Hypoxia Molecular Characterization in Hepatocellular Carcinoma Identifies One Risk Signature and Two Nomograms for Clinical Management

Single-cell transcriptome analysis profiling lymphatic invasion-related TME in colorectal cancer

Lymphatic invasion (LI) is extremely aggressive and induces worse prognosis among patients with colorectal cancer (CRC). Thus, it is critical to characterize the cellular and molecular mechanisms underlying LI in order to establish novel and efficacious therapeutic targets that enhance the prognosis of CRC patients. RNA-seq data, clinical and survival information of colon adenocarcinoma (COAD) patients were obtained from the TCGA database. In addition, three scRNA-seq datasets of CRC patients were acquired from the GEO database. Data analyses were conducted with the R packages. We assessed the tumor microenvironment (TME) differences between LI+ and LI- based scRNA-seq data, LI+ cells exhibited augmented abundance of immunosuppression and invasive subset. Marked extracellular matrix network activation was also observed in LI+ cells within SPP1+ macrophages. We revealed that an immunosuppressive and pro-angiogenic TME strongly enhanced LI, as was evidenced by the CD4+ Tregs, CD8+ GZMK+, SPP1+ macrophages, e-myCAFs, and w-myCAFs subcluster infiltrations. Furthermore, we identified potential LI targets that influenced tumor development, metastasis, and immunotherapeutic response. Finally, a novel LIRS model was established based on the expression of 14 LI-related signatures, and in the two testing cohorts, LIRS was also proved to have accurate prognostic predictive ability. In this report, we provided a valuable resource and extensive insights into the LI of CRC. Our conclusions can potentially benefit the establishment of highly efficacious therapeutic targets as well as diagnostic biomarkers that improve patient outcomes.

Integrative pan-cancer analysis of cuproplasia-associated genes for the genomic and clinical characterization of 33 tumors

BACKGROUND

The molecular mechanisms driving tumorigenesis have continually been the focus of researchers. Cuproplasia is defined as copper-dependent cell growth and proliferation, including its primary and secondary roles in tumor formation and proliferation through signaling pathways. In this study, we analyzed the differences in the expression of cuproplasia-associated genes (CAGs) in pan-cancerous tissues and investigated their role in immune-regulation and tumor prognostication.

METHODS

Raw data from 11,057 cancer samples were acquired from multiple databases. Pan-cancer analysis was conducted to analyze the CAG expression, single-nucleotide variants, copy number variants, methylation signatures, and genomic signatures of micro RNA (miRNA)-messenger RNA (mRNA) interactions. The Genomics of Drug Sensitivity in Cancer and the Cancer Therapeutics Response Portal databases were used to evaluate drug sensitivity and resistance against CAGs. Using single-sample Gene Set Enrichment Analysis (ssGSEA) and Immune Cell Abundance Identifier database, immune cell infiltration was analyzed with the ssGSEA score as the standard.

RESULTS

Aberrantly expressed CAGs were found in multiple cancers. The frequency of single-nucleotide variations in CAGs ranged from 1% to 54% among different cancers. Furthermore, the correlation between CAG expression in the tumor microenvironment and immune cell infiltration varied among different cancers. ATP7A and ATP7B were negatively correlated with macrophages in 16 tumors including breast invasive carcinoma and esophageal carcinoma, while the converse was true for MT1A and MT2A . In addition, we established cuproplasia scores and demonstrated their strong correlation with patient prognosis, immunotherapy responsiveness, and disease progression ( P  <0.05). Finally, we identified potential candidate drugs by matching gene targets with existing drugs.

CONCLUSIONS

This study reports the genomic characterization and clinical features of CAGs in pan-cancers. It helps clarify the relationship between CAGs and tumorigenesis, and may be helpful in the development of biomarkers and new therapeutic agents.

Comprehensive machine learning-generated classifier identifies pro-metastatic characteristics and predicts individual treatment in pancreatic cancer: A multicenter cohort study based on super-enhancer profiling

Rationale: Accumulating evidence illustrated that the reprogramming of the super-enhancers (SEs) landscape could promote the acquisition of metastatic features in pancreatic cancer (PC). Given the anatomy-based TNM staging is limited by the heterogeneous clinical outcomes in treatment, it is of great clinical significance to tailor individual stratification and to develop alternative therapeutic strategies for metastatic PC patients based on SEs. Methods: In our study, ChIP-Seq analysis for H3K27ac was performed in primary pancreatic tumors (PTs) and hepatic metastases (HMs). Bootstrapping and univariate Cox analysis were implemented to screen prognostic HM-acquired, SE-associated genes (HM-SE genes). Then, based on 1705 PC patients from 14 multicenter cohorts, 188 machine-learning (ML) algorithm integrations were utilized to develop a comprehensive super-enhancer-related metastatic (SEMet) classifier. Results: We established a novel SEMet classifier based on 38 prognostic HM-SE genes. Compared to other clinical traits and 33 published signatures, the SEMet classifier possessed robust and powerful performance in predicting prognosis. In addition, patients in the SEMetlow subgroup owned dismal survival rates, more frequent genomic alterations, and more activated cancer immunity cycle as well as better benefits in immunotherapy. Remarkably, there existed a tight correlation between the SEMetlow subgroup and metastatic phenotypes of PC. Among 18 SEMet genes, we demonstrated that E2F7 may promote PC metastasis through the upregulation of TGM2 and DKK1. Finally, after in silico screening of potential compounds targeted SEMet classifier, results revealed that flumethasone could enhance the sensitivity of metastatic PC to routine gemcitabine chemotherapy. Conclusion: Overall, our study provided new insights into personalized treatment approaches in the clinical management of metastatic PC patients.

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.

CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma

Introduction

Mounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains largely unexplored in hepatocellular carcinoma (HCC).

Methods

A total of 3,817 immune cells and 1,750 HCC patients of 15 independent public datasets were retrieved. The Seurat and Monocle algorithms were used to depict T cell evolution, and nonnegative matrix factorization (NMF) was further applied to identify the molecular classification. Subsequently, the prognosis, biological characteristics, genomic variations, and immune landscape among distinct clusters were decoded. The clinical efficacy of multiple treatment approaches was further investigated.

Results

According to trajectory gene expression, three heterogeneous clusters with different clinical outcomes were identified. C2, with a more advanced pathological stage, presented the most dismal prognosis relative to C1 and C3. Eight independent external cohorts validated the robustness and reproducibility of the three clusters. Further explorations elucidated C1 to be characterized as lipid metabolic HCC, and C2 was referred to as cell-proliferative HCC, whereas C3 was defined as immune inflammatory HCC. Moreover, C2 also displayed the most conspicuous genomic instability, and C3 was deemed as “immune-hot”, having abundant immune cells and an elevated expression of immune checkpoints. The assessments of therapeutic intervention suggested that patients in C1 were suitable for transcatheter arterial chemoembolization treatment, and patients in C2 were sensitive to tyrosine kinase inhibitors, while patients in C3 were more responsive to immunotherapy. We also identified numerous underlying therapeutic agents, which might be conducive to clinical transformation in the future.

Conclusions

Our study developed three clusters with distinct characteristics based on immune cell evolutions. For specifically stratified patients, we proposed individualized treatment strategies to improve the clinical outcomes and facilitate the clinical management.

Necroptosis throws novel insights on patient classification and treatment strategies for hepatocellular carcinoma

Introduction

Necroptosis is a novel pattern of immunogenic cell death and has triggered an emerging wave in antitumor therapy. More evidence has suggested the potential associations between necroptosis and intra-tumoral heterogeneity. Currently, the underlying role of necroptosis remains elusive in hepatocellular carcinoma (HCC) at antitumor immunity and inter-tumoral heterogeneity.

Methods

This study enrolled a total of 728 HCC patients and 139 immunotherapy patients from eight public datasets. The consensus clustering approach was employed to depict tumor heterogeneity of cancer necroptosis. Subsequently, our study further decoded the heterogeneous clinical outcomes, genomic landscape, biological behaviors, and immune characteristics in necroptosis subtypes. For each patient, providing curative clinical recommendations and developing potential therapeutic drugs were used to promote precise medicine.

Results

With the use of the weighted gene coexpression network analysis (WGCNA) algorithm, necroptosis-associated long non-coding RNAs (lncRNAs) (NALRs) were identified in HCC. Based on the NALR expression, two heterogeneous subtypes were decoded with distinct clinical outcomes. Compared to patients in C1, patients in C2 harbored superior pathological stage and presented more unfavorable overall survival and recurrence-free survival. Then, the robustness and reproducibility of necroptosis subtypes were further validated via the nearest template prediction (NTP) approach and classical immune phenotypes. Through comprehensive explorations, C1 was characterized by enriched immune-inflammatory and abundant immune infiltration, while C2 possessed elevated proliferative and metabolic activities and highly genomic instability. Moreover, our results indicated that C1 was more prone to obtain desirable benefits from immunotherapy. For patients in C2, numerous underlying therapeutic agents were developed, which might produce significant efficacy.

Conclusion

This study identified two necroptosis subtypes with distinct characteristics, decoding the tumor heterogeneity. For an individualized patient, our work tailored corresponding treatment strategies to improve clinical management.

Title: Multi-Omics and Immune Landscape of Proliferative LncRNA Signatures: Implications for Risk Stratification and Immunotherapy in Hepatocellular Carcinoma

Background: Long noncoding RNAs (lncRNAs) are significantly implicated in tumor proliferation. Nevertheless, proliferation-derived lncRNAs and their latent clinical significance remain largely unrevealed in hepatocellular carcinoma (HCC).

Methods: This research enrolled 658 HCC patients from five independent cohorts. We retrieved 50 Hallmark gene sets from the MSigDB portal. Consensus clustering was applied to identify heterogeneous proliferative subtypes, and the nearest template prediction (NTP) was utilized to validate the subtypes. We introduced an integrative framework (termed “ProLnc”) to identify proliferation-derived lncRNAs. Moreover, a proliferation-related signature was developed and verified in four independent cohorts.

Results: In 50 Hallmarks, seven proliferation pathways were significantly upregulated and correlated with a worse prognosis. Subsequently, we deciphered two heterogeneous proliferative subtypes in TCGA-LIHC. Subtype 2 displayed enhanced proliferative activities and a worse prognosis, whereas subtype 1 was associated with hyperproliferative HCC and a favorable prognosis. The NTP further verified the robustness and reproducibility of two subtypes in four cohorts derived from different platforms. Combining the differentially expressed lncRNAs from two subtypes with proliferative lncRNA modulators from our ProLnc pipeline, we determined 230 proliferation-associated lncRNAs. Based on the bootstrapping channel and the verification of multiple cohorts, we further identified ten lncRNAs that stably correlated with prognosis. Subsequently, we developed and validated a proliferative lncRNA signature (ProLncS) that could independently and accurately assess the overall survival (OS) and relapse-free survival (RFS) of HCC patients in the four cohorts. Patients with high ProLncS score displayed significantly genomic alterations (e.g., TP53 mutation, 8p23-8p24 copy number variation) and higher abundances of immune cells and immune checkpoint molecules, which suggested immunotherapy was more suitable for patients with high ProLncS score.

Conclusion: Our work provided new insights into the heterogeneity of tumor proliferation, and ProLncS could be a prospective tool for tailoring the clinical decision and management of HCC.

Identification and Validation of Immune Molecular Subtypes and Immune Landscape Based on Colon Cancer Cohort

Background

The incidence and mortality rates of colon adenocarcinoma (COAD), which is the fourth most diagnosed cancer worldwide, are high. A subset of patients with COAD has shown promising responses to immunotherapy. However, the percentage of patients with COAD benefiting from immunotherapy is unclear. Therefore, gaining a better understanding of the immune milieu of colon cancer could aid in the development of immunotherapy and suitable combination strategies.

Methods

In this study, gene expression profiles and clinical follow-up data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and molecular subtypes were identified using the ConsensusClusterPlus package in R. Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic value of immune subtypes. The graph structure learning method was used to reduce the dimension to reveal the internal structure of the immune system. Weighted correlation network analysis (WGCNA) was performed to identify immune-related gene modules. Finally, western blotting was performed to verify the gene expression patterns in COAD samples.

Results

The results showed that 424 COAD samples could be divided into three subtypes based on 1921 immune cell-related genes, with significant differences in prognosis between subtypes. Furthermore, immune-related genes could be divided into five functional modules, each with a different distribution pattern of immune subtypes. Immune subtypes and gene modules were highly reproducible across many data sets. There were significant differences in the distribution of immune checkpoints, molecular markers, and immune characteristics among immune subtypes. Four core genes, namely, CD2, FGL2, LAT2, and SLAMF1, with prognostic significance were identified by WGCNA and univariate Cox analysis.

Conclusion

Overall, this study provides a conceptual framework for understanding the tumor immune microenvironment of colon cancer.

Construction of a Lactate-Related Prognostic Signature for Predicting Prognosis, Tumor Microenvironment, and Immune Response in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent primary malignancies with high heterogeneity in the urological system. Growing evidence implies that lactate is a significant carbon source for cell metabolism and plays a vital role in tumor development, maintenance, and therapeutic response. However, the global influence of lactate-related genes (LRGs) on prognostic significance, tumor microenvironment characteristics, and therapeutic response has not been comprehensively elucidated in patients with KIRC. In the present study, we collected RNA sequencing and clinical data of KIRC from The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts. Unsupervised clustering of 17 differentially expressed LRG profiles divided the samples into three clusters with distinct immune characteristics. Three genes (FBP1, HADH, and TYMP) were then identified to construct a lactate-related prognostic signature (LRPS) using the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. The novel signature exhibited excellent robustness and predictive ability for the overall survival of patients. In addition, the constructed nomogram based on the LRPS-based risk scores and clinical factors (age, gender, tumor grade, and stage) showed a robust predictive performance. Furthermore, patients classified by risk scores had distinguishable immune status, tumor mutation burden, response to immunotherapy, and sensitivity to drugs. In conclusion, we developed an LRPS for KIRC that was closely related to the immune landscape and therapeutic response. This LRPS may guide clinicians to make more precise and personalized treatment decisions for KIRC patients.

The Effect of Hypoxia and Hypoxia-Associated Pathways in the Regulation of Antitumor Response: Friends or Foes?

Hypoxia is an environmental stressor that is instigated by low oxygen availability. It fuels the progression of solid tumors by driving tumor plasticity, heterogeneity, stemness and genomic instability. Hypoxia metabolically reprograms the tumor microenvironment (TME), adding insult to injury to the acidic, nutrient deprived and poorly vascularized conditions that act to dampen immune cell function. Through its impact on key cancer hallmarks and by creating a physical barrier conducive to tumor survival, hypoxia modulates tumor cell escape from the mounted immune response. The tumor cell-immune cell crosstalk in the context of a hypoxic TME tips the balance towards a cold and immunosuppressed microenvironment that is resistant to immune checkpoint inhibitors (ICI). Nonetheless, evidence is emerging that could make hypoxia an asset for improving response to ICI. Tackling the tumor immune contexture has taken on an in silico, digitalized approach with an increasing number of studies applying bioinformatics to deconvolute the cellular and non-cellular elements of the TME. Such approaches have additionally been combined with signature-based proxies of hypoxia to further dissect the turbulent hypoxia-immune relationship. In this review we will be highlighting the mechanisms by which hypoxia impacts immune cell functions and how that could translate to predicting response to immunotherapy in an era of machine learning and computational biology.

Immune Landscape Refines the Classification of Colorectal Cancer With Heterogeneous Prognosis, Tumor Microenvironment and Distinct Sensitivity to Frontline Therapies

The immune microenvironment has profound impacts on the initiation and progression of colorectal cancer (CRC). Therefore, the goal of this article is to identify two robust immune subtypes in CRC, further provide novel insights for the underlying mechanisms and clinical management. In this study, two CRC immune subtypes were identified using the consensus clustering of immune-related gene expression profiles in the meta-GEO dataset (n = 1,198), and their reproducibility was further verified in the TCGA-CRC dataset (n = 638). Subsequently, we characterized the immune escape mechanisms, gene alterations, and clinical features of two immune subtypes. Cluster 1 (C1) was defined as the “immune cold subtype” with immune cell depletion and deficiency, while cluster 2 (C2) was designed as the “immune hot subtype”, with abundant immune cell infiltration and matrix activation. We also underlined the potential immune escape mechanisms: lack of MHC molecules and defective tumor antigen presentation capacity in C1, increased immunosuppressive molecules in C2. The prognosis and sensitivity to 5-FU, Cisplatin and immunotherapy differed between two subtypes. According to the two immune subtypes, we developed a prognosis associated risk score (PARS) with the accurate performance for predicting the prognosis. Additionally, two nomograms for overall survival (OS) and disease-free survival (DFS) were further constructed to facilitate clinical management. Overall, our research provides new references and insights for understanding and refining the CRC.

Histone Acetylation Regulator-Mediated Acetylation Patterns Define Tumor Malignant Pathways and Tumor Microenvironment in Hepatocellular Carcinoma

Background

Histone acetylation modification is one of the most common epigenetic methods used to regulate chromatin structure, DNA repair, and gene expression. Existing research has focused on the importance of histone acetylation in regulating tumorigenicity, tumor progression, and tumor microenvironment (TME) but has not explored the potential roles and interactions of histone acetylation regulators in TME cell infiltration, drug sensitivity, and immunotherapy.

Methods

The mRNA expression and genetic alterations of 36 histone acetylation regulators were analyzed in 1599 hepatocellular carcinoma (HCC) samples. The unsupervised clustering method was used to identify the histone acetylation patterns. Then, based on their differentially expressed genes (DEGs), an HAscore model was constructed to quantify the histone acetylation patterns and related subtypes of individual samples. Lastly, the relationship between HAscore and transcription background, tumor clinical features, characteristics of TME, drug response, and efficacy of immunotherapy were analyzed.

Results

We identified three histone acetylation patterns characterized by high, medium, and low HAscore. Patients with HCC in the high HAscore group experienced worse overall survival time, and the cancer-related malignant pathways were more active in the high HAscore group, comparing to the low HAscore group. The high HAscore group was characterized by an immunosuppressive subtype because of the high infiltration of immunosuppressive cells, such as regulatory T cells and myeloid-derived suppressor cells. Following validation, the HAscore was highly correlated with the sensitivity of anti-tumor drugs; 116 therapeutic agents were found to be associated with it. The HAscore was also correlated with the therapeutic efficacy of the PD-L1 and PD-1 blockade, and the response ratio was significantly higher in the low HAscore group.

Conclusion

To the best of our knowledge, our study is the first to provide a comprehensive analysis of 36 histone acetylation regulators in HCC. We found close correlations between histone acetylation patterns and tumor malignant pathways and TME. We also analyzed the therapeutic value of the HAscore in targeted therapy and immunotherapy. This work highlights the interactions and potential clinical utility of histone acetylation regulators in treatment of HCC and improving patient outcomes.

Methylation-Driven Gene PLAU as a Potential Prognostic Marker for Differential Thyroid Carcinoma

Purpose: Aberrant DNA methylation plays a crucial role in the tumorigenesis of differentiated thyroid cancer (DTC); nevertheless, the factors leading to the local and regional recurrence of DTC are not well understood. This study aimed to establish the connection between DNA methylation-driven genes and the recurrence of DTC.

Methods: RNA sequencing profiles and DNA methylation profiles of DTC were downloaded from The Cancer Genome Atlas (TCGA) database. Combined application of the methylmix R package and univariate Cox regression analyses were used to screen and distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses were utilized to identify the target genes that were closely associated with the recurrence of DTC. Then, correlations between the expression levels of the target genes and the clinicopathological features were verified, as well as their potential biological functions.

Results: A total of 168 Methylation-driven genes were differentially expressed in thyroid cancer, among which 10 genes (GSTO2, GSTM5, GSTM1, GPX7, FGF2, LIF, PLAU, BCL10, SHARPIN and TNFRSF1A) were identified as Hub genes. We selected PLAU for further analysis because PLAU was most strongly correlated with DTC recurrence and the DNA methylation levels of PLAU were closely associated with multiple clinicopathological features of DTC. PLAU was significantly upregulated in DTC, and patients with a high expression level of PLAU had a higher risk of recurrence (p < 0.05). Functional predictions suggested that PLAU-related genes were mainly involved in the regulation of immune-related signaling pathways. Moreover, the mRNA level of PLAU was found to be positively correlated with the cell markers of neutrophils and dendritic cells. In addition, we found that two DNA methylation sites (cg06829584, cg19399285) were associated with abnormal expression of PLAU in DTC.

Conclusion: The methylation-driven gene PLAU is an independent risk factor for the recurrence of DTC and it functions as an oncogene through the regulation of immune-related signaling pathways, which offers new insight into the molecular mechanisms of thyroid cancer and provides new possibilities for individualized treatment of thyroid cancer patients.

Characterization of Two Ferroptosis Subtypes With Distinct Immune Infiltration and Gender Difference in Gastric Cancer

Background: Iron is an essential nutrient involved in the redox cycle and the formation of free radicals. The reprogramming of iron metabolism is the main link to tumor cell survival. Ferroptosis is an iron-dependent form of regulated cell death associated with cancer; the characteristics of ferroptosis in cancers are still uncertain. This study aimed to explore the application value and gender difference of ferroptosis in prognosis and immune prediction to provide clues for targeted therapy of gastric cancer.

Methods: We comprehensively evaluated the ferroptosis levels of 1,404 gastric cancer samples from six independent GC cohorts based on ferroptosis-related specific genes and systematically correlated ferroptosis with immune cell infiltrating and gender characteristics. The ferroptosis score was constructed to quantify the ferroptosis levels of individual tumors using principal component analysis (PCA) algorithms.

Results: We identified two distinct ferroptosis subtypes in gastric cancer, namely Subtype-A and Subtype-B. We found that male patients in Subtype-B had the worst prognosis in contrast with the other groups. Three sex hormone receptors (AR, ER, and PR) in Subtype-B tumor patients were higher than in Subtype-A tumor patients in GC, while the HER2 displayed an opposite trend. We developed a risk model termed ferroptosis score to evaluate ferroptosis levels within individual tumors. The low-ferroptosis score group was characterized by activation of immune cells and increased mutation burden, which is also linked to increased neoantigen load and enhanced response to anti-PD-1/L1 immunotherapy. The patients with a low-ferroptosis score showed a high microsatellite instability status (MSI-H) and had a higher response to immunotherapy. Furthermore, the patients with low-ferroptosis scores have a lower estimated IC50 in the several chemotherapy drugs, including paclitaxel, gemcitabine, and methotrexate.

Conclusions: We revealed that sex hormone receptors and immune cell infiltration were markedly different between ferroptosis subtypes in GC patients. The results suggested that gender difference may be critical when the ferroptosis-related strategy is applied in GC treatment. Further, ferroptosis levels were identified with an extreme variety of prognosis and tumor immune characteristics, which might benefit GC individualized treatment.

An Integrated Fibrosis Signature for Predicting Survival and Immunotherapy Efficacy of Patients With Hepatocellular Carcinoma

Introduction: Fibrosis, a primary cause of hepatocellular carcinoma (HCC), is intimately associated with inflammation, the tumor microenvironment (TME), and multiple carcinogenic pathways. Currently, due to widespread inter- and intra-tumoral heterogeneity of HCC, the efficacy of immunotherapy is limited. Seeking a stable and novel tool to predict prognosis and immunotherapy response is imperative. Methods: Using stepwise Cox regression, least absolute shrinkage and selection operator (LASSO), and random survival forest algorithms, the fibrosis-associated signature (FAIS) was developed and further validated. Subsequently, comprehensive exploration was conducted to identify distinct genomic alterations, clinical features, biological functions, and immune landscapes of HCC patients. Results: The FAIS was an independent prognostic predictor of overall survival and recurrence-free survival in HCC. In parallel, the FAIS exhibited stable and accurate performance at predicting prognosis based on the evaluation of Kaplan-Meier survival curves, receiver operator characteristic curves, decision curve analysis, and Harrell's C-index. Further investigation elucidated that the high-risk group presented an inferior prognosis with advanced clinical traits and a high mutation frequency of TP53, whereas the low-risk group was characterized by superior CD8⁺ T cell infiltration, a higher TIS score, and a lower TIDE score. Additionally, patients in the low-risk group might yield more benefits from immunotherapy. Conclusion: The FAIS was an excellent scoring system that could stratify HCC patients and might serve as a promising tool to guide surveillance, improve prognosis, and facilitate clinical management.

CD8+ T Cell-Based Molecular Classification With Heterogeneous Immunogenomic Landscapes and Clinical Significance of Clear Cell Renal Cell Carcinoma

The tumor microenvironment (TME) exerts a high impact on tumor biology and immunotherapy. The heterogeneous phenotypes and the clinical significance of CD8⁺ T cells in TME have not been fully elucidated. Here, a comprehensive immunogenomic analysis based on multi-omics data was performed to investigate the clinical significance and tumor heterogeneity between CD8⁺ T cell-related molecular clusters. We identified two distinct molecular clusters of ccRCC (C1 and C2) in TCGA and validated in E-MTAB-1980 cohorts. The C1 cluster was characterized by unfavorable prognosis, increased expression levels of CD8⁺ T cell exhaustion markers, high immune infiltration levels as well as more immune escape mechanisms. The C2 cluster was featured by favorable prognosis, elevated expression levels of CD8⁺ T cell effector markers, low load of copy number loss and low frequency of 9p21.3 deletion. Moreover, the effect of molecular classifications on Nivolumab therapeutic efficacy in the CheckMate 025 cohort was examined, and the C2 cluster exhibited a better prognosis. Taken together, we determine two CD8⁺ T cell-related molecular clusters in ccRCC, and provide new insights for evaluating the functions of CD8⁺ T cells. Our molecular classification is a potential strategy for prognostic prediction and immunotherapeutic guidance for ccRCC patients.

Identification of the Immune Cell Infiltration Landscape in Hepatocellular Carcinoma to Predict Prognosis and Guide Immunotherapy

Background: Globally, hepatocellular carcinoma (HCC) is the sixth most frequent malignancy with a high incidence and a poor prognosis. Immune cell infiltration (ICI) underlies both the carcinogenesis and immunogenicity of tumors. However, a comprehensive classification system based on the immune features for HCC remains unknown.

Methods: The HCC dataset from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts was used in this study. The ICI patterns of 571 patients were characterized using two algorithms: the patterns were determined based on the ICI using the ConsensusClusterPlus package, and principal component analysis (PCA) established the ICI scores. Differences in the immune landscape, biological function, and somatic mutations across ICI scores were evaluated and compared, followed by a predictive efficacy evaluation of ICI scores for immunotherapy by the two algorithms and validation using an external immunotherapy cohort.

Results: Based on the ICI profile of the HCC patients, three ICI patterns were identified, including three subtypes having different immunological features. Individual ICI scores were determined; the high ICI score subtype was characterized by enhanced activation of immune-related signaling pathways and a significantly high tumor mutation burden (TMB); concomitantly, diminished immunocompetence and enrichment of pathways associated with cell cycle and RNA degradation were found in the low ICI score subtype. Taken together, our results contribute to a better understanding of an active tumor and plausible reasons for its poor prognosis.

Conclusion: The present study reveals that ICI scores may serve as valid prognostic biomarkers for immunotherapy in HCC.

Identification of the Pyroptosis-Related Gene Signature for Overall Survival Prediction in Patients With Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor worldwide, with an increasing incidence and mortality. Due to general resistance to antitumor drugs, only limited therapies are currently available for advanced HCC patients, leading to a poor prognosis with a 5-year survival rate less than 20%. Pyroptosis is a type of inflammation-related programmed cell death and may become a new potential target for cancer therapy. However, the function and prognostic value of pyroptosis-related genes (PRGs) in HCC remain unknown. Here, we identified a total of 58 PRGs reported before and conducted a six-PRG signature via the LASSO regression method in the GEO training cohort, and model efficacy was further validated in an external dataset. The HCC patients can be classified into two subgroups based on the median risk score. High-risk patients have significantly shorter overall survival (OS) than low-risk patients in both training and validation cohorts. Multivariable analysis indicated that the risk score was an independent prognostic factor for OS of HCC patients. Functional enrichment analysis and immune infiltration evaluation suggested that immune status was more activated in the low-risk group. In summary, PRGs can be a prediction factor for prognosis of HCC patients and targeting pyroptosis is a potential therapeutic alternative in HCC.

Computational Recognition and Clinical Verification of TGF-β-Derived miRNA Signature With Potential Implications in Prognosis and Immunotherapy of Intrahepatic Cholangiocarcinoma

MicroRNAs (miRNAs) were recently implicated in modifying the transforming growth factor β (TGF-β) signaling in multiple cancers. However, TGF-β-derived miRNAs and their potential clinical significance remain largely unexplored in intrahepatic cholangiocarcinoma (ICC). In this study, we proposed an integrated framework that enables the identification of TGF-β-derived miRNAs in ICC (termed “TGFmitor”). A total of 36 TGF-β-derived miRNAs were identified, of which nine significantly correlated with overall survival (OS) and aberrantly expressed in ICC. According to these miRNAs, we discovered and validated a TGF-β associated miRNA signature (TAMIS) in GSE53870 (n =63) and TCGA-CHOL (n =32). To further confirm the clinical interpretation of TAMIS, another validation based on qRT-PCR results from 181 ICC tissues was performed. TAMIS was proven to be an independent risk indicator for both OS and relapse-free survival (RFS). TAMIS also displayed robust performance in three cohorts, with satisfactory AUCs and C-index. Besides, patients with low TAMIS were characterized by superior levels of CD8+ T cells infiltration and PD-L1 expression, while patients with high TAMIS possessed enhanced CMTM6 expression. Kaplan-Meier analysis suggested CMTM6 could further stratify TAMIS. The TAMIS^highCMTM6^high subtype had the worst prognosis and lowest levels of CD8A and PD-L1 expression relative to the other subtypes, indicating this subtype might behave as “super-cold” tumors. Notably, the improved discrimination was observed when CMTM6 was combined with TAMIS. Overall, our signature could serve as a powerful tool to help improve prognostic management and immunotherapies of ICC patients.

Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer

Background

Renal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis.

Methods

Herein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.

Results

The immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.

Conclusions

The immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

Clinical Application Value of Circulating Cell-free DNA in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and a leading cause of cancer-related deaths. Due to late diagnosis, early intrahepatic metastasis and nonresponse to systemic treatments, surgical resection and/or biopsy specimens remain the gold standard for disease staging, grading and clinical decision-making. Since only a small amount of tissue was obtained in a needle biopsy, the conventional tissue biopsy is unable to represent tumor heterogeneity in HCC. For this reason, it is imperative to find a new non-invasive and easily available diagnostic tool to detect HCC at an early stage and to monitor HCC recurrence. The past decade has witnessed considerable evolution in the development of liquid biopsy technologies with the emergence of next-generation sequencing. As a liquid biopsy approach, molecular analysis of cell-free DNA (cfDNA), characterized by noninvasiveness and real-time analysis, may accurately represent the tumor burden and comprehensively reflect genetic profile of HCC. Therefore, cfDNA may be used clinically as a predictive biomarker in early diagnosis, outcome assessment, and even molecular typing. In this review, we provide an update on the recent advances made in clinical applications of cfDNA in HCC.

Predictive value of preoperative C-reactive protein/serum albumin ratio and gamma-glutamyl transpeptidase for early recurrence in patients with hepatocellular carcinoma after resection

BACKGROUND

In recent years, some studies have reported that increased C-reactive protein/serum albumin ratio (CRP/Alb) and gamma-glutamyl transpeptidase (GGT) predict a poor prognosis in hepatocellular carcinoma (HCC) after treatment; however, there are few studies on their predictive value for the prognosis after HCC resection.

AIM

To evaluate the predictive value of preoperative CRP/Alb and GGT for early recurrence in patients with HCC after resection.

METHODS

A retrospective analysis was performed on the clinical data of 80 initially treated patients who were pathologically confirmed as having hepatocellular carcinoma after radical resection at our hospital from January 1, 2016 to January 20, 2019 and then followed for the prognosis. The patients were divided into either an early recurrence group or a non-early recurrence group based on the presence of recurrence in 2 years or not. The χ² test was used for univariate analysis, and Cox proportional hazards model was used for multivariate analysis. The tumor-free survival curves of independent risk factors were plotted using the Kaplan-Meier method, and log-rank method was used to compare their difference.

RESULTS

The best cut-off values of CRP/Alb and GGT were 0.041 and 75.5 U/L, respectively, according to the results of ROC curve analysis. The recurrence-free survival rates within 1 and 2 years were 70% and 56.25%, respectively, in this group of patients. Univariate analysis showed that microvascular invasion (MVI), CRP/Alb, and GGT were risk factors for early recurrence in patients with hepatocellular carcinoma after resection. Multivariate analysis showed that MVI and GGT were independent risk factors for early recurrence in patients with hepatocellular carcinoma after resection. The tumor-free survival time of HCC patients with MVI was significantly shorter than that of patients without (P ＜ 0.001). The tumor-free survival time in the GGT > 75.5 U/L group was significantly shorter than that of the GGT ≤ 75.5 U/L group (P = 0.001).

CONCLUSION

CRP/Alb, GGT, and MVI have potential predictive value for early tumor recurrence in patients with hepatocellular carcinoma after resection.

CELF2 is a candidate prognostic and immunotherapy biomarker in triple-negative breast cancer and lung squamous cell carcinoma: A pan-cancer analysis

CUGBP Elav-like family member 2(CELF2) plays crucial roles in the development and activation of T cell. However, the impacts of CELF2 on tumour-infiltrating immune cells (TIICs) and clinical outcomes of tumours remain unclear. In this study, we found that elevated CELF2 expression was markedly correlated with prolonged survival in multiple tumours, particularly in breast and lung cancers. Notably, CELF2 only impacted the prognosis of triple-negative breast cancer (TNBC) with lymph node metastasis. Further investigation showed CELF2 expression was positively correlated with the infiltration abundance of dendritic cells (DCs), CD8+ T cells and neutrophils in breast invasive carcinoma (BRCA) and DCs in lung squamous cell carcinoma (LUSC). CELF2 also had strong correlations with markers of diverse TIICs such as T cells, tumour-associated macrophages and DCs in BRCA and LUSC. Importantly, CELF2 was significantly associated with plenty of immune checkpoint molecules (ICMs) and outperformed five prevalent biomarkers including PD-1, PD-L1, CTLA-4, CD8 and tumour mutation burden in predicting immunotherapeutic responses. Immunohistochemistry also revealed lower protein levels of CELF2 in TNBC and LUSC compared to normal tissues, and patients with high expression showed significantly prolonged prognosis. In conclusion, we demonstrated that increased CELF2 expression was closely related to better prognosis and superior TIIC infiltration and ICM expression, particularly in BRCA and LUSC. CELF2 also performed well in evaluating the immunotherapeutic efficacy, suggesting CELF2 might be a promising biomarker.

Immune checkpoint molecules are regulated by transforming growth factor (TGF)-β1-induced epithelial-to-mesenchymal transition in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer with a high mortality rate. Epithelial-to-mesenchymal transition (EMT) confers cancer cells with immune evasive ability by modulating the expression of immune checkpoints in many cancers. Thus, the aim of our study is to examine the interplay between EMT and immune checkpoint molecules in HCC. A reversible EMT model was utilised with transforming growth factor (TGF)-β1 as an EMT inducer for HCC cell lines Hep3B and PLC/PRF/5. HCC cells were treated with TGF-β1 for 72 h and the EMT status and immune checkpoint expression were examined. In addition, the migratory ability of HCC cells were examined using wound healing and transwell migration assays in the reversible EMT model. siRNA-mediated knockdown of immune checkpoint molecule, B7-H3, was further utilised to validate the association between TGF-β1-mediated EMT and immune checkpoint expression in HCC. In addition, a web-based platform, SurvExpress, was utilised to evaluate the association between expression of TGF-β1 in combination with immune checkpoint molecules and overall survival in HCC patients. We observed induction of EMT upon treatment of HCC cells with TGF-β1 revealed by reduced expression of epithelial markers along with increased expression of mesenchymal markers. Withdrawal of TGF-β1 reversed the process of EMT with elevated expression of epithelial markers and reduced expression of mesenchymal markers. TGF-β1 treatment elevated the migratory potential of HCC cells which was reversed following reversal assay. Notably, during TGF-β1-induced EMT, there was upregulation of immune checkpoint molecules PD-L1 and B7-H3. However, the reversal of EMT decreased the expression of PD-L1 and B7-H3. In addition, TGF-β1 driven EMT was reversed following knockdown of B7-H3 in both HCC cells further validating the interplay between TGF-β1-mediated EMT and immune checkpoint expression in HCC. Furthermore, the coordinate expression of TGF-β1 with PD-L1 (p=0.01487) and B7-H3 (p=0.009687) was correlated with poor overall survival in 422 HCC patients. Our study has demonstrated a close association between TGF-β1-mediated EMT and regulation of immune checkpoints in HCC.