M2-like tumor-associated macrophage-related biomarkers to construct a novel prognostic signature, reveal the immune landscape, and screen drugs in hepatocellular carcinoma

Machine learning-based identification of biomarkers and drugs in immunologically cold and hot pancreatic adenocarcinomas

BACKGROUND

Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor milieu, which is associated with resistance to immune checkpoint blockade therapy; however, the underlying mechanisms are incompletely understood. Here, we aimed to improve our understanding of the molecular mechanisms occurring in the tumor microenvironment and to identify biomarkers, therapeutic targets, and potential drugs to improve PAAD treatment.

METHODS

Patients were categorized according to immunologically hot or cold PAAD subtypes with distinct disease outcomes. Cox regression and weighted correlation network analysis were performed to construct a novel gene signature, referred to as 'Downregulated in hot tumors, Prognostic, and Immune-Related Genes' (DPIRGs), which was used to develop prognostic models for PAAD via machine learning (ML). The role of DPIRGs in PAAD was comprehensively analyzed, and biomarker genes able to distinguish PAAD immune subtypes and predict prognosis were identified by ML. The expression of biomarkers was verified using public single-cell transcriptomic and proteomic resources. Drug candidates for turning cold tumors hot and corresponding target proteins were identified via molecular docking studies.

RESULTS

Using the DPIRG signature as input data, a combination of survival random forest and partial least squares regression Cox was selected from 137 ML combinations to construct an optimized PAAD prognostic model. The effects and molecular mechanisms of DPIRGs were investigated by analysis of genetic/epigenetic alterations, immune infiltration, pathway enrichment, and miRNA regulation. Biomarkers and potential therapeutic targets, including PLEC, TRPV1, and ITGB4, among others, were identified, and the cell type-specific expression of the biomarkers was validated. Drug candidates, including thalidomide, SB-431542, and bleomycin A2, were identified based on their ability to modulate DPIRG expression favorably.

CONCLUSIONS

By combining multiple ML algorithms, we developed a novel prognostic model with excellent performance in PAAD cohorts. ML also proved to be powerful for identifying biomarkers and potential targets for improved PAAD patient stratification and immunotherapy.

High-motility pro-tumorigenic monocytes drive macrophage enrichment in the tumor microenvironment

Enrichment of tumor-associated macrophages (TAMΦs) in the tumor microenvironment correlates with worse clinical outcomes in triple-negative breast cancer (TNBC) patients, prompting the development of therapies to inhibit TAMΦ infiltration. However, the lackluster efficacy of CCL2-based chemotaxis blockade in clinical trials suggests that a new understanding of monocyte/macrophage infiltration may be necessary. Here we demonstrate that random migration, and not only chemotaxis, drives macrophage tumor infiltration. We identified tumor- associated monocytes (TAMos) that display a dramatically enhanced migration capability, induced rapidly by the tumor microenvironment, that drives effective tumor infiltration, in contrast to low-motility differentiated macrophages. TAMo, not TAMΦ, promotes cancer cell proliferation through activation of the MAPK pathway. IL-6 secreted both by cancer cells and TAMo themselves enhances TAMo migration by increasing dendritic protrusion dynamics and myosin- based contractility via the JAK2/STAT3 signaling pathway. Independent from CCL2 mediated chemotaxis, IL-6 driven enhanced migration and pro-proliferative effect of TAMo were validated in a syngeneic TNBC mouse model. Depletion of IL-6 in cancer cells significantly attenuated monocyte infiltration and reversed TAMo-induced cancer cell proliferation. This work reveals the critical role random migration plays in monocyte driven TAMΦ enrichment in a tumor and pinpoints IL-6 as a potential therapeutic target in combination with CCL2 to ameliorate current strategies against TAMΦ infiltration.

OGA mutant aberrantly hydrolyzes O-GlcNAc modification from PDLIM7 to modulate p53 and cytoskeleton in promoting cancer cell malignancy

O-GlcNAcase (OGA) is the only human enzyme that catalyzes the hydrolysis (deglycosylation) of O-linked beta-N-acetylglucosaminylation (O-GlcNAcylation) from numerous protein substrates. OGA has broad implications in many challenging diseases including cancer. However, its role in cell malignancy remains mostly unclear. Here, we report that a cancer-derived point mutation on the OGA's noncatalytic stalk domain aberrantly modulates OGA interactome and substrate deglycosylation toward a specific set of proteins. Interestingly, our quantitative proteomic studies uncovered that the OGA stalk domain mutant preferentially deglycosylated protein substrates with +2 proline in the sequence relative to the O-GlcNAcylation site. One of the most dysregulated substrates is PDZ and LIM domain protein 7 (PDLIM7), which is associated with the tumor suppressor p53. We found that the aberrantly deglycosylated PDLIM7 suppressed p53 gene expression and accelerated p53 protein degradation by promoting the complex formation with E3 ubiquitin ligase MDM2. Moreover, deglycosylated PDLIM7 significantly up-regulated the actin-rich membrane protrusions on the cell surface, augmenting the cancer cell motility and aggressiveness. These findings revealed an important but previously unappreciated role of OGA's stalk domain in protein substrate recognition and functional modulation during malignant cell progression.

Single-cell RNA sequencing reveals the evolution of the immune landscape during perihematomal edema progression after intracerebral hemorrhage

BACKGROUND

Perihematomal edema (PHE) after post-intracerebral hemorrhage (ICH) has complex pathophysiological mechanisms that are poorly understood. The complicated immune response in the post-ICH brain constitutes a crucial component of PHE pathophysiology. In this study, we aimed to characterize the transcriptional profiles of immune cell populations in human PHE tissue and explore the microscopic differences between different types of immune cells.

METHODS

9 patients with basal ganglia intracerebral hemorrhage (hematoma volume 50-100 ml) were enrolled in this study. A multi-stage profile was developed, comprising Group1 (n = 3, 0-6 h post-ICH, G1), Group2 (n = 3, 6-24 h post-ICH, G2), and Group3 (n = 3, 24-48 h post-ICH, G3). A minimal quantity of edematous tissue surrounding the hematoma was preserved during hematoma evacuation. Single cell RNA sequencing (scRNA-seq) was used to map immune cell populations within comprehensively resected PHE samples collected from patients at different stages after ICH.

RESULTS

We established, for the first time, a comprehensive landscape of diverse immune cell populations in human PHE tissue at a single-cell level. Our study identified 12 microglia subsets and 5 neutrophil subsets in human PHE tissue. What's more, we discovered that the secreted phosphoprotein-1 (SPP1) pathway served as the basis for self-communication between microglia subclusters during the progression of PHE. Additionally, we traced the trajectory branches of different neutrophil subtypes. Finally, we also demonstrated that microglia-produced osteopontin (OPN) could regulate the immune environment in PHE tissue by interacting with CD44-positive cells.

CONCLUSIONS

As a result of our research, we have gained valuable insight into the immune-microenvironment within PHE tissue, which could potentially be used to develop novel treatment modalities for ICH.

Construction and significance of a breast cancer prognostic model based on cuproptosis-related genotyping and lncRNAs

BACKGROUND

/Purpose: Cuproptosis may play a significant role in breast cancer (BC). We aimed to investigate the prognostic impact of cuproptosis-related lncRNAs in BC.

METHODS

Consensus clustering analysis categorized TCGA-BRCA samples into 3 clusters, followed by survival and immune analyses of the 3 clusters. LASSO-COX analysis was performed on cuproptosis-related lncRNAs differentially expressed in BC to construct a BC prognostic model. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment, immune, and drug prediction analyses were performed on the high-risk and low-risk groups. Cell experiments were conducted to analyze the results of drug prediction and two cuproptosis-related lncRNAs (AC104211.1 and LINC01863).

RESULTS

Significant differences were observed in survival outcomes and immune infiltration levels among the three clusters (p < 0.05). The validation of the model showed significant differences in survival outcomes between the high-risk and low-risk groups in both the training and validation sets (p < 0.05). Differential mRNAs between the two groups were significantly enriched in the Neuroactive ligand-receptor interaction and cAMP signaling pathway. Additionally, significant differences were found in immune infiltration levels, human leukocyte antigen (HLA) expression, Immunophenoscore (IPS) scores, and Tumor Immune Dysfunction and Exclusion (TIDE) scores between the two groups (p < 0.05). Drug prediction and corresponding cell experimental results showed that Trametinib, 5-fluorouracil, and AICAR significantly inhibited the viability of MCF-7 cells (p < 0.05). AC104211.1 and LINC01863 were found to impact the proliferation of BC cells.

CONCLUSION

The risk-scoring model obtained in this study may serve as a robust prognostic biomarker, potentially aiding in clinical decision-making for BC patients.

Shikonin reduces M2 macrophage population in ovarian cancer by repressing exosome production and the exosomal galectin 3-mediated β-catenin activation

BACKGROUND

Shikonin (SK), a naphthoquinone with anti-tumor effects, has been found to decrease production of tumor-associated exosomes (exo). This study aims to verify the treatment effect of SK on ovarian cancer (OC) cells, especially on the production of exo and their subsequent effect on macrophage polarization.

METHODS

OC cells SKOV3 and A2780 were treated with SK. The exo were isolated from OC cells with or without SK treatment, termed OC exo and SK OC exo, respectively. These exo were used to treat PMA-induced THP-1 cells (M0 macrophages). M2 polarization of macrophages was determined by measuring the M2 specific cell surface markers CD163 and CD206 as well as the secretion of M2 cytokine IL-10. The functions of galectin 3 (LGALS3/GAL3) and β-catenin in macrophage polarization were determined by gain- or loss-of-function assays. CB-17 SCID mice were subcutaneously injected with SKOV3 cells to generate xenograft tumors, followed by OC exo or SK OC exo treatment for in vivo experiments.

RESULTS

SK suppressed viability, migration and invasion, and apoptosis resistance of OC cells in vitro. Compared to OC exo, SK OC exo reduced the M2 polarization of macrophages. Regarding the mechanism, SK reduced exo production in cancer cells, and it decreased the protein level of GAL3 in exo and recipient macrophages, leading to decreased β-catenin activation. M2 polarization of macrophages was restored by LGALS3 overexpression but decreased again by the β-catenin inhibitor FH535. Compared to OC exo, the SK OC exo treatment reduced the xenograft tumor growth in mice, and it decreased the M2 macrophage infiltration within tumor tissues.

CONCLUSION

This study suggests that SK reduces M2 macrophage population in OC by repressing exo production and blocking exosomal GAL3-mediated β-catenin activation.

Identification of Subtypes in Triple-negative Breast Cancer Based on Shared Genes Between Immunity and Cancer Stemness

The correlation between triple-negative breast cancer (TNBC) and genes related to immunity and cancer stemness, particularly shared genes, remains unclear. This study aimed to investigate the correlation of immunity and cancer stemness with the molecular subtyping and survival rates in TNBC using bioinformatics approaches. Differential gene analysis was conducted to identify TNBC-associated differentially expressed genes (DEGs). Cancer stem cell (CSC)-related genes were obtained using weighted gene coexpression network analysis. Immune-related gene sets were retrieved from the literature. Venn analysis was performed to identify the shared DEGs between immunity and cancer stemness in TNBC. Cluster analysis and survival analysis based on the expression of these genes were conducted to identify TNBC subtypes with significant survival differences. A total of 5259 TNBC-associated DEGs, 2214 CSC-related genes, 1793 immune-related genes, and 44 shared DEGs between immunity and cancer stemness were obtained. Among them, 3 shared DEGs were closely associated with TNBC survival rates ( P <0.05). Cluster and survival analyses revealed that among 3 subtypes, cluster2 exhibited the best survival rate, and cluster3 showed the worst survival rate ( P <0.05). Dendritic cells were highly infiltrated in cluster2, while plasma cells and resting mast cells were highly infiltrated in cluster3 ( P <0.05). Genes shared by immunity and cancer stemness were capable of classifying TNBC samples. TNBC patients of different subtypes exhibited significant differences in immune profiles, genetic mutations, and drug sensitivity. These findings could provide new insights into the pathogenesis of TNBC, the immune microenvironment, and the selection of therapeutic targets for drug treatment.

Wnt signaling: Modulating tumor-associated macrophages and related immunotherapeutic insights

Wnt signaling pathways are highly conserved cascades that mediate multiple biological processes through canonical or noncanonical pathways, from embryonic development to tissue maintenance, but they also contribute to the pathogenesis of numerous cancers. Recent studies have revealed that Wnt signaling pathways critically control the interplay between cancer cells and tumor-associated macrophages (TAMs) in the tumor microenvironment (TME) and potentially impact the efficacy of cancer immunotherapy. In this review, we summarize the evidence that Wnt signaling pathways boost the maturation and infiltration of macrophages for immune surveillance in the steady state but also polarize TAMs toward immunosuppressive M2-like phenotypes for immune escape in the TME. Both cancer cells and TAMs utilize Wnt signaling to transmit signals, and this interaction is crucial for the carcinogenesis and progression of common solid cancers, such as colorectal, gastric, hepatocellular, breast, thyroid, prostate, kidney, and lung cancers; osteosarcoma; and glioma. Specifically, compared with those in solid cancers, Wnt signaling pathways play a distinct role in the pathogenesis of leukemia. Efforts to develop Wnt-based drugs for cancer treatment are still ongoing, and some indeed enhance the anticancer immune response. We believe that the combination of Wnt signaling-based therapy with conventional or immune therapies is a promising therapeutic approach and can facilitate personalized treatment for most cancers.

High LGALS3 expression induced by HCP5/hsa-miR-27b-3p correlates with poor prognosis and tumor immune infiltration in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is widely recognized for its unfavorable prognosis. Increasing evidence has revealed that LGALS3 has an essential function in initiating and developing several malignancies in humans. Nevertheless, thorough analysis of the expression profile, clinical prognosis, pathway prediction, and immune infiltration of LGALS3 has not been fully explored in HCC.

METHODS

In this study, an initial pan-cancer analysis was conducted to investigate the expression and prognosis of LGALS3. Following a comprehensive analysis, which included expression analysis and correlation analysis, noncoding RNAs that contribute to the overexpression of LGALS3 were subsequently identified. This identification was further validated using HCC clinical tissue samples. TIMER2 and GEPIA2 were employed to examine the correlation between LGALS3 and HCP5 with immunological checkpoints, cell chemotaxis, and immune infiltration in HCC. The R program was applied to analyze the expression distribution of immune score in in HCC patients with high and low LGALS3 expression. The expression profiles of immune checkpoints were also analyzed. Use R to perform GSVA analysis in order to explore potential signaling pathways.

RESULTS

First, we conducted pan-cancer analysis for LGALS3 expression level through an in-depth analysis of public databases and found that HCC has a high LGALS3 gene and protein expression level, which were then verified in clinical HCC specimens. Meanwhile, high LGALS3 gene expression is related to malignant progression and poor prognosis of HCC. Univariate and multivariate analyses confirmed that LGALS3 could serve as an independent prognostic marker for HCC. Next, by combining comprehensive analysis and validation on HCC clinical tissue samples, we hypothesize that the HCP5/hsa-miR-27b-3p axis could serve as the most promising LGALS3 regulation mechanism in HCC. KEGG and GO analyses highlighted that the LGALS3-related genes were involved in tumor immunity. Furthermore, we detected a significant positive association between LGALS3 and HCP5 with immunological checkpoints, cell chemotaxis, and immune infiltration. In addition, high LGALS3 expression groups had significantly higher immune cell scores and immune checkpoint expression levels. Finally, GSVA analysis was performed to predict potential signaling pathways linked to LGALS3 and HCP5 in immune evasion and metabolic reprogramming of HCC.

CONCLUSIONS

Our findings indicated that the upregulation of LGALS3 via the HCP5/hsa-miR-27b-3p axis is associated with unfavorable prognosis and increased tumor immune infiltration in HCC.

Exosomal ncRNAs: Multifunctional contributors to the immunosuppressive tumor microenvironment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignant liver cancer characterized by aggressive progression, unfavorable prognosis, and an increasing global health burden. Therapies that precisely target immunological checkpoints and immune cells have gained significant attention as possible therapeutics in recent years. In truth, the efficacy of immunotherapy is heavily contingent upon the tumor microenvironment (TME). Recent studies have indicated that exosomes serve as a sophisticated means of communication among biomolecules, executing an essential part in the TME of immune suppression. Exosomal non-coding RNAs (ncRNAs) can induce the activation of tumor cells and immunosuppressive immune cells that suppress the immune system, such as cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), tumor-associated neutrophils (TANs), CD+8 T cells, regulatory T cells (Tregs), and regulatory B cells (Bregs). This cell-cell crosstalk triggered by exosomal ncRNAs promotes tumor proliferation and metastasis, angiogenesis, malignant phenotype transformation, and drug resistance. Hence, it is imperative to comprehend how exosomal ncRNAs regulate tumor cells or immune cells within the TME to devise more comprehensive and productive immunotherapy programs. This study discusses the features of exosomal ncRNAs in HCC and how the activation of the exosomes redefines the tumor's immunosuppressive microenvironment, hence facilitating the advancement of HCC. Furthermore, we also explored the potential of exosomal ncRNAs as a viable biological target or natural vehicle for HCC therapy.

Construction of a prognostic model for lung adenocarcinoma based on m6A/m5C/m1A genes

BACKGROUND

Developing a prognostic model for lung adenocarcinoma (LUAD) that utilizes m6A/m5C/m1A genes holds immense importance in providing precise prognosis predictions for individuals.

METHODS

This study mined m6A/m5C/m1A-related differential genes in LUAD based on public databases, identified LUAD tumor subtypes based on these genes, and further built a risk prognostic model grounded in differential genes between subtypes. The immune status between high- and low-risk groups was investigated, and the distribution of feature genes in tumor immune cells was analyzed using single-cell analysis. Based on the expression levels of feature genes, a projection of chemotherapeutic and targeted drugs was made for individuals identified as high-risk. Ultimately, cell experiments were further verified.

RESULTS

The 6-gene risk prognosis model based on differential genes between tumor subtypes had good predictive performance. Individuals classified as low-risk exhibited a higher (P < 0.05) abundance of infiltrating immune cells. Feature genes were mainly distributed in tumor immune cells like CD4+T cells, CD8+T cells, and regulatory T cells. Four drugs with relatively low IC50 values were found in the high-risk group: Elesclomol, Pyrimethamine, Saracatinib, and Temsirolimus. In addition, four drugs with significant positive correlation (P < 0.001) between IC50 values and feature gene expression were found, including Alectinib, Estramustine, Brigatinib, and Elesclomol. The low expression of key gene NTSR1 reduced the IC50 value of irinotecan.

CONCLUSION

Based on the m6A/m5C/m1A-related genes in LUAD, LUAD patients were divided into 2 subtypes, and a m6A/m5C/m1A-related LUAD prognostic model was constructed to provide a reference for the prognosis prediction of LUAD.

Immune landscape of hepatocellular carcinoma tumor microenvironment identifies a prognostic relevant model

Background

Various studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression.

Methods

We integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients.

Results

To describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration.

Conclusions

Taken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.

H3K27 acetylation activated-PDLIM7 promotes castration-resistant prostate cancer progression by inducing O-Glycosylation of YAP1 protein

Castration-resistant prostate cancer (CRPC) is a fatal disease that evolves from prostate cancer due to drug resistance after long-term androgen deprivation therapy. In this study, we aimed to find novel molecular targets for treating CRPC. Through peptidome, we screened out polypeptides dysregulated in the serum of CRPC patients. According to RT-qPCR analysis and cell viability detection, we chose PDZ and LIM Domain 7 (PDLIM7) as the research object. As demonstrated by loss-of-function assays, silencing of PDLIM7 could suppress CRPC cell proliferation, migration, and angiogenesis. Moreover, PDLIM7 knockdown enhanced the sensitivity of CRPC cells to docetaxel treatment. Subsequently, we found that CBP/p300 increases the H3K27ac level in the PDLIM7 promoter to activate PDLIM7. Mechanism experiments such as IP and western blot revealed that PDLIM7 interacted with YAP1 to induce O-Glycosylation of YAP1 and thus stabilize YAP1 protein. Rescue assays demonstrated that PDLIM7 promoted the malignant processes of CRPC cells through YAP1. Finally, an animal study validated that PDLIM7 aggravated tumor growth. In conclusion, our findings highlighted the oncogenic role of PDLIM7 upregulated by CBP/p300-induced H3K27ac enhancement in CRPC by stabilizing YAP1.

Immune Evasion in Cancer Is Regulated by Tumor-Asociated Macrophages (TAMs): Targeting TAMs

Recent advancements in cancer treatment have explored a variety of approaches to address the needs of patients. Recently, immunotherapy has evolved as an efficacious treatment for various cancers resistant to conventional therapies. Hence, significant milestones in immunotherapy were achieved clinically in a large subset of cancer patients. Unfortunately, some cancer types do not respond to treatment, and among the responsive cancers, some patients remain unresponsive to treatment. Consequently, there is a critical need to examine the mechanisms of immune resistance and devise strategies to target immune suppressor cells or factors, thereby allowing for tumor sensitivity to immune cytotoxic cells. M2 macrophages, also known as tumor-associated macrophages (TAMs), are of interest due to their role in suppressing the immune system and influencing antitumor immune responses through modulating T cell activity and immune checkpoint expression. TAMs are associated with signaling pathways that modulate the tumor microenvironment (TME), contributing to immune evasion. One approach targets TAMs, focusing on preventing the polarization of M1 macrophages into the protumoral M2 phenotype. Other strategies focus on direct or indirect targeting of M2 macrophages through understanding the interaction of TAMs with immune factors or signaling pathways. Clinically, biomarkers associated with TAMs' immune resistance in cancer patients have been identified, opening avenues for intervention using pharmacological agents or immunotherapeutic approaches. Ultimately, these multifaceted approaches are promising in overcoming immune resistance and improving cancer treatment outcomes.

Unleashing the potential of gene signatures as prognostic and predictive tools: A step closer to personalized medicine in hepatocellular carcinoma (HCC)

Hepatocellular carcinoma (HCC) is one of the growing malignancies globally, affecting a myriad of people and causing numerous cancer-related deaths. Despite therapeutic improvements in treatment strategies over the past decades, HCC still remains one of the leading causes of person-years of life lost. Numerous studies have been conducted to assess the characteristics of HCC with the aim of predicting its prognosis and responsiveness to treatment. However, the identified biomarkers have shown limited sensitivity, and the translation of these findings into clinical practice has faced challenges. The development of sequencing techniques has facilitated the exploration of a wide range of genes, leading to the emergence of gene signatures. Although several studies assessed differentially expressed genes in normal and HCC tissues to find the unique gene signature with prognostic value, to date, no study has reviewed the task, and to the best of our knowledge, this review represents the first comprehensive analysis of relevant studies in HCC. Most gene signatures focused on immune-related genes, while others investigated genes related to metabolism, autophagy, and apoptosis. Even though no identical gene signatures were found, NDRG1, SPP1, BIRC5, and NR0B1 were the most extensively studied genes with prognostic value. Finally, despite challenges such as the lack of consistent patterns in gene signatures, we believe that comprehensive analysis of pertinent gene signatures will bring us a step closer to personalized medicine in HCC, where treatment strategies can be tailored to individual patients based on their unique molecular profiles.

Biomarkers predicting the efficacy of immune checkpoint inhibitors in hepatocellular carcinoma

In recent years, immune checkpoint inhibitors (ICIs) have emerged as a transformative approach in treating advanced hepatocellular carcinoma (HCC). Despite their success, challenges persist, including concerns about their effectiveness, treatment costs, frequent occurrence of treatment-related adverse events, and tumor hyperprogression. Therefore, it is imperative to identify indicators capable of predicting the efficacy of ICIs treatment, enabling optimal patient selection to maximize clinical benefits while minimizing unnecessary toxic side effects and economic losses. This review paper categorizes prognostic biomarkers of ICIs treatment into the following categories: biochemical and cytological indicators, tumor-related markers, imaging and personal features, etiology, gut microbiome, and immune-related adverse events (irAEs). By organizing these indicators systematically, we aim to guide biomarker exploration and inform clinical treatment decisions.

Inflammation-related prognostic markers in resected hepatocellular carcinoma

Hepatocellular carcinoma is usually detected late and therapeutic options are unsatisfactory. Despite marked progress in patient care, HCC remains among the deadliest cancers world-wide. While surgical resection remains a key option for early-stage HCC, the 5-year survival rates after surgical resection are limited. One reason for limited outcomes is the lack of reliable prognostic biomarkers to predict HCC recurrence. HCC prognosis has been shown to correlate with different systemic and pathological markers which are associated with patient survival and HCC recurrence. Liver inflammatory processes offer a large variety of systemic and pathological markers which may be exploited to improve the reliability of prognosis and decision making of liver surgeons and hepatologists. The following review aims to dissect the potential tools, targets and prognostic meaning of inflammatory markers in patients with resectable HCC. We analyze changes in circulant cellular populations and assess inflammatory biomarkers as a surrogate of impaired outcomes and provide an overview on predictive gene expression signatures including inflammatory transcriptional patterns, which are representative of poor survival in these patients.

Cellular senescence-related genes: predicting prognosis in hepatocellular carcinoma

Recent studies have shown that the high incidence and low cure rate of hepatocellular carcinoma (HCC) have not improved significantly. Surgery and liver transplantation are the mainstays of prolonging the survival of HCC patients. However, the surgical resection rate of HCC patients is very low, and even after radical surgical resection, the recurrence rate at 5 years postoperatively remains high and the prognosis is very poor, so more treatment options are urgently needed. Increasing evidence suggests that cellular senescence is not only related to cancer development but may also be one of its primary driving factors. We aimed to establish a prognostic signature of senescence-associated genes to predict the prognosis and therapeutic response of HCC patients. The aim of this study was to develop a risk model associated with cellular senescence and to search for potential strategies to treat HCC. We divided HCC patients into two clusters and identified differentially expressed genes (DEGs) between clusters. In this study, low-risk patients had a better prognosis, higher levels of immune cell infiltration, and better efficacy to fluorouracil, Paclitaxel and Cytarabine chemotherapy compared to high-risk patients. To further identify potential biomarkers for HCC, we further validated the expression levels of the four signature genes in HCC and neighbouring normal tissues by in vitro experiments. In conclusion, we identified and constructed a relevant prognostic signature, which performed well in predicting the survival and treatment response of HCC patients. This helps to differentiate between low-score and high-risk HCC, and the results may contribute to precise treatment protocols in clinical practice.

Targeting the Heterogeneous Tumour-Associated Macrophages in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a prevalent and aggressive cancer that comprises a complex tumour microenvironment (TME). Tumour-associated macrophages (TAMs) are one of the most abundant immune cells present in the TME, and play a key role both in the development and in the progression of HCC. Thus, TAM-based immunotherapy has been presented as a promising strategy to complement the currently available therapies for HCC treatment. Among the novel approaches focusing on TAMs, reprogramming their functional state has emerged as a promising option for targeting TAMs as an immunotherapy in combination with the currently available treatment options. Nevertheless, a further understanding of the immunobiology of TAMs is still required. This review synthesizes current insights into the heterogeneous nature of TAMs in HCC and describes the mechanisms behind their pro-tumoural polarization focusing the attention on their interaction with HCC cells. Furthermore, this review underscores the potential involvement of TAMs' reprogramming in HCC therapy and highlights the urgency of advancing our understanding of these cells within the dynamic landscape of HCC.

The core role of macrophages in hepatocellular carcinoma: the definition of molecular subtypes and the prognostic risk system

Background: In patients with hepatocellular carcinoma (HCC), the tumor microenvironment (TME) is resistant to immunotherapy because of its specificity. It is meaningful to explore the role of macrophage, which is one of the most abundant immune cells in the TME, in cellular communication and its effect on the prognosis and immunotherapy of HCC. Methods: Dimensionality reduction and clustering of the single-cell RNA-seq data from the GSE149614 dataset were carried out to identify the cellular composition of HCC. CellChat was used to analyze the communication between different cells. The specifically highly expressed genes of macrophages were extracted for univariate Cox regression analysis to obtain prognostic genes for HCC cluster analysis, and the risk system of macrophage-specifically highly expressed genes was developed by random forest analysis and multivariate Cox regression analysis. Prognosis, TME infiltration, potential responses to immunotherapy, and antineoplastic drugs were compared among molecular subtypes and between risk groups. Results: We found that HCC included nine identifiable cell types, of which macrophages had the highest communication intensity with each of the other eight cell types. Of the 179 specifically highly expressed genes of macrophage, 56 were significantly correlated with the prognosis of HCC, which classified HCC into three subtypes, which were reproducible and produced different survival outcomes, TME infiltration, and immunotherapy responses among the subtypes. In the integration of four macrophage-specifically highly expressed genes for the development of a risk system, the risk score was significantly involved in higher immune cell infiltration, poor prognosis, immunotherapy response rate, and sensitivity of six drugs. Conclusion: In this study, through single-cell RNA-seq data, we identified nine cell types, among which macrophage had the highest communication intensity with the rest of the cell types. Based on specifically highly expressed genes of macrophage, we successfully divided HCC patients into three clusters with distinct prognosis, TME, and therapeutic response. Additionally, a risk system was constructed, which provided a potential reference index for the prognostic target and preclinical individualized treatment of HCC.

TRAF2 promotes M2-polarized tumor-associated macrophage infiltration, angiogenesis and cancer progression by inhibiting autophagy in clear cell renal cell carcinoma

BACKGROUND

The management of advanced clear cell renal cell carcinoma (ccRCC) remains a major challenge in clinical practice, and the construction of more reliable prognostic prediction models and the further elucidation of key molecular mechanisms of tumor progression are topics in urgent need of in-depth investigation.

METHODS

We used CIBERSORT to estimate the proportion of 22 tumor-infiltrating immune cell types in the TCGA-KIRC cohort. Weighted gene co-expression network analysis, least absolute shrinkage and selection operator regression analysis were used to build risk prediction models. Expression patterns and clinical significance of TRAF2 were determined through bioinformatics analysis, real-time qPCR, Western Blot, immunohistochemistry. GSEA analysis, transmission electron microscopy, 2D/3D colony formation assay, cell migration and invasion assay, and tube-formation assay were used to investigate the underlying function and mechanism of the TRAF2/M2 macrophage/autophagy axis.

RESULTS

We constructed a novel prognostic prediction model based on M2 macrophage-related genes, which was identified as an accurate, independent and specific prognostic risk model for ccRCC patients. A reliable nomogram was constructed to predict 1-, 3-, and 5-year overall survival for patients with ccRCC. As one of the constituent genes of the risk model, TRAF2 was determined to be upregulated in ccRCC and associated with poor clinical prognosis. We found that TRAF2 promotes malignant progression of ccRCC by regulating macrophage polarization, migration and angiogenesis. Mechanistically, we found that TRAF2 promotes the polarization of M2 macrophages, and this chemotaxis is achieved in an autophagy-dependent pathway. Orthotopic tumor growth assay results revealed that TRAF2 plays a key role as a promotor of ccRCC growth and metastasis.

CONCLUSIONS

In conclusion, this risk model is highly predictive of prognostic in ccRCC patients, which is expected to promote improved treatment evaluation and comprehensive management of ccRCC. Moreover, our findings reveal that the TRAF2/M2 macrophage/autophagy axis plays a key regulatory role in the malignant progression of ccRCC, and suggest that TRAF2 is a potential novel therapeutic target for advanced ccRCC.

A DNA Replication Stress-Based Prognostic Model for Lung Adenocarcinoma

Tumor cells endure continuous DNA replication stress, which opens the way to cancer development. Despite previous research, the prognostic implications of DNA replication stress on lung adenocarcinoma (LUAD) have yet to be investigated. Here, we aimed to investigate the potential of DNA replication stress-related genes (DNARSs) in predicting the prognosis of individuals with LUAD. Differentially expressed genes (DEGs) originated from the TCGA-LUAD dataset, and we constructed a 10-gene LUAD prognostic model based on DNARSs-related DEGs (DRSDs) using Cox regression analysis. The receiver operating characteristic (ROC) curve demonstrated excellent predictive capability for the LUAD prognostic model, while the Kaplan-Meier survival curve indicated a poorer prognosis in a high-risk (HR) group. Combined with clinical data, the Riskscore was found to be an independent predictor of LUAD prognosis. By incorporating Riskscore and clinical data, we developed a nomogram that demonstrated a capacity to predict overall survival and exhibited clinical utility, which was validated through the calibration curve, ROC curve, and decision curve analysis curve tests, confirming its effectiveness in prognostic evaluation. Immune analysis revealed that individuals belonging to the low-risk (LR) group exhibited a greater abundance of immune cell infiltration and higher levels of immune function. We calculated the immunopheno score and TIDE scores and tested them on the IMvigor210 and GSE78220 cohorts and found that individuals categorized in the LR group exhibited a higher likelihood of deriving therapeutic benefits from immunotherapy intervention. Additionally, we predicted that patients classified in the HR group would demonstrate enhanced sensitivity to Docetaxel using anti-tumor drugs. To summarize, we successfully developed and validated a prognostic model for LUAD by incorporating DNA replication stress as a key factor.

Sulfatinib, a novel multi-targeted tyrosine kinase inhibitor of FGFR1, CSF1R, and VEGFR1-3, suppresses osteosarcoma proliferation and invasion via dual role in tumor cells and tumor microenvironment

Introduction

Tumor progression is driven by intrinsic malignant behaviors caused by gene mutation or epigenetic modulation, as well as crosstalk with the components in the tumor microenvironment (TME). Considering the current understanding of the tumor microenvironment, targeting the immunomodulatory stromal cells such as cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs) could provide a potential therapeutic strategy. Here, we investigated the effect of sulfatinib, a multi-targeted tyrosine kinase inhibitor (TKI) of FGFR1, CSF1R, and VEGFR1-3, on the treatment of osteosarcoma (OS).

Methods

In vitro, the antitumor effect was tested by clony formation assay and apoptosis assay.The inhibition of tumor migration and invasion was detected by Transwell assay, and the de-polarization of macrophage was detected by flow cytometry.In vivo, subcutaneous and orthotopic tumor models were established to verify antitumor effect, and the underlying mechanism was verified by immunohistochemistry(IHC), immunofluorescence(IF) and flow cytometry.

Results

Sulfatinib suppressed OS cell migration and invasion by inhibiting epithelial-mesenchymal transition (EMT) by blocking the secretion of basic fibroblast growth factor (bFGF) in an autocrine manner. In addition, it regulated immune TME via inhibition of the migration of skeletal stem cells (SSCs) to the TME and the differentiation from SSCs to CAFs. Moreover, sulfatinib can suppress OS by modulation of the TME by inhibiting M2 polarization of macrophages. Systemic treatment of sulfatinib can reduce immunosuppression cells M2-TAMs, Tregs, and myeloid-derived suppressor cells (MDSCs) and increase cytotoxic T-cell infiltration in tumors, the lungs, and the spleens.

Discussion

Our preclinical experiments have shown that sulfatinib can inhibit the proliferation, migration, and invasion of OS by playing a dual role on tumor cells and the tumor microenvironment simultaneously and systematically reverse immunosuppression to immune activation status, which could be translated into clinical trials.

Cancer-derived mutation in the OGA stalk domain promotes cell malignancy through dysregulating PDLIM7 and p53

O-GlcNAcase (OGA) is the sole enzyme that hydrolyzes O-GlcNAcylation from thousands of proteins and is dysregulated in many diseases including cancer. However, the substrate recognition and pathogenic mechanisms of OGA remain largely unknown. Here we report the first discovery of a cancer-derived point mutation on the OGA's non-catalytic stalk domain that aberrantly regulated a small set of OGA-protein interactions and O-GlcNAc hydrolysis in critical cellular processes. We uncovered a novel cancer-promoting mechanism in which the OGA mutant preferentially hydrolyzed the O-GlcNAcylation from modified PDLIM7 and promoted cell malignancy by down-regulating p53 tumor suppressor in different types of cells through transcription inhibition and MDM2-mediated ubiquitination. Our study revealed the OGA deglycosylated PDLIM7 as a novel regulator of p53-MDM2 pathway, offered the first set of direct evidence on OGA substrate recognition beyond its catalytic site, and illuminated new directions to interrogate OGA's precise role without perturbing global O-GlcNAc homeostasis for biomedical applications.

Identification of Prognosis-Related Oxidative Stress Model with Immunosuppression in HCC

For hepatocellular carcinoma (HCC) patients, we attempted to establish a new oxidative stress (OS)-related prognostic model for predicting prognosis, exploring immune microenvironment, and predicting the immunotherapy response. Significantly differently expressed oxidative stress-related genes (DEOSGs) between normal and HCC samples from the Cancer Genome Atlas (TCGA) were screened, and then based on weighted gene coexpression network analysis (WGCNA), HCC-related hub genes were discovered. Based on the least absolute shrinkage and selection operator (LASSO) and cox regression analysis, a prognostic model was developed. We validated the prognostic model’s predictive power using an external validation cohort: the International Cancer Genome Consortium (ICGC).Then a nomogram was determined. Furthermore, we also examined the relationship of the risk model and clinical characteristics as well as immune microenvironment. 434 DEOSGs, comprising 62 downregulated and 372 upregulated genes (p < 0.05 and |log2FC| ≥ 1), and 257 HCC-related hub genes were recognized in HCC. Afterward, we built a five-DEOSG (LOX, CYP2C9, EIF2B4, EZH2, and SRXN1) prognostic risk model. Using the nomogram, the risk model was shown to have good prognostic value. Compared to the low risk group, HCC patients with high risk had poorer outcomes, worse pathological grades, and advanced tumor stages (p < 0.05). There were significant increases in LOX, EIF2B4, EZH2, and SRXN1 expression in HCC samples, while CYP2C9 expression was decreased. Finally, Real-time PCR (RT-qPCR) confirmed the mRNA expressions of five genes (CYP2C9, EIF2B4, EZH2, SRXN1, LOX) in HCC cell lines. Our study constructed a prognostic OS-related model with strong predictive power and potential as an immunosuppressive biomarker for HCC leading to improving prediction and providing new insights for HCC immunotherapy.

KLF2 is a clinical diagnostic and treatment biomarker of breast cancer

Background: As a highly prevalent malignancy among women worldwide, breast cancer, remains a critical public health issue necessitating the development of novel therapeutics and biomarkers. Kruppel Like Factor 2 (KLF2), a member of the Kruppel family of transcription factors, has been implicated in various types of cancer due to its diminished expression; however, the potential implications of KLF2 expression in relation to breast cancer progression, prognosis, and therapy remain unclear. Methods: The present study employed the Tumor Immune Estimation Resource (TIMER) and The Human Protein Atlas databases to investigate the expression pattern of KLF2 in pan-cancer. The relationship between KLF2 expression and clinical features or immune infiltration of The Cancer Genome Atlas (TCGA) breast cancer samples was evaluated using Breast Cancer Integrative Platform (BCIP) and TIMER. The expression levels of KLF2 in breast cancer were validated via immunohistochemical staining analysis. Gene Set Enrichment Analysis (GSEA) to study the KLF2-related gene ontology. STRING database was employed to construct a protein-protein interaction (PPI) network of KLF2 in relation to vascular endothelial growth factor A (VEGFA) and hypoxia-inducible factor 1α (HIF1α). The expression of KLF2 following diverse breast cancer therapies was analyzed in the Gene Expression Omnibus (GEO) databases. The expression of KLF2 following treatment with simvastatin was validated via immunofluorescence and western blotting. Results: Our study reveals that KLF2 displays significantly reduced expression in cancerous tissues compared to non-cancerous controls. Patients with low KLF2 expression levels exhibited poor prognosis across multiple cancer types. KLF2 expression levels were found to be reduced in advanced cancer stages and grades, while positively correlated with the expression of estrogen receptor (ER), progesterone receptor (PR), and tumor size in breast cancer. KLF2 expression is associated with diverse immune infiltration cells, and may impact the breast tumor immune microenvironment by regulating dendritic cell activation. Additionally, we observed a negative correlation between KLF2 expression levels and angiogenesis, as well as the expression of VEGFA and HIF1α. Notably, the anticancer drug simvastatin could induce KLF2 expression in both breast cancer. Conclusion: Based on our observations, KLF2 has potential as a diagnostic, prognostic, and therapeutic biomarker for breast cancer.

m6A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma

Background. m6A modification plays a key role in the development of hepatocellular carcinoma (HCC). Angiogenesis-related genes (ARGs) are increasingly being used to define signatures predicting patient prognosis. The correlations between m6A-related ARGs (mARGs), clinical outcomes, and the immune and oxidative stress landscape are unclear. Methods. Univariate Cox regression analysis of 24 mARGs yielded 13 prognostic genes, which were then analyzed for their enriched functions and pathways. After LASSO regression analysis, a prognostic signature was constructed and its reliability validated. Patients were grouped by risk using the signature score, and then the clinical prognosis, the immune landscape, and the oxidative stress landscape between the two groups were analyzed. Drug sensitivity analysis was performed to identify potentially efficient therapeutic agents. Results. Thirteen prognosis-related mARGs consistently clustered patients with HCC into four groups with significantly different prognosis. Four mARGs (EGF, ITGA5, ITGAV, and PLG) were used to construct a prognostic signature and define risk groups. Among them, EGF, ITGA5, and ITGAV, were defined as prognostic risk factors, while PLG was defined as a prognostic protective factor. Compared to low-risk patients, HCC patients in the high-risk group had a poorer prognosis and showed significant differences in clinical characteristics, enriched pathways, tumor stemness, and tumor microenvironment. The drug sensitivity of oxaliplatin and LDK-378 negatively correlated with ITGAV expression. Ten drugs had lower IC50s in the high-risk group, indicating better antitumor efficacy than in the low-risk group, with epothilone B having the lowest IC50 value. Conclusions. A prognostic model consisting of mARGs can be used to predict the prognosis of HCC patients. The risk grouping of our model can be used to reveal differences in the tumor immune microenvironment of patients with HCC. Further in-depth study may provide new targets for future treatment.