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy, with the potential for deeper integration with PPPM. Combining m7G within the framework of PPPM will provide a unique opportunity for clinical intelligence and new approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00305-1.

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.

Comprehensive characterisation of immunogenic cell death in melanoma revealing the association with prognosis and tumor immune microenvironment

Increasing evidence has highlighted the critical functions of immunogenic cell death (ICD) within many tumors. However, the therapeutic possibilities and mechanism of utilizing ICD in melanoma are still not well investigated. Melanoma samples involved in our study were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. First, pan-cancer analysis of ICD systematically revealed its expression characteristics, prognostic values, mutation information, methylation level, pathway regulation relationship in multiple human cancers. The non-negative matrix factorization clustering was utilized to separate the TCGA-melanoma samples into two subtypes (i.e. C1 and C2) with different prognosis and immune microenvironment based on the expression traits of ICD. Then, LASSO-Cox regression analysis was utilized to determine an ICD-dependent risk signature (ICDRS) based on the differentially expressed genes (DEGs) between the two subtypes. Principal component analysis and t-distributed stochastic neighbor embedding analysis of ICDRS showed that high- and low-risk subpopulations could be clearly distinguished. Survival analysis and ROC curves in the training, internal validation, and external validation cohorts highlighted the accurate prognosis evaluation of ICDRS. The obvious discrepancies of immune microenvironment between the different risk populations might be responsible for the different prognoses of patients with melanoma. These findings revealed the close association of ICD with prognosis and tumor immune microenvironment. More importantly, ICDRS-based immunotherapy response and targeted drug prediction might be beneficial to different risk subpopulations of patients with melanoma. The innotative ICDRS could function as a marker to determine the prognosis and tumor immune microenvironment in melanoma. This will aid in patient classification for individualized melanoma treatment.

DNA Damage Repair Classifier Defines Distinct Groups in Hepatocellular Carcinoma

Simple Summary

DNA repair pathways have been implicated in hepatocellular carcinoma outcomes. We found that hepatocellular carcinomas (HCC) could be separated into two groups (high and low) based on the overall expression of genes involved in DNA repair. Among the low repair group, there were three subgroups, one of which shared features of the high repair group. Given the important role of liver in metabolism and detoxification and its regenerative capacity, proliferation and DNA damage responses are critical in subdividing major biological categories of liver tumors. High repair samples showed more proliferative and regenerative signatures and had poorer outcomes versus the low repair that were more associated with the genes involved in normal liver biology. These biological groups suggest that dysregulation in endogenous liver processes promotes a pro-tumorigenic microenvironment that may facilitate tumor progression or identify tumors that require more substantial clinical intervention.

Abstract

DNA repair pathways have been associated with variability in hepatocellular carcinoma (HCC) clinical outcomes, but the mechanism through which DNA repair varies as a function of liver regeneration and other HCC characteristics is poorly understood. We curated a panel of 199 genes representing 15 DNA repair pathways to identify DNA repair expression classes and evaluate their associations with liver features and clinicopathologic variables in The Cancer Genome Atlas (TCGA) HCC study. We identified two groups in HCC, defined by low or high expression across all DNA repair pathways. The low-repair group had lower grade and retained the expression of classical liver markers, whereas the high-repair group had more clinically aggressive features, increased p53 mutant-like gene expression, and high liver regenerative gene expression. These pronounced features overshadowed the variation in the low-repair subset, but when considered separately, the low-repair samples included three subgroups: L1, L2, and L3. L3 had high DNA repair expression with worse progression-free (HR 1.24, 95% CI 0.81–1.91) and overall (HR 1.63, 95% CI 0.98–2.71) survival. High-repair outcomes were also significantly worse compared with the L1 and L2 groups. HCCs vary in DNA repair expression, and a subset of tumors with high regeneration profoundly disrupts liver biology and poor prognosis.

Identification of Prognostic Genes and Immune Landscape Signatures Based on Tumor Microenvironment in Lung Adenocarcinoma

Background

Lung adenocarcinoma is the most common lung cancer subtype and accounts for the highest proportion of cancer-related deaths. The tumor microenvironment influences prognostic outcomes in lung adenocarcinoma (LUAD).

Materials and Methods

We used the ESTIMATE algorithm (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) to investigate the role of microenvironment-related genes and stromal cells in lung adenocarcinoma prognosis. This analysis was done on lung adenocarcinoma cases from The Cancer Genome Atlas (TCGA). The cases were divided into high and low groups on the basis of immune and stromal scores, respectively.

Results

There were close correlations between immune scores with prognosis and disease stage. There were 367 differentially expressed genes. Combining the Gene Expression Omnibus (GEO) database, we found 14 prognosis-related genes.

Results

There were close correlations between immune scores with prognosis and disease stage. There were 367 differentially expressed genes. Combining the Gene Expression Omnibus (GEO) database, we found 14 prognosis-related genes. Results. Based on the enrichment levels of the immune cell types, we clustered LUAD into Immunity_H and Immunity_L subtypes. Most of these genes were upregulated in Immunity_H subtype. Finally, using the Human Protein Atlas (HPA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, most of the proteins corresponding to prognostic genes were verified to be differentially expressed between the tumor and normal groups.

Conclusions

The key genes identified in this study are involved in molecular mechanisms of LUAD.

A novel risk model based on immune response predicts clinical outcomes and characterizes immunophenotypes in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is highly heterogeneous in prognosis. The current TNM staging system shows its limitation in accurate risk evaluation. Immune response and immune cell abundances in the tumor immune microenvironment (TIME) are critical for cancer progression, clinical outcome and therapeutic response in TNBC. However, there is a lack of an effective risk model based on the overall transcriptional alterations relevant to different immune responses. In this study, multiple bioinformatics and statistical approaches were used to develop an immune-related risk (IRR) signature based on the differentially expressed genes between the immune-active and immune-inactive samples. The IRR model showed great performance in risk stratification, immune landscape evaluation and immunotherapy response prediction. Compared with the low-IRR group, the high-IRR group exhibited a poorer prognosis, less cytotoxic cell infiltration, higher M2/M1 ratio and upregulated glycolytic activity. Moreover, the high-IRR group showed more resistance to immunotherapy than the low-IRR group. Our study reveals that the IRR model may be a promising tool to help clinicians assess risk and optimize treatment for TNBC patients.