The combined signatures of hypoxia and cellular landscape provides a prognostic and therapeutic biomarker in hepatitis B virus-related hepatocellular carcinoma

Spatial multi-omics reveals the potential involvement of SPP1+ fibroblasts in determining metabolic heterogeneity and promoting metastatic growth of colorectal cancer liver metastasis

This study investigates key microscopic regions involved in colorectal cancer liver metastasis (CRLM), focusing on the crucial role of cancer-associated fibroblasts (CAFs) in promoting tumor progression and providing molecular- and metabolism-level insights for its diagnosis and treatment using multi-omics. We followed 12 fresh surgical samples from 2 untreated CRLM patients. Among these, 4 samples were used for spatial transcriptomics (ST), 4 for spatial metabolomics, and 4 for single-cell RNA sequencing (scRNA-seq). Additionally, 92 frozen tissue samples from 40 patients were collected. Seven patients were used for immunofluorescence and RT-qPCR, while 33 patients were used for untargeted metabolomics. ST revealed that the spatial regions of CRLM consists of 7 major components, with fibroblast-dominated regions being the most prominent. These regions are characterized by diverse cell-cell interactions, and immunosuppressive and tumor growth-promoting environments. scRNA-seq identified that SPP1+ fibroblasts interact with CD44+ tumor cells, as confirmed through immunofluorescence. Spatial metabolomics revealed suberic acid and tetraethylene glycol as specific metabolic components of this structure, which was further validated by untargeted metabolomics. In conclusion, an SPP1+ fibroblast-rich spatial region with metabolic reprogramming capabilities and immunosuppressive properties was identified in CRLM, which potentially facilitates metastatic outgrowth through interactions with tumor cells.

Unveiling disulfidptosis-related genes in HBV-associated hepatocellular carcinoma: an integrated study incorporating transcriptome and Mendelian randomization analyses

Disulfidptosis, a recently unveiled mechanism of demise, has been linked to an unfavorable prognosis in the context of hepatocellular carcinoma (HCC). However, few studies have focused on the causal link between disulfidptosis and HBV-related HCC (HBV-HCC). In this study, the Mendelian randomization (MR) analysis demonstrated that the risk of HCC increased with increasing genetic susceptibility to HBV, and the genetic changes of disulfidptosis were significantly associated with the increased risk of HBV-HCC. Within both the TCGA and GEO cohorts, it is possible to accurately forecast the prognosis of HBV-HCC by utilizing a risk score that is derived from a combination of GYS1, RPN1, SLC7A11, LRPPRC and CAPZB genes. GYS1, a potential therapeutic target for HBV-HCC, exhibits a remarkable positive correlation with immune infiltration and MSI when compared to other molecules. Furthermore, we demonstrated that silencing GYS1 effectively inhibits the tumor proliferation and metastasis of HBV-HCC in vitro and in vivo. Overall, this study expands the understanding of the potential roles of disulfidptosis in HBV-HCC and highlights GYS1 as a promising target for HBV-HCC.

Liver metastases across cancer types sharing tumor environment immunotolerance can impede immune response therapy and immune monitoring

BACKGROUND

Hepatic immune tolerance might contribute to the development of therapeutic resistance to immunotherapy. However, addressing this issue is challenging since the efficacy of immunotherapy in the context of liver metastasis (LM) remains poorly studied. Here, we aimed to establish an LM common immune feature (LMCIF) score to quantify the characteristics of LM immunotolerance across cancer types for assisting clinical disease management.

METHODS

Large-scale clinical data were collected to identify the prognosis of LM. Multi-omics datasets of metastatic cancers with LM special immune-related pathways (LMSIPs) from the Molecular Signatures Database (MSigDB)were used to obtain an LMCIF cluster. Based on differential expression genes (DEGs), a novel LMCIF score for the LMCIF cluster was constructed. In addition, multi-omics, and immunohistochemistry (IHC) data from the public and in-house cohorts were used to explore the features of LM, and LMCIF score.

RESULTS

Patients with LM had a worse prognosis and significantly lower infiltration of immune cells than patients with metastasis to other organs when analyzed with combined clinical and RNA sequencing data. After extracting the LMCIF cluster from 373 samples by utilizing 29 LMSIPs and validating them in a microarray cohort, an LMCIF score was established to confirm the role of the immunosuppressive environment as a contributor to the poor prognosis of LM across cancer types. Moreover, this LMCIF score could be used to predict the immune response of cancer patients undergoing immunotherapy. Finally, we identified that the majority of the 31 LMCIF genes exhibited a negative correlation with TME cells in LM patients, one of them, KRT19, which possessed the strongest positive correlation with LMCIF score, was confirmed to have an immunosuppressive effect through IHC analysis.

CONCLUSIONS

Our results suggest that LM across cancer types share similar immunological profiles that induce immunotolerance and escape from immune monitoring. The novel LMCIF score represents a common liver metastasis immune cluster for predicting immunotherapy response, the results of which might benefit clinical disease management.

Harnessing CD8+ T cell dynamics in hepatitis B virus-associated liver diseases: Insights, therapies and future directions

Hepatitis B virus (HBV) infection playsa significant role in the etiology and progression of liver-relatedpathologies, encompassing chronic hepatitis, fibrosis, cirrhosis, and eventual hepatocellularcarcinoma (HCC). Notably, HBV infection stands as the primary etiologicalfactor driving the development of HCC. Given the significant contribution ofHBV infection to liver diseases, a comprehensive understanding of immunedynamics in the liver microenvironment, spanning chronic HBV infection,fibrosis, cirrhosis, and HCC, is essential. In this review, we focused on thefunctional alterations of CD8+ T cells within the pathogenic livermicroenvironment from HBV infection to HCC. We thoroughly reviewed the roles ofhypoxia, acidic pH, metabolic reprogramming, amino acid deficiency, inhibitory checkpointmolecules, immunosuppressive cytokines, and the gut-liver communication in shapingthe dysfunction of CD8+ T cells in the liver microenvironment. Thesefactors significantly impact the clinical prognosis. Furthermore, we comprehensivelyreviewed CD8+ T cell-based therapy strategies for liver diseases,encompassing HBV infection, fibrosis, cirrhosis, and HCC. Strategies includeimmune checkpoint blockades, metabolic T-cell targeting therapy, therapeuticT-cell vaccination, and adoptive transfer of genetically engineered CD8+ T cells, along with the combined usage of programmed cell death protein-1/programmeddeath ligand-1 (PD-1/PD-L1) inhibitors with mitochondria-targeted antioxidants.Given that targeting CD8+ T cells at various stages of hepatitis Bvirus-induced hepatocellular carcinoma (HBV + HCC) shows promise, we reviewedthe ongoing need for research to elucidate the complex interplay between CD8+ T cells and the liver microenvironment in the progression of HBV infection toHCC. We also discussed personalized treatment regimens, combining therapeuticstrategies and harnessing gut microbiota modulation, which holds potential forenhanced clinical benefits. In conclusion, this review delves into the immunedynamics of CD8+ T cells, microenvironment changes, and therapeuticstrategies within the liver during chronic HBV infection, HCC progression, andrelated liver diseases.

Stratifying ICIs-responsive tumor microenvironment in HCC: from parsing out immune-hypoxic crosstalk to clinically applicable MRI-radiomics models

BACKGROUND

We aimed to redefine Immune checkpoint inhibitors (ICIs)-responsive "hot" TME and develop a corresponding stratification model to maximize ICIs-efficacy in Hepatocellular Carcinoma (HCC).

METHODS

Hypoxic scores were designed, and the relevance to immunotherapy responses were validated in pan-cancers through single cell analysis. Multi-omics analysis using the hypoxic scores and immune infiltrate abundance was performed to redefine the ICIs-responsive TME subtype in HCC patients from TCGA (n = 363) and HCCDB database (n = 228). The immune hypoxic stress index (IHSI) was constructed to stratify the ICIs-responsive TME subtype, with exploring biological mechanism in vitro and in vivo. MRI-radiomics models were built for clinical applicability.

RESULTS

The hypoxic scores were lower in the dominant cell-subclusters of responders in pan-cancers. The higher immune infiltrate-normoxic (HIN) subtype was redefined as the ICIs-responsive TME. Stratification of the HIN subtype using IHSI effectively identified ICIs-responders in Melanoma (n = 122) and urological cancer (n = 22). TRAF3IP3, the constituent gene of IHSI, was implicated in ICIs-relevant "immune-hypoxic" crosstalk by stimulating MAVS/IFN-I pathway under normoxic condition. MRI-radiomics models assessing TRAF3IP3 with HIF1A expression (AUC > 0.80) screened ICIs-Responders in HCC cohort (n = 75).

CONCLUSION

The hypoxic-immune stratification redefined ICIs-responsive TME and provided MRI-Radiomics models for initial ICIs-responders screening, with IHSI facilitating further identification.

Malignant cell receptor-ligand subtypes guide the prediction of prognosis and personalized immunotherapy of liver cancer

OBJECTIVE

Liver cancer is a prevalent disease with a dismal prognosis. The aim of the research is to identify subgroups based on malignant cell receptor ligand gene from single-cell RNA, which might lead to customized immunotherapy for patients with liver cancer.

METHODS

Based on scRNA-seq data, we identified the receptor-ligand genes associated with prognosis and classify patients into molecular subtypes by univariate Cox regression and consensus clustering. LASSO regression was performed to construct a prognostic model, which was validated in TCGA and ICGC datasets. Immune infiltration and prediction of immunotherapy response were analyzed using ssGSEA, ESTIMATE, TIDE, and TRS score calculation. Finally, qPCR and Western blot validation of key genes and protein levels in cell lines.

RESULTS

A risk model using 16-gene expression levels predicted liver cancer patients' prognosis. The RiskScore associated significantly with tumor clinical characteristics and immunity, integrated with clinicopathological features for survival prediction. Differential expression of SRXN1 was verified in hepatocellular carcinoma and normal liver cells.

CONCLUSION

Our study utilizes single-cell analysis to investigate the communication between malignant cells and other cell types, identifying molecular subtypes based on malignant cell receptor ligand genes, offering new insights for the development of personalized immunotherapy and prognostic prediction models.

Interplay of Sphingolipid Metabolism in Predicting Prognosis of GBM Patients: Towards Precision Immunotherapy

Background: In spite of numerous existing bio-surveillance systems for predicting glioma (GBM) prognosis, enhancing the efficacy of immunotherapy remains an ongoing conundrum. The continual scrutiny of the dynamic interplay between the sphingolipid metabolic pathway and tumor immunophenotypes has unveiled potential implications. However, the intricate orchestration of functional and regulatory mechanisms by long non-coding RNAs (lncRNAs) in GBM, particularly in the context of sphingolipid metabolism, remains cryptic. Methods: We harnessed established R packages to intersect gene expression profiles of GBM patients within the The Cancer Genome Atlas (TCGA) database with the compilation of sphingolipid metabolism genes from GeneCards. This enabled us to discern markedly distinct lncRNAs, which were subsequently deployed to construct a robust prognostic model utilizing Lasso-Cox regression analysis. We then scrutinized the immune microenvironment across various risk strata using the ssGSEA and CIBERSORT algorithms. To evaluate mutation patterns and drug resistance profiles within patient subgroups, we devised the "Prophytic" and "Maftools" packages, respectively. Results: Our investigation scrutinized lncRNAs linked to sphingolipid metabolism, utilizing glioma specimens from TCGA. We meticulously curated 1224 sphingolipid-associated genes gleaned from GeneCards and pinpointed 272 differentially expressed mRNAs via transcriptomic analysis. Enrichment analyses underscored their significance in sphingolipid processes. A prognostic model founded on 17 meticulously selected lncRNAs was systematically constructed and validated. This model adeptly stratified GBM patients into high- and low-risk categories, yielding highly precise prognostic insights. We also discerned correlations between immune cell infiltration and genetic mutation discrepancies, along with distinct therapeutic responses through drug sensitivity analysis. Notably, computational findings were corroborated through experimental validation by RT-PCR. Conclusion: In summation, our exhaustive inquiry underscores the multifaceted utility of the sphingolipid metabolic pathway as an autonomous diagnostic and prognostic indicator for glioma patients. Furthermore, we amalgamate a profusion of substantiated evidence concerning immune infiltration and gene mutations, thereby reinforcing the proposition that sphingolipid metabolism may function as a pivotal determinant in the panorama of immunotherapeutic interventions.

Impact of hepatectomy and postoperative adjuvant transarterial chemoembolization on serum tumor markers and prognosis in intermediate-stage hepatocellular carcinoma

BACKGROUND

Primary hepatocellular carcinoma (HCC) is a common malignant tumour, and its early symptoms are often not obvious, resulting in many patients experiencing middle- to late-stage disease at the time of diagnosis. The optimal time for surgery is often missed for these patients, and those who do undergo surgery have unsatisfactory long-term outcomes and a high recurrence rate within five years. Therefore, postoperative follow-up treatments, such as transhepatic arterial chemoembolization (TACE), have become critical to improving survival and reducing recurrence rates.

AIM

To validate the prophylactic role of TACE after hepatic resection and to assess its impact on patient prognosis.

METHODS

This study investigated the efficacy of TACE in patients with intermediate-stage HCC after hepatectomy. When the post-treatment results of the observation group and the control group were compared, it was found that the inclusion of TACE significantly improved the clinical efficacy, reduced the levels of tumour markers and did not aggravate the damage to liver function. Thus, this may be an effective and comprehensive treatment strategy for patients with intermediate-stage HCC that helps to improve their quality of life and survival time.

RESULTS

When the baseline data were analysed, no statistical differences were found between the two groups in terms of gender, age, hepatitis B virus, cirrhosis, Child-Pugh grading, number of tumours, maximum tumour diameter and degree of tumour differentiation. The assessment of clinical efficacy showed that the post-treatment overall remission rate of the observation group was significantly higher than that of the control group. In terms of changes in tumour markers, the alpha-fetoprotein and carcinoembryonic antigen levels in the patients in the observation group decreased more significantly after treatment compared with those in the control group. When post-treatment changes in liver function indicators were analysed, no statistical differences were found in the total bilirubin, alanine aminotransferase and aspartate aminotransferase levels between the two groups.

CONCLUSION

In patients with intermediate-stage HCC, post-hepatectomy TACE significantly improved clinical outcomes, reduced tumour-marker levels and may have improved the prognosis by removing residual lesions. Thus, this may be an effective and comprehensive treatment strategy for patients with intermediate-stage HCC.

Impact of apolipoprotein A1 on tumor immune microenvironment, clinical prognosis and genomic landscape in hepatocellular carcinoma

Background

Current knowledge on apolipoprotein A1 (APOA1) in hepatocellular carcinoma (HCC) is fragmented and even contradictory. Multi-dimensional analyses are required to comprehensively elucidate its value and underlying mechanism.

Methods

We collected 49 RNA-seq datasets, 40 cell line types data and 70 scRNA pan-cancer datasets public available, including 17 HCC datasets (1754 tumor samples), and enrolled 73 pairs of HCC tissue and 516 blood samples independently from our clinics. APOA1 impacting on the HCC tumor microenvironment (TME) was analyzed using intensive data mining. Methylation sequencing, flow cytometry, quantitative PCR, western blot, immunohistochemistry and clinical chemistry assays were conducted for wet experimental investigation.

Results

The APOA1 ontology fingerprint indicated that it played various crucial biological roles in HCC, primarily involved in cholesterol efflux. Consistent findings at histology, serology, and clinical follow-up revealed that high APOA1 was a good prognosis indicator of HCC. Hypermethylation in the APOA1 promoter region was found in clinical samples which is in accordance with the reduction of APOA1 in HCC. The cell cycle, DNA replication, mismatch repair pathways, and tumor cell proliferation were less observed in the HCC APOA1high subgroup. The favorable immunoregulatory abilities of APOA1 showed interesting findings: a positive correlation between APOA1 and anti-tumor immune cells (NK, CD8+ T cells) and a negative association with immune cells exerting immunosuppressive effects, including M2 macrophages.

Conclusion

This is an integrative multidimensional exploration of APOA1 using bioinformatics and experiments. Both the prognostic value and anti-tumor effects based on APOA1 panoramic exploration in the HCC TME demonstrate a new potential clinical target for HCC assessment and intervention in the future.

Guanylate-binding proteins signature predicts favorable prognosis, immune-hot microenvironment, and immunotherapy response in hepatocellular carcinoma

BACKGROUND

The role of guanylate-binding proteins (GBPs) in various cancers has been elucidated recently. However, our knowledge of the clinical relevance and biological characteristics of GBPs in hepatocellular carcinoma (HCC) remains limited.

METHODS

A total of 955 HCC patients were enrolled from five independent public HCC cohorts. The role of GBP molecules in HCC was preliminarily investigated, and a GBP family signature, termed GBPs-score, was constructed by principal component analysis to combine the GBP molecule values. We revealed the effects of GBP genes and GBPs-score in HCC via well-established bioinformatics methods and validated GBP1-5 experimentally in a tissue microarray (TMA) cohort.

RESULTS

GBPs molecules were closely associated with the prognosis of patients with HCC, and a high GBPs-score highly inferred a favorable survival outcome. We also revealed high GBPs-score was related to anti-tumor immunity, the immune-hot tumor microenvironment (TME), and immunotherapy response. Among the GBPs members, GBP1-5 rather than GBP6/7 may be dominant in these fields. The TMA analysis based on immunohistochemistry showed positive correlations between GBP1-5 and the immune-hot TME with abundant infiltration of CD8+ T cells in HCC.

CONCLUSIONS

Our integrative study revealed the genetic and immunologic characterizations of GBPs in HCC and highlighted their potential values as promising biomarkers for prognosis and immunotherapy.

Cuproptosis combines immune landscape providing prognostic biomarker in head and neck squamous carcinoma

Head and neck squamous carcinomas (HNSC) are the seventh most common cancer around the world. Treatment options available today have considerable limitations in terms of efficacy. Identifying novel therapeutic targets for HNSC is, therefore, urgently needed. As a novel determined regulated cell death (RCD), Cuproptosis is correlated with the development, treatment response, and prognosis of various cancer. However, the potential role of Cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of HNSC remains unclear. To figure out whether TME cells and Cuproptosis could better predict prognosis, in this study, we analyzed the expression, mutation status, and other clinical information of 502 HNSC patients by dividing them into four clusters based on their CRGs and TME cell expression. Utilizing the LASSO-Cox method and bootstrap, we established Prognostic Cuproptosis and TME classifier, which were significantly associated with prognosis, pathways, clinical features, and immune cell infiltration in TME of HNSC. To go further, the subgroup Cup low/TMEhigh displayed a better prognosis than any others. Two GEO datasets demonstrated the proposed risk model's clinical applicability. Our GO enrichment analyses proved the conjoint effect of Cuproptosis and TME on tumor angiogenesis, proliferation, and so on. Single-cell analysis and Immunotherapy profile then provided a foundation for determining the molecular mechanisms. It revealed the prognostic risk score positively correlated with T cell activation and natural killer (NK) recruiting. As far as we know, this study is the first time to explore the involvement of CRGs regulation in the TME of HNSC. In a word, it is vital to use these findings to develop new therapeutic strategies.

The combined signatures of the tumour microenvironment and nucleotide metabolism-related genes provide a prognostic and therapeutic biomarker for gastric cancer

The tumour microenvironment (TME) is vital to tumour development and influences the immunotherapy response. Abnormal nucleotide metabolism (NM) not only promotes tumour cell proliferation but also inhibits immune responses in the TME. Therefore, this study aimed to determine whether the combined signatures of NM and the TME could better predict the prognosis and treatment response in gastric cancer (GC). 97 NM-related genes and 22 TME cells were evaluated in TCGA-STAD samples, and predictive NM and TME characteristics were determined. Subsequent correlation analysis and single-cell data analysis illustrated a link between NM scores and TME cells. Thereafter, NM and TME characteristics were combined to construct an NM-TME classifier. Patients in the NMlow/TMEhigh group exhibited better clinical outcomes and treatment responses, which could be attributed to the differences in immune cell infiltration, immune checkpoint genes, tumour somatic mutations, immunophenoscore, immunotherapy response rate and proteomap. Additionally, the NMhigh/TMElow group benefited more from Imatinib, Midostaurin and Linsitinib, while patients in the NMlow/TMEhigh group benefited more from Paclitaxel, Methotrexate and Camptothecin. Finally, a highly reliable nomogram was developed. In conclusion, the NM-TME classifier demonstrated a pretreatment predictive value for prognosis and therapeutic responses, which may offer novel strategies for strategizing patients with optimal therapies.

Integrated Analysis of TME and Hypoxia Identifies a Classifier to Predict Prognosis and Therapeutic Biomarkers in Soft Tissue Sarcomas

Soft tissue sarcoma (STS) is one of the rarest but most aggressive cancer. It is important to note that intratumoral hypoxia and tumor microenvironment (TME) infiltration play a significant role in the growth and therapeutic resistance of STS. The goal of this study was therefore to determine whether linking hypoxia-related parameters to TME cells could provide a more accurate prediction of prognosis and therapeutic response. An analysis of 109 hypoxia-related genes and 64 TME cells was conducted in STS. Hypoxia-TME classifier was constructed based on 6 hypoxia prognostic genes and 8 TME cells. As a result, we evaluated the prognosis, tumor, and immune characteristics, as well as the effectiveness of therapies in Hypoxia-TME-defined subgroups. The Lowplus group showed a better prognosis and therapeutic response than any other subgroup. It is possible to unravel these differences based on immune-related molecules and somatic mutations in tumors. Further validation of Hypoxia-TME was done in an additional cohort of 225 STS patients. Additionally, we identified five key genes through differential analysis and RT-qPCR, namely, ACSM5, WNT7B, CA9, MMP13, and RAC3, which could be targeted for therapy. As a whole, the Hypoxia-TME classifier demonstrated a pretreatment predictive value for prognosis and therapeutic outcome, providing new approaches to therapy strategizing for patients.

The immune escape signature predicts the prognosis and immunotherapy sensitivity for pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies worldwide. Immune escape is considered to be a reason for immunotherapy failure in PDAC. In this study, we explored the correlation between immune escape-related genes and the prognosis of PDAC patients.

Methods

1163 PDAC patients from four public databases, including The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Array-express, and Gene Expression Omnibus (GEO), were included in our study. Cox regression analysis was used to identify the 182 immune genes which were significantly associated with overall survival (OS). And then we established an immune escape-related gene prognosis index (IEGPI) score using several datasets as the training cohort and validated it using the validation cohort. Kaplan-Meier (KM) and Cox regression analysis were used to detect the relationship of IEGPI score with OS. We further explored the relationship between the IEGPI and immune indexes. And the prediction value of response for immunotherapy in Tumor Immune Dysfunction and Exclusion (TIDE) dataset.

Results

We establish an IEGPI score based on 27 immune escape genes which were significantly related to the prognosis of OS in PDAC patients. Patients in the high-IEGPI group had a significantly worse overall survival rate compared with that in the low-IEGPI groups by KM curves and cox-regression. 5 of the 32 cancer types in TCGA could be significantly distinguished in survival rates through the low- and high-IEGPI groups. Moreover, the correlation between the IEGPI score was negatively correlated with an immune score in several datasets. And higher IEGPI better recurrence-free survival (RFS) and OS in the patients after patients were treated with both PD-1 and CTLA4 in the public datasets (P<0.05). Intriguingly, by using RT-PCR, we verified that the gene of PTPN2, CEP55, and JAK2 were all higher in the BxPC-3 and PANC-1 than HPDE5 cells. Lastly, we found that the IEGPI score was higher in K-rasLSL.G12D/+, p53LSL.R172H/+, Pdx1Cre (KPC) mice model with anti-PD-L1 than that without anti-PD-L1.

Conclusion

Using the immune escape-related genes, our study established and validated an IEGPI score in PDAC patients from the public dataset. IEGPI score has the potential to serve as a prognostic marker and as a tool for selecting tumor patients suitable for immunotherapy in clinical practice.