Gao Q, Zhu H, Dong L, Shi W, Chen R, Song Z, Huang C, Li J, Dong X, Zhou Y, Liu Q, Ma L, Wang X, Zhou J, Liu Y, Boja E, Robles AI, Ma W, Wang P, Li Y, Ding L, Wen B, Zhang B, Rodriguez H, Gao D, Zhou H, Fan J. Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma. Cell 2019; 179: 561-577. e22. [PMID: 31585088 DOI: 10.1016/j.cell.2019.08.052]

Proteomics of adjacent-to-tumor samples uncovers clinically relevant biological events in hepatocellular carcinoma

Normal adjacent tissues (NATs) of hepatocellular carcinoma (HCC) differ from healthy liver tissues and their heterogeneity may contain biological information associated with disease occurrence and clinical outcome that has yet to be fully evaluated at the proteomic level. This study provides a detailed description of the heterogeneity of NATs and the differences between NATs and healthy livers and revealed that molecular features of tumor subgroups in HCC were partially reflected in their respective NATs. Proteomic data classified HCC NATs into two subtypes (Subtypes 1 and 2), and Subtype 2 was associated with poor prognosis and high-risk recurrence. The pathway and immune features of these two subtypes were characterized. Proteomic differences between the two NAT subtypes and healthy liver tissues were further investigated using data-independent acquisition mass spectrometry, revealing the early molecular alterations associated with the progression from healthy livers to NATs. This study provides a high-quality resource for HCC researchers and clinicians and may significantly expand the knowledge of tumor NATs to eventually benefit clinical practice.

An invasive zone in human liver cancer identified by Stereo-seq promotes hepatocyte-tumor cell crosstalk, local immunosuppression and tumor progression

Dissecting and understanding the cancer ecosystem, especially that around the tumor margins, which have strong implications for tumor cell infiltration and invasion, are essential for exploring the mechanisms of tumor metastasis and developing effective new treatments. Using a novel tumor border scanning and digitization model enabled by nanoscale resolution-SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq), we identified a 500 µm-wide zone centered around the tumor border in patients with liver cancer, referred to as "the invasive zone". We detected strong immunosuppression, metabolic reprogramming, and severely damaged hepatocytes in this zone. We also identified a subpopulation of damaged hepatocytes with increased expression of serum amyloid A1 and A2 (referred to collectively as SAAs) located close to the border on the paratumor side. Overexpression of CXCL6 in adjacent malignant cells could induce activation of the JAK-STAT3 pathway in nearby hepatocytes, which subsequently caused SAAs' overexpression in these hepatocytes. Furthermore, overexpression and secretion of SAAs by hepatocytes in the invasive zone could lead to the recruitment of macrophages and M2 polarization, further promoting local immunosuppression, potentially resulting in tumor progression. Clinical association analysis in additional five independent cohorts of patients with primary and secondary liver cancer (n = 423) showed that patients with overexpression of SAAs in the invasive zone had a worse prognosis. Further in vivo experiments using mouse liver tumor models in situ confirmed that the knockdown of genes encoding SAAs in hepatocytes decreased macrophage accumulation around the tumor border and delayed tumor growth. The identification and characterization of a novel invasive zone in human cancer patients not only add an important layer of understanding regarding the mechanisms of tumor invasion and metastasis, but may also pave the way for developing novel therapeutic strategies for advanced liver cancer and other solid tumors.

Proteome Landscapes of Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma

Liver cancer is among the top leading causes of cancer mortality worldwide. Particularly, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (CCA) have been extensively investigated from the aspect of tumor biology. However, a comprehensive and systematic understanding of the molecular characteristics of HCC and CCA remains absent. Here, we characterized the proteome landscapes of HCC and CCA using the data-independent acquisition (DIA) mass spectrometry (MS) method. By comparing the quantitative proteomes of HCC and CCA, we found several differences between the two cancer types. In particular, we found an abnormal lipid metabolism in HCC and activated extracellular matrix-related pathways in CCA. We next developed a three-protein classifier to distinguish CCA from HCC, achieving an area under the curve (AUC) of 0.92, and an accuracy of 90% in an independent validation cohort of 51 patients. The distinct molecular characteristics of HCC and CCA presented in this study provide new insights into the tumor biology of these two major important primary liver cancers. Our findings may help develop more efficient diagnostic approaches and new targeted drug treatments.

Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology

Organoid models have the potential to recapitulate the biological and pharmacotypic features of parental tumors. Nevertheless, integrative pharmaco-proteogenomics analysis for drug response features and biomarker investigation for precision therapy of patients with liver cancer are still lacking. We established a patient-derived liver cancer organoid biobank (LICOB) that comprehensively represents the histological and molecular characteristics of various liver cancer types as determined by multiomics profiling, including genomic, epigenomic, transcriptomic, and proteomic analysis. Proteogenomic profiling of LICOB identified proliferative and metabolic organoid subtypes linked to patient prognosis. High-throughput drug screening revealed distinct response patterns of each subtype that were associated with specific multiomics signatures. Through integrative analyses of LICOB pharmaco-proteogenomics data, we identified the molecular features associated with drug responses and predicted potential drug combinations for personalized patient treatment. The synergistic inhibition effect of mTOR inhibitor temsirolimus and the multitargeted tyrosine kinase inhibitor lenvatinib was validated in organoids and patient-derived xenografts models. We also provide a user-friendly web portal to help serve the biomedical research community. Our study is a rich resource for investigation of liver cancer biology and pharmacological dependencies and may help enable functional precision medicine.

Pyrroline-5-Carboxylate Reductase-2 Promotes Colorectal Carcinogenesis by Modulating Microtubule-Associated Serine/Threonine Kinase-like/Wnt/β-Catenin Signaling

BACKGROUND

Despite significant progress in clinical management, colorectal cancer (CRC) remains the third most common cause of cancer-related deaths. A positive association between PYCR2 (pyrroline-5-carboxylate reductase-2), a terminal enzyme of proline metabolism, and CRC aggressiveness was recently reported. However, how PYCR2 promotes colon carcinogenesis remains ill understood.

METHODS

A comprehensive analysis was performed using publicly available cancer databases and CRC patient cohorts. Proteomics and biochemical evaluations were performed along with genetic manipulations and in vivo tumor growth assays to gain a mechanistic understanding.

RESULTS

PYCR2 expression was significantly upregulated in CRC and associated with poor patient survival, specifically among PYCR isoforms (PYCR1, 2, and 3). The genetic inhibition of PYCR2 inhibited the tumorigenic abilities of CRC cells and in vivo tumor growth. Coinciding with these observations was a significant decrease in cellular proline content. PYCR2 overexpression promoted the tumorigenic abilities of CRC cells. Proteomics (LC-MS/MS) analysis further demonstrated that PYCR2 loss of expression in CRC cells inhibits survival and cell cycle pathways. A subsequent biochemical analysis supported the causal role of PYCR2 in regulating CRC cell survival and the cell cycle, potentially by regulating the expression of MASTL, a cell-cycle-regulating protein upregulated in CRC. Further studies revealed that PYCR2 regulates Wnt/β-catenin-signaling in manners dependent on the expression of MASTL and the cancer stem cell niche.

CONCLUSIONS

PYCR2 promotes MASTL/Wnt/β-catenin signaling that, in turn, promotes cancer stem cell populations and, thus, colon carcinogenesis. Taken together, our data highlight the significance of PYCR2 as a novel therapeutic target for effectively treating aggressive colon cancer.

Aberrant LYZ expression in tumor cells serves as the potential biomarker and target for HCC and promotes tumor progression via csGRP78

Hepatocellular carcinoma (HCC) takes the predominant malignancy of hepatocytes with bleak outcomes owing to high heterogeneity among patients. Personalized treatments based on molecular profiles will better improve patients' prognosis. Lysozyme (LYZ), a secretory protein with antibacterial function generally expressed in monocytes/macrophages, has been observed for the prognostic implications in different types of tumors. However, studies about the explicit applicative scenarios and mechanisms for tumor progression are still quite limited, especially for HCC. Here, based on the proteomic molecular classification data of early-stage HCC, we revealed that the LYZ level was elevated significantly in the most malignant HCC subtype and could serve as an independent prognostic predictor for HCC patients. Molecular profiles of LYZ-high HCCs were typical of those for the most malignant HCC subtype, with impaired metabolism, along with promoted proliferation and metastasis characteristics. Further studies demonstrated that LYZ tended to be aberrantly expressed in poorly differentiated HCC cells, which was regulated by STAT3 activation. LYZ promoted HCC proliferation and migration in both autocrine and paracrine manners independent of the muramidase activity through the activation of downstream protumoral signaling pathways via cell surface GRP78. Subcutaneous and orthotopic xenograft tumor models indicated that targeting LYZ inhibited HCC growth markedly in NOD/SCID mice. These results propose LYZ as a prognostic biomarker and therapeutic target for the subclass of HCC with an aggressive phenotype.

Proteogenomics of clear cell renal cell carcinoma response to tyrosine kinase inhibitor

The tyrosine kinase inhibitor (TKI) Sunitinib is one the therapies approved for advanced renal cell carcinoma. Here, we undertake proteogenomic profiling of 115 tumors from patients with clear cell renal cell carcinoma (ccRCC) undergoing Sunitinib treatment and reveal the molecular basis of differential clinical outcomes with TKI therapy. We find that chromosome 7q gain-induced mTOR signaling activation is associated with poor therapeutic outcomes with Sunitinib treatment, whereas the aristolochic acid signature and VHL mutation synergistically caused enhanced glycolysis is correlated with better prognosis. The proteomic and phosphoproteomic analysis further highlights the responsibility of mTOR signaling for non-response to Sunitinib. Immune landscape characterization reveals diverse tumor microenvironment subsets in ccRCC. Finally, we construct a multi-omics classifier that can detect responder and non-responder patients (receiver operating characteristic-area under the curve, 0.98). Our study highlights associations between ccRCC molecular characteristics and the response to TKI, which can facilitate future improvement of therapeutic responses.

Molecular subtypes based on DNA sensors predict prognosis and tumor immunophenotype in hepatocellular carcinoma

DNA sensors play crucial roles in inflammation and have been indicated to be involved in antitumor or tumorigenesis, while it is still unclear whether DNA sensors have potential roles in the prognosis and immunotherapy of hepatocellular carcinoma (HCC). Herein, The Cancer Genome Atlas and Gene Expression Omnibus databases were used to analyze RNA sequencing data and clinical information. A total of 14 DNA sensors were collected and performed consensus clustering to determine their molecular mechanisms in HCC. Two distinct molecular subtypes (Clusters C1 and C2) were identified and were associated with different overall survival (OS). Immune subtype analysis revealed that C1 was mainly characterized by inflammation, while C2 was characterized by lymphocyte depletion. Immune scoring and immunomodulatory function analysis confirmed the different immune microenvironment of C1 and C2. Notably, significant differences in "Hot Tumor" Immunophenotype were observed between the two subtypes. Moreover, the prognostic model based on DNA sensors is capable of effectively predicting the OS of HCC patients. Besides, the chemotherapeutic drug analysis showed the different sensitivity of two subtypes. Taken together, our study shows that the proposed DNA sensors were a reliable signature to predict the prognosis and immunotherapy response with potential application in the clinical decision and treatment of HCC.

Radiomics model based on contrast-enhanced computed tomography to predict early recurrence in patients with hepatocellular carcinoma after radical resection

BACKGROUND

Radical resection remains an effective strategy for patients with hepatocellular carcinoma (HCC). Unfortunately, the postoperative early recurrence (recurrence within 2 years) rate is still high.

AIM

To develop a radiomics model based on preoperative contrast-enhanced computed tomography (CECT) to evaluate early recurrence in HCC patients with a single tumour.

METHODS

We enrolled a total of 402 HCC patients from two centres who were diagnosed with a single tumour and underwent radical resection. First, the features from the portal venous and arterial phases of CECT were extracted based on the region of interest, and the early recurrence-related radiomics features were selected via the least absolute shrinkage and selection operator proportional hazards model (LASSO Cox) to determine radiomics scores for each patient. Then, the clinicopathologic data were combined to develop a model to predict early recurrence by Cox regression. Finally, we evaluated the prediction performance of this model by multiple methods.

RESULTS

A total of 1915 radiomics features were extracted from CECT images, and 31 of them were used to determine the radiomics scores, which showed a significant difference between the early recurrence and nonearly recurrence groups. Univariate and multivariate Cox regression analyses showed that radiomics scores and serum alpha-fetoprotein were independent indicators, and they were used to develop a combined model to predict early recurrence. The area under the receiver operating characteristic curve values for the training and validation cohorts were 0.77 and 0.74, respectively, while the C-indices were 0.712 and 0.674, respectively. The calibration curves and decision curve analysis showed satisfactory accuracy and clinical utilities. Kaplan-Meier curves based on recurrence-free survival and overall survival showed significant differences.

CONCLUSION

The preoperative radiomics model was shown to be effective for predicting early recurrence among HCC patients with a single tumour.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

HBV confers innate immune evasion through triggering HAT1/acetylation of H4K5/H4K12/miR-181a-5p or KPNA2/cGAS-STING/IFN-I signaling

Viral immune evasion is crucial to the pathogenesis of hepatitis B virus (HBV) infection. However, the role of HBV in the modulation of innate immune evasion is poorly understood. A liver-specific histone acetyltransferase 1 (Hat1) knockout (KO) mouse model and HAT1 KO cell line were established. Immunohistochemistry staining, Western blot analysis, Southern blot analysis, Northern blot analysis, immunofluorescence assays, enzyme-linked immunosorbent assay, reverse transcription-quantitative polymerase chain reaction, and chromatin immunoprecipitation assays were performed in the livers of mouse models, primary human hepatocytes, HepG2-NTCP, and Huh7 and HepG2 cell lines. HBV-elevated HAT1 increased the expression of miR-181a-5p targeting cyclic GMP-AMP synthase (cGAS) messenger RNA 3' untranslated regions through modulating acetylation of H4K5 and H4K12 in vitro and in vivo, leading to the inability of cGAS-stimulator of interferon genes (STING) pathway and type I interferon (IFN-I) signaling. Additionally, HBV-elevated HAT1 promoted the expression of KPNA2 through modulating acetylation of H4K5 and H4K12 in the system, resulting in nuclear translocation of cGAS, HBx was responsible for the events by HAT1, suggesting that HBV-elevated HAT1 controls the cGAS-STING pathway and IFN-I signaling to modulate viral innate immune evasion. HBV confers innate immune evasion through triggering HAT1/acetylation of H4K5/H4K12/miR-181a-5p or KPNA2/cGAS-STING/IFN-I signaling. Our finding provides new insights into the mechanism by which HBV drives viral innate immune evasion.

Nuclear lamina erosion-induced resurrection of endogenous retroviruses underlies neuronal aging

The primate frontal lobe (FL) is sensitive to aging-related neurocognitive decline. However, the aging-associated molecular mechanisms remain unclear. Here, using physiologically aged non-human primates (NHPs), we depicted a comprehensive landscape of FL aging with multidimensional profiling encompassing bulk and single-nucleus transcriptomes, quantitative proteome, and DNA methylome. Conjoint analysis across these molecular and neuropathological layers underscores nuclear lamina and heterochromatin erosion, resurrection of endogenous retroviruses (ERVs), activated pro-inflammatory cyclic GMP-AMP synthase (cGAS) signaling, and cellular senescence in post-mitotic neurons of aged NHP and human FL. Using human embryonic stem-cell-derived neurons recapitulating cellular aging in vitro, we verified the loss of B-type lamins inducing resurrection of ERVs as an initiating event of the aging-bound cascade in post-mitotic neurons. Of significance, these aging-related cellular and molecular changes can be alleviated by abacavir, a nucleoside reverse transcriptase inhibitor, either through direct treatment of senescent human neurons in vitro or oral administration to aged mice.

Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach

Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.

Characterization and clinical verification of immune-related genes in hepatocellular carcinoma to aid prognosis evaluation and immunotherapy

BACKGROUND

Immune-related genes (IRGs) have been confirmed to play an important role in tumorigenesis and tumor microenvironment formation in hepatocellular carcinoma (HCC). We investigated how IRGs regulates the HCC immunophenotype and thus affects the prognosis and response to immunotherapy.

METHODS

We investigated RNA expression of IRGs and developed an immune-related genes-based prognostic index (IRGPI) in HCC samples. Then, the influence of the IRGPI on the immune microenvironment was comprehensively analysed.

RESULTS

According to IRGPI, HCC patients are divided into two immune subtypes. A high IRGPI was characterized by an increased tumor mutation burden (TMB) and a poor prognosis. More CD8 + tumor infiltrating cells and expression of PD-L1 were observed in low IRGPI subtypes. Two immunotherapy cohorts confirmed patients with low IRGPI demonstrated significant therapeutic benefits. Multiplex immunofluorescence staining determined that there were more CD8 + T cells infiltrating into tumor microenvironment in IRGPI-low groups, and the survival time of these patients was longer.

CONCLUSIONS

This study demonstrated that the IRGPI serve as a predictive prognostic biomarker and potential indicator for immunotherapy.

PANoptosis-based molecular subtyping and HPAN-index predicts therapeutic response and survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a highly prevalent and fatal cancer. The role of PANoptosis, a novel form of programmed cell death, in HCC is yet to be fully understood. This study focuses on identifying and analyzing PANoptosis-associated differentially expressed genes in HCC (HPAN_DEGs), aiming to enhance our understanding of HCC pathogenesis and potential treatment strategies.

Methods

We analyzed HCC differentially expressed genes from TCGA and IGCG databases and mapped them to the PANoptosis gene set, identifying 69 HPAN_DEGs. These genes underwent enrichment analyses, and consensus clustering analysis was used to determine three distinct HCC subgroups based on their expression profiles. The immune characteristics and mutation landscape of these subgroups were evaluated, and drug sensitivity was predicted using the HPAN-index and relevant databases.

Results

The HPAN_DEGs were mainly enriched in pathways associated with the cell cycle, DNA damage, Drug metabolism, Cytokines, and Immune receptors. We identified three HCC subtypes (Cluster_1, SFN+PDK4-; Cluster_2, SFN-PDK4+; Cluster_3, SFN/PDK4 intermediate expression) based on the expression profiles of the 69 HPAN_DEGs. These subtypes exhibited distinct clinical outcomes, immune characteristics, and mutation landscapes. The HPAN-index, generated by machine learning using the expression levels of 69 HPAN_DEGs, was identified as an independent prognostic factor for HCC. Moreover, the high HPAN-index group exhibited a high response to immunotherapy, while the low HPAN-index group showed sensitivity to small molecule targeted drugs. Notably, we observed that the YWHAB gene plays a significant role in Sorafenib resistance.

Conclusion

This study identified 69 HPAN_DEGs crucial to tumor growth, immune infiltration, and drug resistance in HCC. Additionally, we discovered three distinct HCC subtypes and constructed an HPAN-index to predict immunotherapeutic response and drug sensitivity. Our findings underscore the role of YWHAB in Sorafenib resistance, presenting valuable insights for personalized therapeutic strategy development in HCC.

ABPEPserver: a web application for documentation and analysis of substitutants

BACKGROUND

Cancer immunotherapy is implemented by identifying antigens that are presented on the cell surface of cancer cells and illicit T-cell response (Schumacher and Schreiber, Science 348:69-74, 2015; Waldman et al., Nat Rev Immunol 20:651-668, 2020; Zhang et al., Front Immunol 12:672,356, 2021b). Classical candidates of such antigens are the peptides resulting from genetic alterations and are named "neoantigen" (Schumacher and Schreiber, Science 348:69-74, 2015). Neoantigens have been widely catalogued across several human cancer types (Tan et al., Database (Oxford) 2020;2020b; Vigneron et al., Cancer Immun 13:15, 2013; Yi et al., iScience 24:103,107, 2021; Zhang et al., BMC Bioinformatics 22:40, 2021a). Recently, a new class of inducible antigens has been identified, namely Substitutants, that are produced as a result of aberrant protein translation (Pataskar et al., Nature 603:721-727, 2022). MAIN: Catalogues of Substitutant expression across human cancer types, their specificity and association to gene expression signatures remain elusive for the scientific community's access. As a solution, we present ABPEPserver, an online database and analytical platform that can visualize a large-scale tumour proteomics analysis of Substitutant expression across eight tumour types sourced from the CPTAC database (Edwards et al., J Proteome Res 14:2707-2713, 2015). Functionally, ABPEPserver offers the analysis of gene-association signatures of Substitutant peptides, a comparison of enrichment between tumour and tumour-adjacent normal tissues, and a list of peptides that serve as candidates for immunotherapy design. ABPEPserver will significantly enhance the exploration of aberrant protein production in human cancer, as exemplified in a case study.

CONCLUSION

ABPEPserver is designed on an R SHINY platform to catalogue Substitutant peptides in human cancer. The application is available at https://rhpc.nki.nl/sites/shiny/ABPEP/ . The code is available under GNU General public license from GitHub ( https://github.com/jasminesmn/ABPEPserver ).

A transcriptomic intratumour heterogeneity-free signature overcomes sampling bias in prognostic risk classification for hepatocellular carcinoma

Background & Aims

Intratumour heterogeneity (ITH) fosters the vulnerability of RNA expression-based biomarkers derived from a single biopsy to tumour sampling bias, and is regarded as an unaddressed confounding factor for patient precision stratification using molecular biomarkers. This study aimed to identify an ITH-free predictive biomarker in hepatocellular carcinoma (HCC).

Methods

We interrogated the confounding effect of ITH on performance of molecular biomarkers and quantified transcriptomic heterogeneity utilising three multiregional HCC transcriptome datasets involving 142 tumoural regions from 30 patients. A de novo strategy based on the heterogeneity metrics was devised to develop a surveillant biomarker (a utility gadget using RNA; AUGUR) using three datasets involving 715 liver samples from 509 patients with HCC. The performance of AUGUR was assessed in seven cross-platform HCC cohorts that encompassed 1,206 patients.

Results

An average discordance rate of 39.9% at the level of individual patients was observed applying 13 published prognostic signatures to classify tumour regions. We partitioned genes into four heterogeneity quadrants, from which we developed and validated a reproducible robust ITH-free expression signature AUGUR that showed significant positive associations with adverse features of HCC. High AUGUR risk increased the risk of disease progression and mortality independent of established clinicopathological indices, which maintained concordance across seven cohorts. Moreover, AUGUR compared favourably to the discriminative ability, prognostic accuracy, and patient risk concordant rates of 13 published signatures. Finally, a well-calibrated predictive nomogram integrating AUGUR and tumour-node-metastasis (TNM) stage was established, which generated a numerical probability of mortality.

Conclusions

We constructed and validated an ITH-free AUGUR and nomogram that overcame sampling bias and provided reliable prognostic information for patients with HCC.

Impact and Implications

Intratumour heterogeneity (ITH) is prevalent in hepatocellular carcinoma (HCC), and is regarded as an unaddressed confounding factor for biomarker design and application. We examined the confounding effect of transcriptomic ITH in patient risk classification, and found existing molecular biomarkers of HCC were vulnerable to tumour sampling bias. We then developed an ITH-free expression biomarker (a utility gadget using RNA; AUGUR) that overcame clinical sampling bias and maintained prognostic reproducibility and generalisability across multiple HCC patient cohorts from different commercial platforms. Furthermore, we established and validated a well-calibrated nomogram based on AUGUR and tumour-node-metastasis (TNM) stage that provided an individualised prognostic information for patients with HCC.

The Use of ctDNA in the Diagnosis and Monitoring of Hepatocellular Carcinoma-Literature Review

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and is one of the leading causes of cancer-related deaths worldwide. Despite advances in medicine, it is still a cancer with a very poor prognosis. Both imaging and liver biopsy still have important limitations, especially in very small nodules and those which show atypical imaging features. In recent years, liquid biopsy and molecular analysis of tumor breakdown products have become an attractive source of new biomarkers. Patients with liver and biliary malignancies, including hepatocellular carcinoma (HCC), may greatly benefit from ctDNA testing. These patients are often diagnosed at an advanced stage of the disease, and relapses are common. Molecular analysis may indicate the best cancer treatment tailored to particular patients with specific tumor DNA mutations. Liquid biopsy is a minimally invasive technique that facilitates the early detection of cancer. This review summarizes the knowledge of ctDNA in liquid biopsy as an indicator for early diagnosis and monitoring of hepatocellular cancer.

Proteomics profiling reveals the molecular signatures and potential therapeutic targets of human nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC), a malignant tumor distinctly characterized by ethnic and geographic distribution, is highly prevalent in Southern China and Southeast Asia. However, the molecular mechanisms of NPC have not been fully revealed at the proteomic level. In this study, 30 primary NPC samples and 22 normal nasopharyngeal epithelial tissues were collected for proteomics analysis, and a relatively complete proteomics landscape of NPC was depicted for the first time. By combining differential expression analysis, differential co-expression analysis, and network analysis, potential biomarkers and therapeutic targets were identified. Some identified targets were verified by biological experiments. We found that 17-AAG, a specific inhibitor of the identified target, HSP90, could be a potential therapeutic drug for NPC. Finally, consensus clustering identified two NPC subtypes with specific molecular features. The subtypes and the related molecules were verified by an independent data set and may have different progression-free survival. The results of this study provide a comprehensive understanding of the molecular signatures of NPC and provide new perspectives and inspiration for prognostic determination and treatment of NPC.

Using cancer proteomics data to identify gene candidates for therapeutic targeting

Gene-level associations obtained from mass-spectrometry-based cancer proteomics datasets represent a resource for identifying gene candidates for functional studies. When recently surveying proteomic correlates of tumor grade across multiple cancer types, we identified specific protein kinases having a functional impact on uterine endometrial cancer cells. This previously published study provides just one template for utilizing public molecular datasets to discover potential novel therapeutic targets and approaches for cancer patients. Proteomic profiling data combined with corresponding multi-omics data on human tumors and cell lines can be analyzed in various ways to prioritize genes of interest for interrogating biology. Across hundreds of cancer cell lines, CRISPR loss of function and drug sensitivity scoring can be readily integrated with protein data to predict any gene's functional impact before bench experiments are carried out. Public data portals make cancer proteomics data more accessible to the research community. Drug discovery platforms can screen hundreds of millions of small molecule inhibitors for those that target a gene or pathway of interest. Here, we discuss some of the available public genomic and proteomic resources while considering approaches to how these could be leveraged for molecular biology insights or drug discovery. We also demonstrate the inhibitory effect of BAY1217389, a TTK inhibitor recently tested in a Phase I clinical trial for the treatment of solid tumors, on uterine cancer cell line viability.

Elevated Nuclear PHGDH Synergistically Functions with cMyc to Reshape the Immune Microenvironment of Liver Cancer

Herein, we observed that nuclear localization of phosphoglycerate dehydrogenase (PHGDH) is associated with poor prognosis in liver cancer, and Phgdh is required for liver cancer progression in a mouse model. Unexpectedly, impairment of Phgdh enzyme activity exerts a slight effect in a liver cancer model. In liver cancer cells, the aspartate kinase-chorismate mutase-tyrA prephenate dehydrogenase (ACT) domain of PHGDH binds nuclear cMyc to form a transactivation axis, PHGDH/p300/cMyc/AF9, which drives chemokine CXCL1 and IL8 gene expression. Then, CXCL1 and IL8 promote neutrophil recruitment and enhance tumor-associated macrophage (TAM) filtration in the liver, thereby advancing liver cancer. Forced cytosolic localization of PHGDH or destruction of the PHGDH/cMyc interaction abolishes the oncogenic function of nuclear PHGDH. Depletion of neutrophils by neutralizing antibodies greatly hampers TAM filtration. These findings reveal a nonmetabolic role of PHGDH with altered cellular localization and suggest a promising drug target for liver cancer therapy by targeting the nonmetabolic region of PHGDH.

SUMOylation of RNF146 results in Axin degradation and activation of Wnt/β-catenin signaling to promote the progression of hepatocellular carcinoma

Aberrant SUMOylation contributes to the progression of hepatocellular carcinoma (HCC), yet the molecular mechanisms have not been well elucidated. RING-type E3 ubiquitin ligase RNF146 is a key regulator of the Wnt/β-catenin signaling pathway, which is frequently hyperactivated in HCC. Here, it is identified that RNF146 can be modified by SUMO3. By mutating all lysines in RNF146, we found that K19, K61, K174 and K175 are the major sites for SUMOylation. UBC9/PIAS3/MMS21 and SENP1/2/6 mediated the conjugation and deconjugation of SUMO3, respectively. Furthermore, SUMOylation of RNF146 promoted its nuclear localization, while deSUMOylation induced its cytoplasmic localization. Importantly, SUMOylation promotes the association of RNF146 with Axin to accelerate the ubiquitination and degradation of Axin. Intriguingly, only UBC9/PIAS3 and SENP1 can act at K19/K175 in RNF146 and affect its role in regulating the stability of Axin. In addition, inhibiting RNF146 SUMOylation suppressed the progression of HCC both in vitro and in vivo. And, patients with higher expression of RNF146 and UBC9 have the worst prognosis. Taken together, we conclude that RNF146 SUMOylation at K19/K175 promotes its association with Axin and accelerates Axin degradation, thereby enhancing β-catenin signaling and contributing to cancer progression. Our findings reveal that RNF146 SUMOylation is a potential therapeutic target in HCC.

Genomic and transcriptomic profiling reveals key molecules in metastatic potentials and organ-tropisms of hepatocellular carcinoma

Metastasis is a landmark event for rapid postsurgical relapse and death of HCC patients. Although distinct genomic and transcriptomic profiling of HCC metastasis had been reported previously, the causal relationships of somatic mutants, mRNA levels and metastatic potentials were difficult to be established in clinic. Therefore, 11 human HCC cell lines and 7 monoclonal derivatives with definite metastatic potentials and tropisms were subjected to whole exome sequencing (WES) and whole transcriptome sequencing (WTS). TP53, MYO5A, ROS1 and ARID2 were the prominent mutants of metastatic drivers in HCC cells. During HCC clonal evaluation, TP53, MYO5A and ROS1 mutations occurred in the early stage, EXT2 and NIN in the late stage. NF1 mutant was unique in lung tropistic cell lines, RNF126 mutant in lymphatic tropistic ones. PER1, LMO2, GAS7, NR4A3 expression levels were positively associated with relapse-free survival (RFS) of HCC patients. The integrative analysis revealed 58 genes exhibited both somatic mutation and dysregulated mRNA levels in high metastatic cells. Altogether, metastatic drivers could accumulate gradually at different stages during HCC progression, some drivers might modulate HCC metastatic potentials and the others regulate metastatic tropisms.

Dominant neoantigen verification in hepatocellular carcinoma by a single-plasmid system coexpressing patient HLA and antigen

BACKGROUND

Previous studies confirmed that most neoantigens predicted by algorithms do not work in clinical practice, and experimental validations remain indispensable for confirming immunogenic neoantigens. In this study, we identified the potential neoantigens with tetramer staining, and established the Co-HA system, a single-plasmid system coexpressing patient human leukocyte antigen (HLA) and antigen, to detect the immunogenicity of neoantigens and verify new dominant hepatocellular carcinoma (HCC) neoantigens.

METHODS

First, we enrolled 14 patients with HCC for next-generation sequencing for variation calling and predicting potential neoantigens. Then, the Co-HA system was established. To test the feasibility of the system, we constructed target cells coexpressing HLA-A*11:01 and the reported KRAS G12D neoantigen as well as specific T-cell receptor (TCR)-T cells. The specific cytotoxicity generated by this neoantigen was shown using the Co-HA system. Moreover, potential HCC-dominant neoantigens were screened out by tetramer staining and validated by the Co-HA system using methods including flow cytometry, enzyme-linked immunospot assay and ELISA. Finally, antitumor test in mouse mode and TCR sequencing were performed to further evaluate the dominant neoantigen.

RESULTS

First, 2875 somatic mutations in 14 patients with HCC were identified. The main base substitutions were C>T/G>A transitions, and the main mutational signatures were 4, 1 and 16. The high-frequency mutated genes included HMCN1, TTN and TP53. Then, 541 potential neoantigens were predicted. Importantly, 19 of the 23 potential neoantigens in tumor tissues also existed in portal vein tumor thrombi. Moreover, 37 predicted neoantigens restricted by HLA-A*11:01, HLA-A*24:02 or HLA-A*02:01 were performed by tetramer staining to screen out potential HCC-dominant neoantigens. HLA-A*24:02-restricted epitope 5'-FYAFSCYYDL-3' and HLA-A*02:01-restricted epitope 5'-WVWCMSPTI-3' demonstrated strong immunogenicity in HCC, as verified by the Co-HA system. Finally, the antitumor efficacy of 5'-FYAFSCYYDL-3'-specific T cells was verified in the B-NDG-B2m^tm1Fcrn^tm1(mB2m) mouse and their specific TCRs were successfully identified.

CONCLUSION

We found the dominant neoantigens with high immunogenicity in HCC, which were verified with the Co-HA system.

A lipid metabolism-based prognostic risk model for HBV-related hepatocellular carcinoma

BACKGROUND

Up to 85% of hepatocellular carcinoma (HCC) cases in China can be attributed to infection of hepatitis B virus (HBV). Lipid metabolism performs important function in hepatocarcinogenesis of HBV-related liver carcinoma. However, limited studies have explored the prognostic role of lipid metabolism in HBV-related HCC. This study established a prognostic model to stratify HBV-related HCC based on lipid metabolisms.

METHODS

Based on The Cancer Genome Atlas HBV-related HCC samples, this study selected prognosis-related lipid metabolism genes and established a prognosis risk model by performing uni- and multi-variate Cox regression methods. The final markers used to establish the model were selected through the least absolute shrinkage and selection operator method. Analysis of functional enrichment, immune landscape, and genomic alteration was utilized to investigate the inner molecular mechanism involved in prognosis.

RESULTS

The risk model independently stratified HBV-infected patients with liver cancer into two risk groups. The low-risk groups harbored longer survival times (with P < 0.05, log-rank test). TP53, LRP1B, TTN, and DNAH8 mutations and high genomic instability occurred in high-risk groups. Low-risk groups harbored higher CD8 T cell infiltration and BTLA expression. Lipid-metabolism (including "Fatty acid metabolism") and immune pathways were significantly enriched (P < 0.05) in the low-risk groups.

CONCLUSIONS

This study established a robust model to stratify HBV-related HCC effectively. Analysis results decode in part the heterogeneity of HBV-related liver cancer and highlight perturbation of lipid metabolism in HBV-related HCC. This study's findings could facilitate patients' clinical classification and give hints for treatment selection.

Hepatitis B virus X protein promotes MAN1B1 expression by enhancing stability of GRP78 via TRIM25 to facilitate hepatocarcinogenesis

BACKGROUND

GRP78 has been implicated in hepatocarcinogenesis. However, the clinical relevance, biological functions and related regulatory mechanisms of GRP78 in hepatitis B virus (HBV)-associated hepatoma carcinoma (HCC) remain elusive.

METHODS

The association between GRP78 expression and HBV-related HCC was investigated. The effects of HBV X protein (HBX) on GRP78 and MAN1B1 expression, biological functions of GRP78 and MAN1B1 in HBX-mediated HCC cells and mechanisms related to TRIM25 on GRP78 upregulation to induce MAN1B1 expression in HBX-related HCC cells were examined.

RESULTS

GRP78 expression was correlated with poor prognosis in HBV-positive HCC. HBX increased MAN1B1 protein expression depending on GRP78, and HBX enhanced the levels of MAN1B1 to promote proliferation, migration and PI3-K/mTOR signalling pathway activation in HCC cells. GRP78 activates Smad4 via its interaction with Smad4 to increase MAN1B1 expression in HBX-expressing HCC cells. TRIM25 enhanced the stability of GRP78 by inhibiting its ubiquitination. HBX binds to GRP78 and TRIM25 and accelerates their interaction of GRP78 and TRIM25, leading to an increase in GRP78 expression.

CONCLUSIONS

HBX enhances the stability of GRP78 through TRIM25 to increase the expression of MAN1B1 to facilitate tumorigenesis, and we provide new insights into the molecular mechanisms underlying HBV-induced malignancy.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol

Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.

Characterization of heterogeneous metabolism in hepatocellular carcinoma identifies new therapeutic target and treatment strategy

Background

Metabolic reprogramming is a well-known hallmark of cancer. Systematical identification of clinically relevant metabolic subtypes of Hepatocellular carcinoma (HCC) is critical to understand tumor heterogeneity and develop efficient treatment strategies.

Methods

We performed an integrative analysis of genomic, transcriptomic, and clinical data from an HCC patient cohort in The Cancer Genome Atlas (TCGA).

Results

Four metabolic subtypes were defined: mHCC1, mHHC2, mHCC3, and mHCC4. These subtypes had distinct differences in mutations profiles, activities of metabolic pathways, prognostic metabolism genes, and immune features. The mHCC1 was associated with poorest outcome and was characterized by extensive metabolic alterations, abundant immune infiltration, and increased expression of immunosuppressive checkpoints. The mHHC2 displayed lowest metabolic alteration level and was associated with most significant improvement in overall survival in response to high CD8+ T cell infiltration. The mHHC3 was a “cold-tumor” with low immune infiltration and few metabolic alterations. The mHCC4 presented a medium degree of metabolic alteration and high CTNNB1 mutation rate. Based on our HCC classification and in vitro study, we identified palmitoyl-protein thioesterase 1 (PPT1) was a specific prognostic gene and therapeutic target for mHCC1.

Conclusion

Our study highlighted mechanistic differences among metabolic subtypes and identified potential therapeutic targets for subtype-specific treatment strategies targeting unique metabolic vulnerabilities. The immune heterogeneities across metabolic subtypes may help further clarify the association between metabolism and immune environment and guide the development of novel strategies through targeting both unique metabolic vulnerabilities and immunosuppressive triggers.

Integrated proteomic and phosphoproteomic analysis for characterization of colorectal cancer

Proteins and translationally modified proteins like phosphoproteins have essential regulatory roles in tumorigenesis. This study attempts to elucidate the dysregulated proteins driving colorectal cancer (CRC). To explore the differential proteins, we performed iTRAQ labeling proteomics and TMT labeling phosphoproteomics analysis of CRC tissues and adjacent non-cancerous tissues. The functions of quantified proteins were analyzed using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Subcellular localization analysis. Depending on the results, we identified 330 differential proteins and 82 phosphoproteins in CRC. GO and KEGG analyses demonstrated that protein changes were primarily associated with regulating biological and metabolic processes through binding to other molecules. Co-expression relationships between proteomic and phosphoproteomic analysis revealed that TMC5, SMC4, SLBP, VSIG2, and NDRG2 were significantly dysregulated differential proteins. Additionally, based on the predicted co-expression proteins, we identified that the stem-loop binding protein (SLBP) was up-regulated in CRC cells and promoted the proliferation and migration of CRC. This study reports an integrated proteomic and phosphoproteomic analysis of CRC to discern the functional impact of protein alterations and provides a candidate diagnostic biomarker or therapeutic target for CRC. SIGNIFICANCE: Combining one or more high-throughput omics technologies with bioinformatics to analyze biological samples and explore the links between biomolecules and their functions can provide more comprehensive and multi-level insights for disease mechanism research. Proteomics, phosphoproteomics, metabolomics and their combined analysis play an important role in the auxiliary diagnosis, the discovery of biomarkers and novel therapeutic targets for colorectal cancer. In this integrated proteomic and phosphoproteomic analysis, we identified proteins and phosphoproteins in colorectal cancer tissue and analyzed potential mechanisms contributing to progression in colorectal cancer. The results of this study provide a foundation to focus future experiments on the contribution of altered protein and phosphorylation patterns to prevention and treatment of colorectal cancer.

An immune-related signature for optimizing prognosis prediction and treatment decision of hepatocellular carcinoma

BACKGROUND

An immune-related gene signature (IGS) was established for discriminating prognosis, predicting benefit of immunotherapy, and exploring therapeutic options in hepatocellular carcinoma (HCC).

METHODS

Based on Immune-related hub genes and The Cancer Genome Atlas (TCGA) LIHC dataset (n = 363), an immune-related gene signature (IGS) was established by least absolute shrinkage and selection operator (LASSO) analysis. The prognostic significance and clinical implications of IGS were verified in International Cancer Genome Consortium (ICGC) and Chinese HCC (CHCC) cohorts. The molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in IGS-defined subgroups were analyzed. In addition, by leveraging the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing datasets, we determined the potential therapeutic agents for high IGS-risk patients.

RESULTS

The IGS was constructed based on 8 immune-related hub genes with individual coefficients. The IGS risk model could robustly predict the survival of HCC patients in TCGA, ICGC, and CHCC cohorts. Compared with 4 previous established immune genes-based signatures, IGS exhibited superior performance in survival prediction. Additionally, for immunological characteristics and enriched pathways, a low-IGS score was correlated with IL-6/JAK/STAT3 signaling, inflammatory response and interferon α/γ response pathways, low TP53 mutation rate, high infiltration level, and more benefit from ICI therapy. In contrast, high IGS score manifested an immunosuppressive microenvironment and activated aggressive pathways. Finally, by in silico screening potential compounds, vindesine, ispinesib and dasatinib were identified as potential therapeutic agents for high-IGS risk patients.

CONCLUSIONS

This study developed a robust IGS model for survival prediction of HCC patients, providing new insights into integrating tailored risk stratification with precise immunotherapy and screening potentially targeted agents.

Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.

ACR-Based Probe for the Quantitative Profiling of Histidine Reactivity in the Human Proteome

The quantitative profiling of residue reactivity in proteins promotes the discovery of covalent druggable targets for precise therapy. Histidine (His) residues, accounting for more than 20% of the active sites in enzymes, have not been systematically characterized for their reactivity, due to lack of labeling probes. Herein, we report a chemical proteomics platform for the site-specific quantitative analysis of His reactivity by combination of acrolein (ACR) labeling and reversible hydrazine chemistry enrichment. Based on this platform, in-depth characterization of His residues was conducted for the human proteome, in which the rich content of His residues (>8200) was quantified, including 317 His hyper-reactive residues. Intriguingly, it was observed that the hyper-reactive residues were less likely to be the sites for phosphorylation, and the possible mechanism of this antagonistic effect still needs to be evaluated in further research. Based on the first comprehensive map of His residue reactivity, many more residues could be adopted as the bindable sites to disrupt the activities of a diverse number of proteins; meanwhile, ACR derivatives could also be used as a novel reactive warhead in the development of covalent inhibitors.

CD36+ cancer-associated fibroblasts provide immunosuppressive microenvironment for hepatocellular carcinoma via secretion of macrophage migration inhibitory factor

Hepatocellular carcinoma (HCC) is an immunotherapy-resistant malignancy characterized by high cellular heterogeneity. The diversity of cell types and the interplay between tumor and non-tumor cells remain to be clarified. Single cell RNA sequencing of human and mouse HCC tumors revealed heterogeneity of cancer-associated fibroblast (CAF). Cross-species analysis determined the prominent CD36⁺ CAFs exhibited high-level lipid metabolism and expression of macrophage migration inhibitory factor (MIF). Lineage-tracing assays showed CD36⁺CAFs were derived from hepatic stellate cells. Furthermore, CD36 mediated oxidized LDL uptake-dependent MIF expression via lipid peroxidation/p38/CEBPs axis in CD36⁺ CAFs, which recruited CD33⁺myeloid-derived suppressor cells (MDSCs) in MIF- and CD74-dependent manner. Co-implantation of CD36⁺ CAFs with HCC cells promotes HCC progression in vivo. Finally, CD36 inhibitor synergizes with anti-PD-1 immunotherapy by restoring antitumor T-cell responses in HCC. Our work underscores the importance of elucidating the function of specific CAF subset in understanding the interplay between the tumor microenvironment and immune system.

Targeting PYCR2 inhibits intraperitoneal metastatic tumors of mouse colorectal cancer in a proline-independent approach

Whether proline deficiency is a metabolic vulnerability in colorectal tumors is unknown. The aim of this study was to investigate the effects of proline metabolism-related genes and exogenous proline on the progression of colorectal cancer (CRC). We aimed to further clarify the role of pyrroline-5-carboxylate reductase (PYCR) 2, a key enzyme of proline synthesis, in the regulation of colorectal intraperitoneal metastatic tumors. This study was carried out based on The Cancer Genome Atlas (TCGA) data, database analysis, single-cell functional analysis, tissue microarray, cell experiments, and animal models. We found that, PYCR2 mRNA and protein levels were upregulated in CRC. The mRNA level of PYCR2 was closely related to the prognosis and tumor metastasis of CRC patients. The upregulated PYCR2 expression was at least partly due to low promoter methylation levels. The nomogram constructed based on PYCR2 expression and clinical characteristics of CRC showed good accuracy in predicting lymph node metastasis. Pycr2 knockdown inhibited epithelial-mesenchymal transition (EMT) of mouse CRC cells. Proline supplementation did not rescue the inhibition of mouse CRC cell proliferation and migration by Pycr2 knockdown. Proline supplementation also did not rescue the suppression of subcutaneous tumors and intraperitoneal metastatic tumors in mice by Pycr2 knockdown. PYCR2 co-expressed genes in TCGA-CRC were enriched in epigenetic modification-related biological processes and molecular functions. Four small molecules with the lowest binding energy to the PYCR2 protein were identified. Collectively, Pycr2 knockdown inhibited mouse CRC progression in a proline-independent approach. PYCR2 may be a promising tumor metastasis predictor and therapeutic target in CRC.

A signature based on NKG2D ligands to predict the recurrence of hepatocellular carcinoma after radical resection

INTRODUCTION

Due to the high recurrence, the HCC prognosis remains poor. Yet, the biomarkers for predicting the recurrence of high-risk patients are currently lacking. We aimed to develop a signature to predict the recurrence of HCC based on NKG2D ligands.

METHODS

The multivariate Cox proportional hazards regression was used to select recurrence-related variables of NKG2D ligands in HCC patients from The Cancer Genome Atlas (TCGA). HCC patients from the OEP000321 dataset and Guilin cohort were used to validate the predictive signature. The mRNA expression of NKG2D ligands was measured by QRT-PCR. Immunohistochemistry analysis of HCC tissue microarray samples was used to identify the expression of NKG2D ligands.

RESULTS

In this study, NKG2D ligands expression in the mRNA and protein level was both abnormally expressed in HCC and associated with recurrence-free survival (RFS). Then, the recurrence-related variables of NKG2D ligands in HCC were selected by the multivariate Cox proportional hazards regression. Among the eight NKG2D ligands, MICA (HR = 1.347; 95% CI = 1.012-1.793; p = 0.041), ULBP3 (HR = 0.453; 95% CI = 0.231-0.889; p = 0.021) and ULBP5 (HR = 3.617; 95% CI = 1.819-7.194; p < 0.001) were significantly correlated with RFS in the TCGA-LIHC cohort. Then, the signature was constructed by the three NKG2D ligands. The predictive effectiveness of this signature was also validated in the OEP000321 dataset and Guilin cohort. Further, HCC patients were classified into low-risk and high-risk subgroups by the predictive score. Compared with the low-risk group, the high-risk group had poor RFS in both training and validation cohorts. Importantly, compared with the low-risk patients with the G1-G2 stage, the levels of infiltrated NK-activated cells and NKG2D expression were both lower in the high-risk patients.

CONCLUSIONS

The signature based on MICA, ULBP3, and ULBP5 could predict HCC recurrence.

Gene-based cancer-testis antigens as prognostic indicators in hepatocellular carcinoma

Cancer/testis antigens (CTAs) are reproductive tissue-restricted genes, frequently ectopic expressed in tumors. CTA genes associate with a poor prognosis in some solid tumors, due to their potential roles in the tumorigenesis and progression. However, whether CTAs relate with hepatocellular carcinoma (HCC) remains unclear. In this study, the prognostic signatures based on CTA genes were investigated and validated in three cohorts including Chinese HCC patients with hepatitis B virus infection (CHCC-HBV), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cohorts. Univariate, LASSO, and multivariate Cox regression analyses were used to screen prognostic genes and develop the prognostic gene signature. A prognosis model was established with six CTA genes (SSX1, CTCFL, OIP5, CEP55, NOL4, and TPPP2) in CHCC-HBV cohort, and further validated in the ICGC and TCGA cohorts. The CTA signature was an essential prognostic predictor independent of other clinical pathological factors. High-risk group exhibited up-regulated cell cycle-related and tumor-related pathways and more M0 macrophage, activated mast cell, activated memory CD4⁺ T cell, and memory B cell infiltration. Furthermore, CTA signature correlated with the sensitivity to multiple chemotherapy drugs. Our results highlighted that the CTA gene profiling was a prognostic assessment tool for HCC patients.

An angiogenesis-related three-long non-coding ribonucleic acid signature predicts the immune landscape and prognosis in hepatocellular carcinoma

The tumour microenvironment is a key determinant of the efficacy of immunotherapy. Angiogenesis is closely linked to tumour immunity. We aimed to screen long non-coding ribonucleic acids (lncRNAs) associated with angiogenesis to predict the prognosis of individuals with hepatocellular carcinoma (HCC) and characterise the tumour immune microenvironment (TIME). Patient data, including transcriptome and clinicopathological parameters, were retrieved from The Cancer Genome Atlas database. Moreover, co-expression algorithm was utilized to obtain angiogenesis-related lncRNAs. Additionally, survival-related lncRNAs were identified using Cox regression and the least absolute shrinkage and selection operator algorithm, which aided in constructing an angiogenesis-related lncRNA signature (ARLs). The ARLs was validated using Kaplan-Meier method, time-dependent receiver operating characteristic analyses, and Cox regression. Additionally, an independent external HCC dataset was used for further validation. Then, gene set enrichment analysis, immune landscape, and drug sensitivity analyses were implemented to explore the role of the ARLs. Finally, cluster analysis divided the entire HCC dataset into two clusters to distinguish different subtypes of TIME. This study provides insight into the involvement of angiogenesis-associated lncRNAs in predicting the TIME characteristics and prognosis for individuals with HCC. Furthermore, the developed ARLs and clusters can predict the prognosis and TIME characteristics in HCC, thereby aiding in selecting the appropriate therapeutic strategies involving immune checkpoint inhibitors and targeted drugs.

Fructose-1,6-bisphosphatase 1 dephosphorylates IκBα and suppresses colorectal tumorigenesis

Emerging evidence demonstrates that some metabolic enzymes that phosphorylate soluble metabolites can also phosphorylate a variety of protein substrates as protein kinases to regulate cell cycle, apoptosis and many other fundamental cellular processes. However, whether a metabolic enzyme dephosphorylates protein as a protein phosphatase remains unknown. Here we reveal the gluconeogenic enzyme fructose 1,6-biphosphatase 1 (FBP1) that catalyzes the hydrolysis of fructose 1,6-bisphosphate (F-1,6-BP) to fructose 6-phosphate (F-6-P) as a protein phosphatase by performing a high-throughput screening of metabolic phosphatases with molecular docking followed by molecular dynamics (MD) simulations. Moreover, we identify IκBα as the substrate of FBP1-mediated dephosphorylation by performing phosphoproteomic analysis. Mechanistically, FBP1 directly interacts with and dephosphorylates the serine (S) 32/36 of IκBα upon TNFα stimulation, thereby inhibiting NF-κB activation. MD simulations indicate that the catalytic mechanism of FBP1-mediated IκBα dephosphorylation is similar to F-1,6-BP dephosphorylation, except for higher energetic barriers for IκBα dephosphorylation. Functionally, FBP1-dependent NF-κB inactivation suppresses colorectal tumorigenesis by sensitizing tumor cells to inflammatory stresses and preventing the mobilization of myeloid-derived suppressor cells. Our finding reveals a previously unrecognized role of FBP1 as a protein phosphatase and establishes the critical role of FBP1-mediated IκBα dephosphorylation in colorectal tumorigenesis.

The Liver Cancer Immune Microenvironment: Emerging Concepts for Myeloid Cell Profiling with Diagnostic and Therapeutic Implications

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide [...].

Integrative phosphoproteomics defines two biologically distinct groups of KMT2A rearranged acute myeloid leukaemia with different drug response phenotypes

Acute myeloid leukaemia (AML) patients harbouring certain chromosome abnormalities have particularly adverse prognosis. For these patients, targeted therapies have not yet made a significant clinical impact. To understand the molecular landscape of poor prognosis AML we profiled 74 patients from two different centres (in UK and Finland) at the proteomic, phosphoproteomic and drug response phenotypic levels. These data were complemented with transcriptomics analysis for 39 cases. Data integration highlighted a phosphoproteomics signature that define two biologically distinct groups of KMT2A rearranged leukaemia, which we term MLLGA and MLLGB. MLLGA presented increased DOT1L phosphorylation, HOXA gene expression, CDK1 activity and phosphorylation of proteins involved in RNA metabolism, replication and DNA damage when compared to MLLGB and no KMT2A rearranged samples. MLLGA was particularly sensitive to 15 compounds including genotoxic drugs and inhibitors of mitotic kinases and inosine-5-monosphosphate dehydrogenase (IMPDH) relative to other cases. Intermediate-risk KMT2A-MLLT3 cases were mainly represented in a third group closer to MLLGA than to MLLGB. The expression of IMPDH2 and multiple nucleolar proteins was higher in MLLGA and correlated with the response to IMPDH inhibition in KMT2A rearranged leukaemia, suggesting a role of the nucleolar activity in sensitivity to treatment. In summary, our multilayer molecular profiling of AML with poor prognosis and KMT2A-MLLT3 karyotypes identified a phosphoproteomics signature that defines two biologically and phenotypically distinct groups of KMT2A rearranged leukaemia. These data provide a rationale for the potential development of specific therapies for AML patients characterised by the MLLGA phosphoproteomics signature identified in this study.

High-throughput sequencing approach for the identification of lncRNA biomarkers in hepatocellular carcinoma and revealing the effect of ZFAS1/miR-150-5p on hepatocellular carcinoma progression

Aims

To screen abnormal lncRNAs and diagnostic biomarkers in the progression of hepatocellular carcinoma through high-throughput sequencing and explore the underlying mechanisms of abnormal lncRNAs in the progression of hepatocellular carcinoma.

Methods

The transcriptome sequencing was used to analyze the RNA expression profile and identify differentially expressed RNAs. Hub lncRNAs were screened by combining (WGCNA, ceRNA regulatory network, PPI, GO and KEGG analyses, Kaplan-Meier curve analysis, Cox analysis, risk model construction and qPCR). Thereafter, the correlation between the expression of hub lncRNAs and tumor clinicopathological parameters was analyzed, and the hub lncRNAs were analyzed by GSEA. Finally, the effects of hub RNAs on the proliferation, migration and invasion of HepG2 cells were investigated in vitro.

Results

Compared with the control group, a total of 610 lncRNAs, 2,593 mRNAs and 26 miRNAs were screened in patients with hepatocellular carcinoma. Through miRNA target prediction and WGCNA, a ceRNA was constructed, comprising 324 nodes and 621 edges. Enrichment analysis showed that mRNAs in ceRNA were involved mainly in cancer development progression. Then, the ZFAS1/miR-150-5p interaction pair was screened out by Kaplan Meier curve analysis, Cox analysis and qPCR analysis. Its expression was related to tumor stage, TNM stage and patient age. ROC curve analysis showed that it has a good predictive value for the risk of hepatocellular carcinoma. GSEA showed that ZFAS1 was also enriched in the regulation of immune response, cell differentiation and proliferation. Loss-of-function experiments revealed that ZFAS1 inhibition could remarkably suppress HepG2 cell proliferation, migration and invasion in vitro. Bioinformatic analysis and luciferase reporter assays revealed that ZFAS1 directly interacted with miR-150-5p. Rescue experiments showed that a miR-150-5p inhibitor reversed the cell proliferation, migration and invasion functions of ZFAS1 knockdown in vitro.

Conclusion

ZFAS1 is associated with the malignant status and prognosis of patients with hepatocellular carcinoma, and the ZFAS1/miR-150-5p axis is involved in hepatocellular carcinoma progression.

OSppc: A web server for online survival analysis using proteome of pan-cancers

Prognostic biomarker, as a feasible and objective indicator, is valuable in the assessment of cancer risk. With the development of high-throughput sequencing technology, the screening of prognostic biomarkers has become easy, but it is difficult to screen prognostic markers based on proteomic data. In this study we developed a tool named Online consensus Survival analysis web server based on Proteome of Pan-cancers, abbreviated as OSppc, to evaluate the prognostic values of protein biomarkers. >8000 cancer cases with proteomic data, transcriptomic data and clinical follow-up information were collected from TCGA and CPTAC. 14,038 proteins (including proteins and their phosphorylated forms) analyzed by reverse-phase protein arrays and mass spectrometry in 33 types of cancers were collected. In OSppc, three analysis modules are provided, including Survival Analysis, Differential Analysis and Correlation Analysis. Survival analysis module exhibits HR with 95% CI and KM curves with log-rank p value of protein and mRNA levels of input genes. Differential analysis module shows the box plots of protein expression levels in different tissues. Correlation analysis module provides scatter plot with pearson's and spearman's correlation coefficient of the protein and its corresponding mRNA. OSppc can be accessed at http://bioinfo.henu.edu.cn/Protein/OSppc.html. SIGNIFICANCE: OSppc can analyze the association between protein, mRNA and prognosis, the correlation between proteome data and gene expression profiles, the differential expression of proteome data between subgroups such as normal and cancer as well. OSppc is registration-free and very valuable to evaluate the prognostic potency of protein of interests. OSppc is very valuable for researchers and clinicians to screen, develop and validate potential protein prognostic biomarkers in pan-cancers, and offers the opportunities to investigate the clinical important functional genes and therapeutic targets of cancers.

Clinical and biological significance of RNA N6-methyladenosine regulators in Alzheimer disease

RNA N6-methyladenosine (m6A) regulators are essential for a variety of biological functions, such as early development, viral infections, and cancer. However, their roles in Alzheimer disease (AD) are still not very clear. Here, 16 significant m6A regulators were identified using difference analysis between AD patients and non-demented controls based on the GSE132903 dataset from the Gene Expression Omnibus database. Using these 16 m6A regulators, a nomogram model was established to predict the prevalence of AD. We found that patients could obtain a good clinical benefit based on this model. In addition, we revealed 2 distinct m6A patterns and 2 distinct m6A gene patterns in AD and demonstrated their prognostic and risk assessment significance. This present work comprehensively evaluated the functions of m6A regulators in the diagnosis and subtype classification of AD. These results suggested they have potential prognostic and risk assessment significance in AD.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Prognosis and Personalized Treatment Prediction in Different Mutation-Signature Hepatocellular Carcinoma

Introduction

Mutation patterns have been extensively explored to decipher the etiologies of hepatocellular carcinoma (HCC). However, the study and potential clinical role of mutation patterns to stratify high-risk patients and optimize precision therapeutic strategies remain elusive in HCC.

Methods

Using exon-sequencing data in public (n=362) and in-house (n=30) cohorts, mutation signatures were extracted to decipher relationships with the etiology and prognosis in HCC. The proteomics (n=159) and cell-line transcriptome data (n=1019) were collected to screen the implication of sensitive drugs. A novel multi-step machine-learning framework was then performed to construct a classification predictor, including recognizing stable reversed gene pairs, establishing a robust prediction model, and validating the robustness of the predictor in five independent cohorts (n=900).

Results

Two heterogeneous mutation signature clusters were identified, and a high-risk prognosis cluster was recognized for further analysis. Notably, mutation signature cluster 1 (MSC1) was featured by activated anti-tumor immune and metabolism dysfunctional states, higher genomic instability (high TMB, SNV neoantigen, indel neoantigens, and total neoantigens), and a dismal prognosis. Notably, MSC performed as an independent risk factor than clinical traits (eg, stage, vascular invasion). Additionally, afatinib and canertinib were recognized which might have potential therapeutic implications in MSC1, and the targets of these drugs presented a higher expression in both gene and protein levels in HCC.

Discussion

Our studies may provide a promising platform for improving prognosis and tailoring therapy in HCC.

Pien Tze Huang regulates phosphorylation of metabolic enzymes in mice of hepatocellular carcinoma

The Chinese medicine formula Pien Tze Huang (PZH) has been applied to the treatment of various diseases, the reported anti-tumor mechanisms included regulation of inflammation-associated cytokine secretion and cancer growth pathways. However, the potential influence of PZH on tumor metabolism remains unclear. This study aimed to investigate the global effect of PZH on hepatocellular carcinoma (HCC) compared with the anti-tumor agent sorafenib based on tandem mass tag (TMT) label proteomic and phosphoproteomic analysis in addition to parallel reaction monitoring (PRM) verification. It was observed that PZH could inhibit tumor weight by 59-69% in different concentrations. TMT proteomic studies indicated that fructose/mannose metabolism and glucagon signaling pathway in PZH group, and arachidonic acid metabolism and PPAR signaling pathway in sorafenib group, were significantly enriched, while glycolysis/gluconeogenesis pathway was found to be enriched remarkably both in PZH and sorafenib groups in TMT phosphoproteomic study. PRM verification further indicated that both PZH and sorafenib could down-regulate phosphorylations of the glycolytic enzymes phosphofructokinases 1, fructose-bisphosphate Aldolase A, phosphoglycerate mutase 2 and lactate dehydrogenase A chain, while phosphorylations of long chain fatty acid CoA ligase in fatty acid activation and acetyl-coenzyme A synthetase in glycolysis were significantly inhibited by PZH and sorafenib, respectively. This study proposed that PZH shared a similar anti-tumor mechanism of metabolic regulation to sorafenib, but differed in the regulation of some metabolic nodes. This is the first time to uncover the relationship between the anti-tumor effect of PZH and metabolic related enzymes, which distinguished from the known mechanisms of PZH. These data provided the potential molecular basis for PZH acting as a therapeutic drug for HCC, and offered cues of manipulation on Warburg effect under the treatment of PZH.

Development and validation of a clinical-radiomics model to predict recurrence for patients with hepatocellular carcinoma after curative resection

PURPOSE

Recurrence is the leading cause of death in hepatocellular carcinoma (HCC) patients with curative resection. In this study, we aimed to develop a preoperative predictive model based on high-throughput radiomics features and clinical factors for prediction of long- and short-term recurrence for these patients.

METHODS

A total of 270 patients with HCC who were followed up for at least 5 years after curative hepatectomy between June 2014 and December 2017 were enrolled in this retrospective study. Regions of interest were manually delineated in preoperative T2-weighted images using ITK-SNAP software on each HCC tumor slice. A total of 1197 radiomics features were extracted, and the recursive feature elimination method based on logistic regression was used for radiomics signature building. Tenfold cross-validation was applied for model development. Nomograms were constructed and assessed by calibration plot, which compares nomogram-predicated probability with observed outcome. Receiver-operating characteristic was then generated to evaluate the predictive performance of the model in the development and test cohorts.

RESULTS

The 10 most recurrence-free survival-related radiomics features were selected for the radiomics signatures. A multiparametric clinical-radiomics model combining albumin and radiomics score for recurrence prediction was further established. The integrated model demonstrated good calibration and satisfactory discrimination, with the area under the curve (AUC) of 0.864, 95% CI 0.842-0.903, sensitivity of 0.889, and specificity of 0.644 in the test set. Calibration curve showed good agreement concerning 5-year recurrence risk predicted by the nomogram. In addition, the AUC of 1-, 2-, 3-, and 4-year recurrence was 0.935 (95% CI 0.836-1.000), 0.861 (95% CI 0.723-0.999), 0.878 (95% CI 0.762-0.994), and 0.878 (95% CI 0.762-0.994) in the test set, respectively.

CONCLUSIONS

The clinical-radiomics model integrating radiomics features and clinical factors can improve recurrence predictions beyond predictions made using clinical factors or radiomics features alone. Our clinical-radiomics model is a valid method to predict recurrence that should improve preoperative prognostic performance and allow more individualized treatment decisions.

A dataset resource for clinically associated phosphosites in hepatocellular carcinoma

Phosphorylation is one of the most common post-translational modifications (PTMs) and is closely related to protein activity and function, playing a critical role during cancer development. Quantitative phosphoproteomic strategies have been widely used to study the underlying mechanisms of cancer progression or drug resistance. In this report, we analyzed the association of phosphosite levels originated from our previously reported proteogenomic study in hepatocellular carcinoma (HCC) with clinical parameters, including prognosis, recurrence, and Tumor-Node-Metastasis (TNM) stages. By using both the log-rank test and univariate Cox proportional hazards regression analysis, we found that the abundance levels of 1712 phosphosites were associated with prognosis and those of 393 phosphosites associated with recurrence. Besides, 692 phosphosites had different abundance levels among TNM stages (I, II, III+IV) by Analysis of Variance (ANOVA) test. Gene ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using proteins with these statistically significant phosphosites. In conclusion, we provided a dataset resource for clinically associated phosphosites in HCC, which may be beneficial to liver cancer related basic research.

Proteogenomic characterization of cholangiocarcinoma

BACKGROUND AND AIMS

Cholangiocarcinoma (CCA) is a highly heterogeneous cancer with limited understanding and few effective therapeutic approaches. We aimed at providing a proteogenomic CCA characterization to inform biological processes and treatment vulnerabilities.

APPROACH AND RESULTS

Integrative genomic analysis with functional validation uncovered biological perturbations downstream of driver events including DPCR1 , RBM47 mutations, SH3BGRL2 copy number alterations, and FGFR2 fusions in CCA. Proteomic clustering identified three subtypes with distinct clinical outcomes, molecular features, and potential therapeutics. Phosphoproteomics characterized targetable kinases in CCA, suggesting strategies for effective treatment with CDK and MAPK inhibitors. Patients with CCA with HBV infection showed increased antigen processing and presentation (APC) and T cell infiltration, conferring a favorable prognosis compared with those without HBV infection. The characterization of extrahepatic CCA recommended the feasible application of vascular endothelial-derived growth factor inhibitors. Multiomics profiling presented distinctive molecular characteristics of the large bile duct and the small bile duct of intrahepatic CCA. The immune landscape further revealed diverse tumor immune microenvironments, suggesting immune subtypes C1 and C5 might benefit from immune checkpoint therapy. TCN1 was identified as a potential CCA prognostic biomarker, promoting cell growth by enhancing vitamin B12 metabolism.

CONCLUSIONS

We characterized the proteogenomic landscape of 217 CCAs with 197 paired normal adjacent tissues and identified their subtypes and potential therapeutic targets. The multiomics analyses with other databases and some functional validations have indicated strategies regarding the clinical, biological, and therapeutic approaches to the management of CCA.