Serum KLKB1 as a Potential Prognostic Biomarker for Hepatocellular Carcinoma Based on Data-Independent Acquisition and Parallel Reaction Monitoring

Construction and validation of a prognostic and therapeutic cuproptosis- and immune-related gene signature in hepatocellular carcinoma

Background

Abnormal accumulation of copper could induce cell death and tumor growth, and affect tumor immune escape by regulating programmed cell death ligand 1 (PD-L1) expression. This study aims to establish and verify a risk signature based on cuproptosis- and immune-related genes (CIRGs) for hepatocellular carcinoma (HCC) management.

Methods

HCC RNA-seq and clinical data were obtained from open databases. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were utilized to screen CIRGs and develop a risk signature. The signature's value for clinical applications, functional enrichment, tumor mutation burden (TMB), and immune profile analyses were investigated systematically.

Results

A risk signature was developed utilizing seven CIRGs, and it performed well in predicting the prognosis of HCC patients in both the training and external validation cohorts. The model's risk score was discovered to be related to important clinical features. Top 15 mutated genes in HCC were significantly different among different risk groups. High-risk patients showed higher TMB, and high TMB was closely identified with a poorer prognosis. Immune profile analyses showed that immune infiltration level was higher in low-risk patients than high-risk patients, and the level of immune checkpoint genes expression varied significantly between patients in two different risk groups. Low-risk patients responded well to immunotherapy treatment, whereas high-risk patients were more sensitive to sorafenib, doxorubicin, gemcitabine and AKT (also known as protein kinase B) inhibitors.

Conclusions

The established risk signature based on CIRGs can not only well predict the prognosis of HCC patients but is also promising in evaluating TMB and treatment response to immunotherapy, targeted therapy and chemotherapy, which has the potential to assist in the clinical management of HCC.

Proteomic Analysis Identifies GSN as a Noninvasive Circulating Serum Biomarker for Predicting Early Recurrence of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is susceptible to early recurrence, but it lacks effective predictive biomarkers. In this study, we retrospectively selected 179 individuals as a discovery cohort (126 HCC patients and 53 liver cirrhosis (LC) patients) for screening candidate serum biomarkers of early recurrence based on data independent acquisition-mass spectrometry strategy. And then, the candidate biomarkers were validated in an additional independent cohort with 192 individuals (142 HCC patients and 50 LC patients) using parallel reaction monitoring targeted quantitative techniques (PXD047852). Eventually, we validated that gelsolin (GSN) concentrations were significantly lower in HCC than in LC (p < 0.0001), patients with low GSN concentrations had a poor prognosis (p < 0.0001), and GSN concentrations were significantly lower in early recurrence HCC than in late recurrence HCC (p < 0.0001). These trends were also observed in alpha-fetoprotein (AFP)-negative HCC patients. The area under the curve of machine-learning-based predictive model (GSN and microvascular invasion) for predicting early recurrence risk reached 0.803 (95% confidence interval (CI): 0.786-0.820) and maintained the same efficacy in AFP-negative patients. In conclusion, GSN is a novel serum biomarker for early recurrence of HCC. The model could provide timely warning to HCC patients at high risk of recurrence.

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Early diagnosis of hepatocellular carcinoma (HCC) lacks highly sensitive and specific protein biomarkers. Here, we describe a staged mass spectrometry (MS)-based discovery-verification-validation proteomics workflow to explore serum proteomic biomarkers for HCC early diagnosis in 1002 individuals. Machine learning model determined as P4 panel (HABP2, CD163, AFP and PIVKA-II) clearly distinguish HCC from liver cirrhosis (LC, AUC 0.979, sensitivity 0.925, specificity 0.915) and healthy individuals (HC, AUC 0.992, sensitivity 0.975, specificity 1.000) in an independent validation cohort, outperforming existing clinical prediction strategies. Furthermore, the P4 panel can accurately predict LC to HCC conversion (AUC 0.890, sensitivity 0.909, specificity 0.877) with predicting HCC at a median of 11.4 months prior to imaging in prospective external validation cohorts (No.: Keshen 2018_005_02 and NCT03588442). These results suggest that proteomics-driven serum biomarker discovery provides a valuable reference for the liquid biopsy, and has great potential to improve early diagnosis of HCC.

Screening of Exosome-Derived Proteins and Their Potential as Biomarkers in Diagnostic and Prognostic for Pancreatic Cancer

In the oncological area, pancreatic cancer is one of the most lethal diseases, with 5-year survival rising just 10% in high-development countries. This disease is genetically characterized by KRAS as a driven mutation followed by SMAD4, CDKN2, and TP53-associated mutations. In clinical aspects, pancreatic cancer presents unspecific clinical symptoms with the absence of screening and early plasmatic biomarker, being that CA19-9 is the unique plasmatic biomarker having specificity and sensitivity limitations. We analyzed the plasmatic exosome proteomic profile of 23 patients with pancreatic cancer and 10 healthy controls by using Nanoscale liquid chromatography coupled to tandem mass spectrometry (NanoLC-MS/MS). The pancreatic cancer patients were subdivided into IPMN and PDAC. Our findings show 33, 34, and 7 differentially expressed proteins when comparing the IPMN vs. control, PDAC-No treatment vs. control, and PDAC-No treatment vs. IPMN groups, highlighting proteins of the complement system and coagulation, such as C3, APOB, and SERPINA. Additionally, PDAC with no treatment showed 11 differentially expressed proteins when compared to Folfirinox neoadjuvant therapy or Gemcitabine adjuvant therapy. So here, we found plasmatic exosome-derived differentially expressed proteins among cancer patients (IPMN, PDAC) when comparing with healthy controls, which could represent alternative biomarkers for diagnostic and prognostic evaluation, supporting further scientific and clinical studies on pancreatic cancer.

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

Background

Hepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients.

Methods

The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR.

Result

11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong.

Conclusion

TEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.

Aggrephagy-related LncRNAs index: A predictor for HCC prognosis, immunotherapy efficacy, and chemosensitivity

BACKGROUND

Due to the complexity and heterogeneity of hepatocellular carcinoma, the existing clinical staging criterias are insufficient to accurately reflect the tumor microenvironment and predict the prognosis of HCC patients. Aggrephagy, as a type of selective autophagy, is associated with various phenotypes of malignant tumors.

OBJECTIVE

This study aimed to identify and validate a prognostic model based on aggrephagy-related LncRNAs to assess the prognosis and immunotherapeutic response of HCC patients.

METHODS

Based on the TCGA-LIHC cohort, aggrephagy-related LncRNAs were identified. Univariate Cox regression analysis and lasso and multivariate Cox regression were used to construct a risk-scoring system based on eight ARLs. CIBERSORT, ssGSEA, and other algorithms were used to evaluate and present the immune landscape of tumor microenvironment.

RESULTS

The high-risk group had a worse overall survival (OS) than the low-risk group. Patients in the high-risk group are more likely to benefit from immunotherapy because of their high infiltration level and high immune checkpoint expression.

CONCLUSION

The ARLs signature is a powerful predictor of prognosis for HCC patients, and the nomogram based on this model can help clinicians accurately determine the prognosis of HCC patients and screen for specific subgroups of patients who are more sensitive to immunotherapy and chemotherapy.

Identification of HPV16 positive cervical cancer subsets characterized by divergent immune and oncogenic phenotypes with potential implications for immunotherapy

BACKGROUND

Cervical cancers (CaCx), like many other cancer types, portray high molecular heterogeneity that affects response to therapy, including immunotherapy. In India and other developing countries, CaCx mortality rates are very high because women report to the clinics with advanced cancers in absence of organized screening programs. This calls for implementation of newer therapeutic regimens for CaCx, like immunotherapy, which is again not used commonly in such countries.

OBJECTIVE

Therefore, we focused on dissecting tumour immune heterogeneity, if any, identify immune gene-based biomarkers of heterogeneity and subsets of such cancers with the potential for immunotherapy. We also attempted to characterize the cancer-associated phenotypes of such subsets, including viral load, to decipher the relationship of tumour immunogenicity with oncogenicity.

METHODS

Employing RNA-seq analysis of 44 HPV16 positive CaCx patients, immune subtypes were identified by unsupervised hierarchical clustering of global immune-gene expression profiles. Proportions of tumor infiltrating immune cells in the tumor milieu were estimated, employing Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), using gene expression data from RNA-seq. The oncogenic phenotypes of the immune subtypes of CaCx were deciphered through differential gene expression (DEGs) and pathway enrichment analysis. Viral load was estimated through TaqMan-based qRT-PCR analysis.

RESULTS

Analysis revealed the presence of two immune subtypes of CaCx, A (26/44; 59.09%) and B (18/44; 40.90%). Compared to Subtype-A, Subtype-B portrayed overexpression of immune genes and high infiltration of immune cells, specifically CD8+ T cells (p < 0.0001). Besides, a significant correlation between PD-1 and PD-L1 co-expression among Subtype-B, as opposed to Subtype-A, confirmed the interactive roles of these immune checkpoint molecules in Subtype B. Stepwise discriminant analysis pin-pointed ten immune-genes that could classify 100% of the patients significantly (p < 0.0001) into the two immune subtypes and serve as potential biomarkers of CaCx immunity. Differential gene expression analysis between the subtypes unveiled that Subtype-B was more biologically aggressive than Subtype-A, reflecting loss of structural integrity and promotion of cancer progression. The viral load was significantly lower in Subtype-B (average viral load = 10.74/100 ng of genomic DNA) compared to Subtype-A (average viral load = 14.29/100 ng of genomic DNA). Thus viral load and the ten-gene panel underscore their association with immunogenicity and oncogenicity.

CONCLUSION

Our study provides strong evidence that only a subset, about 41% of HPV16 positive CaCx patients in India, portray immune enrichment of the tumor milieu coupled with aggressive phenotypes. Such subtypes are therefore likely to benefit through checkpoint molecule-based or tumor infiltrating lymphocyte-based immunotherapy, which could be a leap forward in tackling aggressive forms of such CaCx in India and other developing countries.

Characterization of coagulation-related gene signature to predict prognosis and tumor immune microenvironment in skin cutaneous melanoma

Backgroud

Skin cutaneous melanoma (SKCM) is an extremely metastatic form of skin cancer. However, there are few valuable molecular biomarkers, and accurate diagnosis is still a challenge. Hypercoagulable state encourages the infiltration and development of tumor cells and is significantly associated with poor prognosis in cancer patients. However, the use of a coagulation-related gene (CRG) signature for prognosis in SKCM, on the other hand, has yet to be determined.

Method

We used data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases to identify differentially expressed CRGs, then designed a prognostic model by using the LASSO algorithm, univariate and multivariate Cox regression analysis, and constructed a nomogram which was evaluated by calibration curves. Moreover, the Gene Expression Omnibus (GEO), GSE54467 was used as an independent validation. The correlation between risk score and clinicopathological characteristics, tumor microenvironment (TME), and immunotherapy was further analyzed.

Results

To develop a prognostic model, seven CRGs in SKCM patients related to overall survival (OS) were selected: ANG, C1QA, CFB, DUSP6, KLKB1, MMP7, and RABIF. According to the Kaplan-Meier survival analysis, an increased OS was observed in the low-risk group than in the high-risk group (P<0.05). Immunotherapy was much more beneficial in the low-risk group, as per immune infiltration, functional enrichment, and immunotherapy analysis.

Conclusions

The prognosis of SKCM patients may now be predicted with the use of a CRG prognostic model, thus guiding the development of treatment plans for SKCM patients and promoting OS rates.

Circ_0036412 affects the proliferation and cell cycle of hepatocellular carcinoma via hedgehog signaling pathway

Background

Hepatocellular carcinoma (HCC), as the most common type of liver cancer, is characterized by high recurrence and metastasis. Circular RNA (circRNA) circ_0036412 was selected for studying the underlying mechanisms of HCC.

Methods

Quantitative real time-polymerase chain reaction (qRT-PCR) and western blot analyzed gene and protein expression. Functional experiments evaluated HCC cell proliferation, apoptosis and cell cycle in vitro. In vivo experiments detected HCC carcinogenesis in vivo. Fluorescence in situ hybridization (FISH) assays evaluated the subcellular distribution. Luciferase reporter, Chromatin immunoprecipitation (ChIP), DNA pulldown, RNA-binding protein immunoprecipitation (RIP), and RNA pulldown assays detected the underlying mechanisms.

Results

Circ_0036412 is overexpressed in HCC cells and features circular structure. PRDM1 activates circ_0036412 transcription to regulate the proliferation and cell cycle of HCC cells in vitro. Circ_0036412 modulates Hedgehog pathway. GLI2 propels HCC growth in vivo. Circ_0036412 up-regulates GLI2 expression by competitively binding to miR-579-3p, thus promoting the proliferation and inhibiting cell cycle arrest of HCC cells. Circ_0036412 stabilizes GLI2 expression by recruiting ELAVL1. Circ_0036412 propels the proliferation and inhibits cell cycle arrest of HCC cells in vitro through Hedgehog pathway.

Conclusions

Circ_0036412 affects the proliferation and cell cycle of HCC via Hedgehog signaling pathway. It offers an insight into the targeted therapies of HCC.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03305-x.