Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic

Improving the efficacy of cancer immunotherapy by host-defence caerin 1.1 and 1.9 peptides

Cancer remains a major global health challenge. Immunotherapy has revolutionized the management of cancer, yet only a limited number of patients respond to such treatments. This is largely attributed to the immunosuppressive tumor microenvironment, which diminishes the effectiveness of immunotherapy. Recent studies have underscored the potential of naturally derived caerin 1 peptides, particularly caerin 1.1 and caerin 1.9, which exhibit strong antitumor effects and enhance the efficacy of immunotherapies in animal models. This review encapsulates the current research aimed at augmenting the effectiveness of immunotherapy, focusing on the role of caerin 1.1 and caerin 1.9 in boosting immunotherapeutic outcomes, elucidating possible mechanisms, and discussing their limitations and challenges.

Elucidating the immune landscape and potential prognostic model in acute myeloid leukemia with TP53 mutation

OBJECTIVES

The TP53 mutation, a prevalent tumor suppressor gene alteration, is linked to chemotherapy resistance, increased relapse rates and diminished overall survival (OS) in acute myeloid leukemia (AML) patients.

METHODS

In this study, we characterize the TP53 mutation phenotypes across various AML cohorts utilizing The Cancer Genome Atlas (TCGA) data. We devised a TP53-related prognostic signature derived from differentially expressed genes between mutated and wild-type TP53 AML specimens. In-depth analyses were conducted, encompassing genetic variation, immune cell infiltration and prognostic stratification.

RESULTS

A six-gene TP53-related signature was established using least absolute shrinkage and selection operator (LASSO)-Cox regression, demonstrating robust prognostic predictability. This signature exhibited strong performance in both the OHSU validation cohorts, an independent Gene Expression Omnibus (GEO) validation cohort (GSE71014) and proved by results of the in vivo experiment. Finally, we used single cell database (GSE198681) to observe the characteristics of these six genes.

DISCUSSION

Our study may facilitate the development of efficacious therapeutic approaches and provide a novel idea for future research. Conclusion: The TP53-related signature and pattern hold the potential to refine prognostic stratification and underscore emerging targeted therapies.

Different characteristics of the tumor immune microenvironment among subtypes of salivary gland cancer

AIM

Although immune checkpoint inhibitors (ICPi) for salivary gland cancer (SGC) have been investigated in clinical trials, details of the tumor immune microenvironment (TIME) remain unclear. This research aimed to elucidate the TIME of SGC and its relationship with tumor mutation burden (TMB) and to explore the rationale for the applicability of ICPi.

MATERIALS AND METHODS

We selected five pathological types, namely adenoid cystic carcinoma (ACC); adenocarcinoma, not otherwise specified (ANOS); salivary duct carcinoma (SDC); and low/high-grade mucoepidermoid carcinoma (MEClow/high). We investigated the TIME and TMB of each pathological type. TIME was evaluated by multiplexed fluorescent immunohistochemistry. TMB was measured by next-generation sequencing.

RESULTS

ACC and MEChigh showed the lowest and highest infiltration of immune effector and suppressor cells in both tumor and stroma. ANOS, SDC, and MEClow showed modest infiltration of immune effector cells in tumors. Correlation analysis showed a positive correlation between CD3+CD8+ T cells in tumor and TMB (r = 0.647). CD3+CD8+ T cells in tumors showed a positive correlation with programmed cell death-ligand 1 expression in tumor cells (r = 0.513) and a weak positive correlation with CD3+CD4+Foxp3+ cells in tumors (r = 0.399). However, no correlation was observed between CD3+CD8+ T cells and CD204+ cells in tumors (r = -0.049).

CONCLUSION

The TIME of ACC was the so-called immune desert type, which may explain the mechanisms of the poor response to ICPi in previous clinical trials. On the other hand, MEChigh was the immune-inflamed type, and this may support the rationale of ICPi for this pathological subtype.

Comprehensive pan-cancer analysis unveils the significant prognostic value and potential role in immune microenvironment modulation of TRIB3

TRIB3, a pseudokinase, was previously studied within only some specific cancer types, leaving its comprehensive functions in pan-cancer contexts largely unexplored. Here, we performed an integrated analysis of TRIB3 expression, prognosis, genetic alterations, functional enrichment and tumor immune-related characteristics in 33 cancer types. Our results showed that TRIB3 exhibits high expression levels across 24 different cancer types and correlates closely with unfavorable prognoses. Meanwhile, TRIB3 shows mutations in a wide spectrum of 22 distinct cancer types, with the predominant mutation types being missense mutations and gene amplifications, and significant changes in DNA methylation levels in 14 types of cancer. We further discovered that TRIB3 expression is significantly associated with cancer immune-related genome mutations, such as tumor mutational burden (TMB), microsatellite instability (MSI) and DNA mismatch repair (MMR), and infiltration of immunosuppressive cells, such as CD4+ Th2 cells and myeloid-derived suppressor cells (MDSCs), into the tumor microenvironment. These results indicated that the expression of TRIB3 might reshape the tumor immune microenvironment (TIME) and lead to immunosuppressive "cold" tumors. In addition, our results confirmed that the loss of function of TRIB3 inhibits cell proliferation, promotes apoptosis, and leads to significant enrichment of "hot" tumor-related immune pathways, at least in breast cancer cells, which further supports the important role of TRIB3 in cancer prognosis and TIME regulation. Together, this pan-cancer investigation provided a comprehensive understanding of the critical role of TRIB3 in human cancers, and suggested that TRIB3 might be a promising prognostic biomarker and a potential target for cancer immunotherapy.

Revolutionizing prognostic predictions in colorectal cancer: Macrophage‑driven transcriptional insights from single‑cell RNA sequencing and gene co‑expression network analysis

Tumor-associated macrophages have become important biomarkers for cancer diagnosis, prognosis and therapy. The dynamic changes in macrophage subpopulations significantly impact the outcomes of cancer immunotherapy. Hence, identifying additional macrophage-related biomarkers is essential for enhancing prognostic predictions in colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression Omnibus (GEO) database. The data were processed, normalized and clustered using the 'Seurat' package. Cell types within each cluster were annotated using the 'SingleR' package. Weighted gene co-expression network analysis identified modules corresponding to specific cell types. A non-negative matrix factorization algorithm was employed to segregate different clusters based on the selected module. Differentially expressed genes (DEGs) were identified across various clusters and a prognostic model was constructed using lasso regression and Cox regression analyses. The robustness of the model was validated using The Cancer Genome Atlas (TCGA) database and GEO microarrays. Additionally, the prognosis, immune characteristics and response to immune checkpoint inhibitor (ICI) therapy were individually analyzed. The scRNA-seq data from GSE200997, consisting of 23 samples, were analyzed. Dimensionality reduction and cluster identification allowed the isolation of the primary myeloid cell subpopulations. The macrophage-related brown module was identified, which was further divided into two clusters. Using the DEGs from these clusters, a prognostic model was developed, comprising five macrophage-related genes. The robustness of the model was confirmed using microarray datasets GSE17536, GSE38832 and GSE39582, as well as TCGA cohort. Patients classified as high-risk by the present model exhibited poorer survival rates, lower tumor mutation burden, reduced microsatellite instability, lower tumor purity, more severe tumor immune dysfunction and exclusion, and less benefit from ICIs therapy compared with low-risk patients. The present prognostic model shows promise as a biomarker for risk stratification and predicting therapeutic efficacy in patients with CRC. However, further well-designed prospective studies are necessary to validate the findings.

Liquid-liquid phase separation-related signature predicts prognosis and therapeutic response in esophageal adenocarcinoma

BACKGROUND

Esophageal adenocarcinoma is a leading cause of mortality worldwide. New evidence indicates that liquid-liquid phase separation is related to malignancies. The current study aims at exploring the functions of liquid-liquid phase separation within esophageal adenocarcinoma. Patients within the TCGA dataset were classified using liquid-liquid phase separation-related genes. Significantly differentially expressed genes and prognostic factors for overall survival have been screened by Cox regression. Based on the liquid-liquid phase separation score, the construction of a prognostic model and liquid-liquid phase separation signature was constructed. Tumor mutation burden and drug sensitivity were analyzed in two groups: high liquid-liquid phase separation scores, and low liquid-liquid phase separation scores. According to liquid-liquid phase separation, some small-molecule compounds targeting esophageal adenocarcinoma were screened. The results were verified in vitro with an external cohort.

RESULTS

87 samples are involved, and 61 liquid-liquid phase separation-related genes may influence esophageal adenocarcinoma by changing DNA conformation and metabolism. Meanwhile, based on a high liquid-liquid phase separation score and low score group including 43 patients, it is found that the result significantly lowered the 5-year overall survival to 32.6 %, compared to 64.8 % in the low-score group of 44 patients with p < 0.001. The high score group had an average TIDE score of 0.27 versus 0.14 in the low-score group, with p = 0.003. The median tumor mutation burden was 9.1 mutations/Mb in the high-score group versus 6.4 mutations/Mb in the low-score group, with p = 0.011. The predictive model worked very well, with area under the curve values of 0.82, 0.79, and 0.76 for 1-, 3-, and 5-year survival, respectively. Liquid-liquid phase separation has been validated as an effective prognostic biomarker and drug sensitivity predictor.

SIGNIFICANCE

Liquid-liquid phase separation is potentially implicated in esophageal adenocarcinoma and works as a prognostic biomarker assessment of vulnerability to LLPS, which could help develop individualized therapies by showing how one is situated about various medications where responses vary across the body.

The hallmarks of cancer immune evasion

According to the widely accepted "three Es" model, the host immune system eliminates malignant cell precursors and contains microscopic neoplasms in a dynamic equilibrium, preventing cancer outgrowth until neoplastic cells acquire genetic or epigenetic alterations that enable immune escape. This immunoevasive phenotype originates from various mechanisms that can be classified under a novel "three Cs" conceptual framework: (1) camouflage, which hides cancer cells from immune recognition, (2) coercion, which directly or indirectly interferes with immune effector cells, and (3) cytoprotection, which shields malignant cells from immune cytotoxicity. Blocking the ability of neoplastic cells to evade the host immune system is crucial for increasing the efficacy of modern immunotherapy and conventional therapeutic strategies that ultimately activate anticancer immunosurveillance. Here, we review key hallmarks of cancer immune evasion under the "three Cs" framework and discuss promising strategies targeting such immunoevasive mechanisms.

Genetic Alterations in Chromatin Regulatory Genes in Upper Tract Urothelial Carcinoma and Urothelial Bladder Cancer

PURPOSE

Upper tract urothelial carcinoma (UTUC) and urothelial carcinoma of the bladder (UCB) share histomorphological and therapeutic features but distinct epidemiologic and clinicopathologic characteristics. We examined alterations of chromatin regulatory genes in molecular subtypes, clonal relatedness, and T-cell receptor (TCR) diversity in UTUC and UCB.

MATERIALS AND METHODS

Targeted next-generation sequencing or whole-exome DNA sequencing and TCR sequencing were conducted with 34 UTUC and 49 UCB specimens from 63 patients. Tumors were subtyped based on the expression of CK5 and GATA3. Results of tissue microarray of 78 muscle-invasive bladder cancer (MIBC) samples were used as prognostic factors of different subtypes of MIBC.

RESULTS

Chromatin regulatory genes were frequently mutated in both UTUC and UCB. Rapid relapse and progression of non-MIBC are correlated with alterations of KMT2C and EP300. Frequency of alterations in chromatin regulatory genes is higher in UTUC patients with SBS22 and SBS2 signatures and lower in UCB patients with SBS2 and SBS6 signatures. GATA3 and CK5 double-positive patients with higher frequencies of SMARCA4, ARID1A, and EP300 mutations have better prognoses than patients with basal subtypes. Although UTUC and UCB in the same patient can be either clonally related or developed independently, mutated genes in chromatin pathway were enriched in the related clones. Compared to UTUC, UCB had more deleterious mutations in DNA damage repair (DDR) genes, higher levels of tumor mutation burden (TMB) and copy number variations (CNVs), as well as higher TCR clonality and lower TCR diversity.

CONCLUSIONS

Since genetic alterations of the chromatin pathway genes are important in both UTUC and UCB, they could serve as potential biomarkers for predicting disease progression and therapeutic targets. Differences in mutation frequencies of DDR pathway, TMB, CNV, and TCR might be the contributing factors for the distinct responses to immune checkpoint inhibitor (ICI) between UTUC and UCB.

FAM136A as a Diagnostic Biomarker in Esophageal Cancer: Insights into Immune Infiltration, m6A Modification, Alternative Splicing, Cuproptosis, and the ceRNA Network

FAM136A promotes the progression and metastasis of various tumors. However, there are few studies on the role of FAM136A in esophageal cancer (ESCA). The TCGA, GTEx, and GEO databases are employed to analyze the expression of FAM136A in ESCA, and qPCR and TMA experiments are performed for validation. Enrichment analyzes are performed to investigate the association of FAM136A expression with immune features, m6A modification, alternative splicing, cuproptosis, and the ceRNA network via bioinformatics analysis. FAM136A is highly expressed in ESCA and correlated with lymph node metastasis and overall survival (OS). Bioinformatics analysis suggested that FAM136A may participate in the following processes to promote ESCA development and progression: 1) Promotion of mast cells infiltration to influence the ESCA immune microenvironment, 2) HNRNPC upregulation to regulate m6A modification, 3) ALYREF upregulation to increase the occurrence of retained intron (RI) events, 4) CDK5RAP1 upregulation to achieve inhibition of tumor cell apoptosis, and 5) promotion of ESCA progression through the lncRNA SNHG15/hsa-miR-29c-3p/FAM136A ceRNA network. FAM136A is a potential biomarker for ESCA diagnosis and treatment response evaluation, and the underlying mechanisms may be associated with immune infiltration, m6A modification, alternative splicing, cuproptosis, and the ceRNA regulatory network.

The effect of nursing intervention combined with PD-1 inhibitor on platelets, white blood cells, tumor markers and quality of life in patients with lung cancer

Tumor immunotherapy has become one of the important directions in the field of anti-tumor research. Among them, programmed death molecule-1 (PD-1) and its ligand (PD-L1) inhibitors have attracted considerable attention. This study analyzed the application effects of PD-1 inhibitors assisted nursing intervention in patients with lung cancer (LC). Sixty-eight patients with LC were divided into research group and control group randomly. Control group was treated with PD-1 inhibitor chemotherapy. Research group was treated with PD-1 inhibitors as auxiliary nursing intervention. Platelets, immune function indexes, tumor markers, and white blood cells were analyzed. Clinical efficacy was evaluated by traditional Chinese medicine (TCM) symptom score, survival quality of karnofsky performance scale (KPS) score, living quality of quality of life (QOL) score, and nausea and vomiting classification. Hemoglobin (HB), platelet (PLT) and serum white blood cells (WBC) levels in the two groups were decreased after treatment. HB, PLT and WBC levels were enhanced in research group versus control group. Moreover, carcino-embryonic antigen (CEA), carbohydrate antigen 199 (CA199) and CA125 levels in both groups were reduced after treatment. Compared with before treatment, the levels of cluster of differentiation (CD)3+, CD4+, CD4+/CD8+ in control group and the research group increased, while the CD8+ content was significantly decreased after treatment. And their content of the research group was significantly higher/lower than that of the control group. TCM symptom score, KPS score, QOL score and nausea and vomiting classification were improved in research group compared to control group. PD-1 inhibitors assisted nursing intervention can improve the living quality of patients with LC after chemotherapy.

The ESMO Tumour-Agnostic Classifier and Screener (ETAC-S): a tool for assessing tumour-agnostic potential of molecularly guided therapies and for steering drug development

BACKGROUND

Advances in precision oncology led to approval of tumour-agnostic molecularly guided treatment options (MGTOs). The minimum requirements for claiming tumour-agnostic potential remain elusive.

METHODS

The European Society for Medical Oncology (ESMO) Precision Medicine Working Group (PMWG) coordinated a project to optimise tumour-agnostic drug development. International experts examined and summarised the publicly available data used for regulatory assessment of the tumour-agnostic indications approved by the US Food and Drug Administration and/or the European Medicines Agency as of December 2023. Different scenarios of minimum objective response rate (ORR), number of tumour types investigated, and number of evaluable patients per tumour type were assessed for developing a screening tool for tumour-agnostic potential. This tool was tested using the tumour-agnostic indications approved during the first half of 2024. A taxonomy for MGTOs and a framework for tumour-agnostic drug development were conceptualised.

RESULTS

Each tumour-agnostic indication had data establishing objective response in at least one out of five patients (ORR ≥ 20%) in two-thirds (≥4) of the investigated tumour types, with at least five evaluable patients in each tumour type. These minimum requirements were met by tested indications and may serve as a screening tool for tumour-agnostic potential, requiring further validation. We propose a conceptual taxonomy classifying MGTOs based on the therapeutic effect obtained by targeting a driver molecular aberration across tumours and its modulation by tumour-specific biology: tumour-agnostic, tumour-modulated, or tumour-restricted. The presence of biology-informed mechanistic rationale, early regulatory advice, and adequate trial design demonstrating signs of biology-driven tumour-agnostic activity, followed by confirmatory evidence, should be the principles for tumour-agnostic drug development.

CONCLUSION

The ESMO Tumour-Agnostic Classifier (ETAC) focuses on the interplay of targeted driver molecular aberration and tumour-specific biology modulating the therapeutic effect of MGTOs. We propose minimum requirements to screen for tumour-agnostic potential (ETAC-S) as part of tumour-agnostic drug development. Definition of ETAC cut-offs is warranted.

Impact of clonal TP53 mutations with loss of heterozygosity on adjuvant chemotherapy and immunotherapy in gastric cancer

BACKGROUND

This study aimed to reveal the effect of TP53 status on clinical outcomes and underlying mechanism in gastric cancer (GC) patients.

METHODS

TP53 status was divided into three groups according to genome sequencing, namely clonal mutations with LOH (C-LOH), clonal diploid or subclonal mutations (CD-SC), and wild type (WT). The p53 protein activity was divided into over-expression (OE), Null and WT according to immunohistochemical staining. Four cohorts, including the TCGA, SMC, ZSHS and FUSCC cohort, were analyzed for association between TP53 mutation status and clinical outcomes and the underlying mechanism.

RESULTS

In TCGA cohort, TP53 CD-SC were associated with superior overall survival compared to TP53 C-LOH cases. GC patients could benefit from ACT only in TP53 CD-SC/ p53 OE and TP53/ p53 WT subgroups, and TP53 C-LOH subgroup demonstrated the worst response to pembrolizumab among three subgroups. Genomic and immunophenotypic deconvolution revealed that TP53 C-LOH, CD-SC and WT differed for genomic and immune-related features.

CONCLUSIONS

TP53 C-LOH GCs with genomic instability and immune evasion phenotype have poor clinical outcomes in patients treated with ACT or immunotherapy.

Predictive Prognostic Model for Hepatocellular Carcinoma Based on Seven Genes Participating in Arachidonic Acid Metabolism

BACKGROUND

The occult onset and rapid progression of hepatocellular carcinoma (HCC) lead to an unsatisfactory overall survival (OS) rate. Established prognostic predictive models based on tumor-node-metastasis staging and predictive factors do not report satisfactory predictive efficacy. Arachidonic acid plays pivotal roles in biological processes including inflammation, regeneration, immune modulation, and tumorigenesis. We, therefore, constructed a prognostic predictive model based on seven genes linked to arachidonic acid metabolism, using samples of HCC patients from databases to analyze the genomic profiles. We also assessed the predictive stability of the constructed model.

METHODS

Sample data of 365 patients diagnosed with HCC were extracted from The Cancer Genome Atlas (TCGA, training set) and HCCDB18, GSE14520, and GSE76427 databases (validation sets). Patient samples were clustered using ConsensusClusterPlus analysis based on the expression levels of 12 genes involved in arachidonic acid metabolism that were significantly associated with HCC prognosis. Differentially expressed genes (DEGs) within different clusters were distinguished and compared using WebGestaltR. Immunohistochemistry (IHC) analysis was performed using a human HCC tissue microarray (TMA). Tumor immune microenvironment assessment was performed using ESTIMATE, ssGSEA, and TIDE.

RESULTS

Samples of patients with HCC were classified into three clusters, with significant differences in OS. Cluster 2 showed the best prognosis, whereas cluster 1 presented the worst. The three clusters showed significant differences in immune infiltration. We then performed Cox and LASSO regression analyses, which revealed CYP2C9, G6PD, CDC20, SPP1, PON1, TRNP1, and ADH4 as prognosis-related hub genes, making it a simplified prognostic model. TMA analysis for the seven target genes showed similar results of regression analyses. The high-risk group showed a significantly worse prognosis and reduced immunotherapy efficacy. Our model showed stable prognostic predictive efficacy.

CONCLUSIONS

This seven-gene-based model showed stable outcomes in predicting HCC prognosis as well as responses to immunotherapy.

The oncogenic functions of SPARCL1 in bladder cancer

Secreted protein, acidic and rich in cysteine-like 1 (SPARCL1) belongs to the SPARC family of matricellular proteins. However, underlying functions of SPARCL1 in bladder cancer (BCa) remain understudied. We performed an integrated search for the expression patterns of SPARCL1 in relation to various clinicopathological features of BCa. We then carried out Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA). Furthermore, we investigated the correlations between SPARCL1 and immunological features, such as tumour mutation burden (TMB), immune activation processes, immune checkpoint expression, tumour immune dysfunction and exclusion (TIDE) scores, and chemotherapeutic sensitivity in BCa. Our analysis revealed that SPARCL1 was downregulated across multiple cancers. In BCa, elevated SPARCL1 was linked with advanced histopathologic stage, higher T and N stage, and poorer prognosis in the clinical cohort. In vitro experiments demonstrated that increased SPARCL1 expression inhibited cell proliferation, migration, and invasion. Additionally, highly expressed SPARCL1 was linked to elevated immune, stromal and ESTIMATE scores, as well as an increase in naive B cells, M2 macrophages, and resting mast cells. We observed a moderate correlation between SPARCL1 expression and CD163, VSIG4 and MS4A4A, which are markers of M2 macrophages. Furthermore, SPARCL1 expression was positively related to TMB, immune activation processes, TIDE scores, immune checkpoint expression, and chemotherapeutic sensitivity in BCa. Our study highlights the potential involvement of SPARCL1 in macrophage recruitment and polarization and suggests its utility as a biomarker for prognosis in BCa.

Pancancer analysis of the interactions between CTNNB1 and infiltrating immune cell populations

Recently, evidence has indicated that CTNNB1 is important in a variety of malignancies. However, how CTNNB1 interacts with immune cell infiltration remains to be further investigated. In this study, we focused on the correlations between CTNNB1 and tumorigenesis, tumor progression, mutation, phosphorylation, and prognosis via gene expression profiling interaction analysis; TIMER 2.0, cBioPortal, GTEx, CPTAC, and GEPIA2 database analyses; and R software. CTNNB1 mutations are most found in uterine endometrioid carcinoma and hepatocellular carcinoma. However, no CTNNB1 mutations were found to be associated with a poor prognosis. In addition, CTNNB1 DNA methylation levels were higher in normal tissues than in tumor tissues in cancer except for breast invasive carcinoma, which had higher methylation levels in tumor tissues. The phosphorylation level of the S675 and S191 sites of CTNNB1 was greater in the primary tumor tissues in the clear cell renal cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma, and breast cancer datasets but not in the glioblastoma multiform dataset. As for, with respect to immune infiltration, CD8 + T-cell infiltration was negatively correlated with the expression of CTNNB1 in thymoma and uterine corpus endometrial carcinoma. The CTNNB1 level was found to be positively associated with the infiltration index of the corresponding fibroblasts in the TCGA tumors of colon adenocarcinoma, human papillomavirus-negative head and neck squamous cell carcinoma, mesothelioma, testicular germ cell tumor, and thymoma. We also identified the top CTNNB1-correlated genes in the TCGA projects and analyzed the expression correlation between CTNNB1 and selected target genes, including PPP4R2, RHOA, and SPRED1. Additionally, pathway enrichment suggested that NUMB is involved in the Wnt pathway. This study highlights the predictive role of CTNNB1 across cancers, suggesting that CTNNB1 might serve as a potential biomarker for the diagnosis and prognosis evaluation of various malignant tumors.

Necroptosis-related lncRNAs: biomarkers for predicting prognosis and immune response in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is one of the most prevalent types of lung cancer (LC), accounting for 50% of all LC cases. Despite therapeutic advancements, patients suffer from adverse drug reactions. Furthermore, the prognosis of LC patients remains poor. Necroptosis is a novel mode of cell death and is critically involved in regulating immunotherapy in patients. However, the correlation between the necroptosis-related long non-coding RNA (lncRNA) (necro-related lnc) signature (NecroLncSig) and the response of patients with LUAD to immunotherapy is unclear. This study developed a model using lncRNAs to predict the prognosis of patients with LUAD.

Methods

We obtained the transcriptomic and clinical data of LUAD patients from The Cancer Genome Atlas (TCGA) database. Next, we conducted a co-expression analysis to identify the necro-related lnc. In addition, we constructed the NecroLncSig using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then we evaluated and validated the NecroLncSig using a Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) curves, principal component analysis (PCA), Gene Ontology (GO) enrichment analysis, a nomogram, and calibration curves. Finally, we used the NecroLncSig to predict the responses of patients to immunotherapy.

Results

We constructed the NecroLncSig based on seven necro-related lnc. The patients were classified into a high-risk group (HRG) and a low-risk group (LRG). The overall survival (OS) of patients in the HRG was significantly poorer in the training, testing, and entire sets (P<0.05) than that of the patients in the LRG. Univariate and multivariate Cox regression analyses demonstrated that the risk score could predict the OS of patients in an independent manner (P<0.001). Time-dependent ROC analysis demonstrated that the area under the curve values of the NecroLncSig for 1-, 2-, and 3-year OS were 0.689, 0.700, and 0.685, respectively, for the entire set. Furthermore, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm showed that the response of patients in the HRG to immunotherapy was better than that of patients in the LRG.

Conclusions

Necro-related lnc can affect disease progression and patient prognosis. In addition, these lncRNAs can be used to design therapeutic strategies, such as immunotherapy, to treat patients with LUAD.

Landscape of targeted therapies for lung squamous cell carcinoma

Lung cancer, a common type of malignant neoplasm, has seen significant advancements in the treatment of lung adenocarcinoma (LUAD). However, the management of lung squamous cell carcinoma (LSCC) continues to pose challenges. Traditional treatment methods for LSCC encompass surgical resection, chemotherapy, and radiotherapy. The introduction of targeted therapy and immunotherapy has greatly benefited LSCC patients, but issues such as limited immune response rates and adverse reactions persist. Therefore, gaining a deeper comprehension of the underlying mechanisms holds immense importance. This review provides an in-depth overview of classical signaling pathways and therapeutic targets, including the PI3K signaling pathway, CDK4/6 pathway, FGFR1 pathway and EGFR pathway. Additionally, we delve into alternative signaling pathways and potential targets that could offer new therapeutic avenues for LSCC. Lastly, we summarize the latest advancements in targeted therapy combined with immune checkpoint blockade (ICB) therapy for LSCC and discuss the prospects and challenges in this field.

Prognostic and therapeutic potential of gene profiles related to tertiary lymphoid structures in colorectal cancer

The role of tertiary lymphoid structures (TLS) in oncology is gaining interest, particularly in colorectal carcinoma, yet a thorough analysis remains elusive. This study pioneered a novel TLS quantification system for prognostic and therapeutic response prediction in colorectal carcinoma, alongside a comprehensive depiction of the TLS landscape. Utilizing single-cell sequencing, we established a TLS score within the Tumor Immune Microenvironment (TIME). Analysis of tertiary lymphoid structure-related genes (TLSRGs) in 1,184 patients with colon adenocarcinoma/rectum adenocarcinoma (COADREAD) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases led to the identification of two distinct molecular subtypes. Differentially expressed genes (DEGs) further segregated these patients into gene subtypes. A TLS score was formulated using gene set variation analysis (GSVA) and its efficacy in predicting immunotherapy outcomes was validated in two independent cohorts. High-scoring patients exhibited a 'hot' immune phenotype, correlating with enhanced immunotherapy efficacy. Key genes in our model, including C5AR1, APOE, CYR1P1, and SPP1, were implicated in COADREAD cell proliferation, invasion, and PD-L1 expression. These insights offer a novel approach to colorectal carcinoma treatment, emphasizing TLS targeting as a potential anti-tumor strategy.

Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors

To date, the public health system has been impacted by the increasing costs of many diagnostic and therapeutic pathways due to limited resources. At the same time, we are constantly seeking to improve these paths through approaches aimed at personalized medicine. To achieve the required levels of diagnostic and therapeutic precision, it is necessary to integrate data from different sources and simulation platforms. Today, artificial intelligence (AI), machine learning (ML), and predictive computer models are more efficient at guiding decisions regarding better therapies and medical procedures. The evolution of these multiparametric and multimodal systems has led to the creation of digital twins (DTs). The goal of our review is to summarize AI applications in discovering new immunotherapies and developing predictive models for more precise immunotherapeutic decision-making. The findings from this literature review highlight that DTs, particularly predictive mathematical models, will be pivotal in advancing healthcare outcomes. Over time, DTs will indeed bring the benefits of diagnostic precision and personalized treatment to a broader spectrum of patients.

Construction of a tumor mutational burden-derived LncRNA prognostic computational framework associated with therapy sensitivity in skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) poses a significant public health challenge due to its aggressive nature and limited treatment options. To address this, the study introduces the Tumor Mutational Burden-Derived Immune lncRNA Prognostic Index (TILPI) as a potential prognostic tool for SKCM.

METHODS

TILPI was developed using a combination of gene set variation analysis, differential expression analysis, and COX regression analysis. Additionally, functional experiments were conducted to validate the findings, focusing on the effects of STARD4-AS1 knockdown on SKCM tumor cell behavior. These experiments encompassed assessments of tumor cell proliferation, gene and protein expression, migration, invasion, and in vivo tumor growth.

RESULTS

The results demonstrated that knockdown of STARD4-AS1 led to a significant reduction in tumor cell proliferation and impaired migration and invasion abilities. Moreover, it resulted in the downregulation of ADCY4, PRKACA, and SOX10 gene expression, as well as decreased protein expression of ADCY4, PRKACA, and SOX10. In vivo experiments further confirmed the efficacy of STARD4-AS1 knockdown in reducing tumor growth.

CONCLUSIONS

This study elucidates the mechanistic role of STARD4-AS1 and its downstream targets in SKCM progression, highlighting the importance of the ADCY4/PRKACA/SOX10 pathway. The integration of computational analysis with experimental validation enhances the understanding of TILPI and its clinical implications. Overall, the findings underscore the potential of novel computational frameworks like TILPI in predicting and managing SKCM, particularly through targeting the ADCY4/PRKACA/SOX10 pathway.

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer

In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher's exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

Unraveling the potential biomarkers of immune checkpoint inhibitors in advanced ovarian cancer: a comprehensive review

The ongoing research on the role of immunotherapy in advanced ovarian cancer (OC) and current clinical trials indicate that patients shown limited response to immune checkpoint inhibitor (ICI) monotherapy. When combined with other treatments or drugs, the efficacy of immunotherapy will be significantly improved. Biomarkers can be used to identify patients with better responses, thereby improving the precision and efficacy of immunotherapy. Key biomarkers for advanced OC include homologous repair deficiency, programmed death-ligand (PD-L) 1 expression, chemokines, and tumor infiltrating lymphocytes. These biomarkers could be applied in the future to select the most suitable patient populations. This review comprehensively examines the research and development of biomarkers in OC immunotherapy from three omics perspectives: genomics, transcriptomics, and proteomics, which may provide guidance for the effectiveness of OC immunotherapy strategies.

Is modulation of immune checkpoints on glioblastoma-infiltrating myeloid cells a viable therapeutic strategy?

The field of immunology has traditionally focused on immune checkpoint modulation of adaptive immune cells. However, many malignancies such as glioblastoma are mostly devoid of T cells and rather are enriched with immunosuppressive myeloid cells of the innate immune system. While some immune checkpoint targets are shared between adaptive and innate immunity, myeloid-specific checkpoints could also serve as potential therapeutics. To better understand the impact of immune checkpoint blockade on myeloid cells, we systematically summarize the current literature focusing on the direct immunological effects of PD-L1/PD-1, CD24/Siglec-10, collagen/LAIR-1, CX3CL1/CX3CR1, and CXCL10/CXCR3. By synthesizing the molecular mechanisms and the translational implications, we aim to prioritize agents in this category of therapeutics for glioblastoma.

Construction and multiple validations of a robust ferroptosis-related prognostic model in bladder cancer: A comprehensive study

Ferroptosis is iron-dependent programmed cell death that inhibits tumor growth, particularly in traditional treatment-resistant tumors. Prognostic models constructed from ferroptosis-related genes are lacking; prognostic biomarkers remain insufficient. We acquired gene expression data and corresponding clinical information for bladder cancer (BC) samples from public databases. Ferroptosis-related genes from the ferroptosis database were screened for clinical predictive value. We validated gene expression differences between tumors and normal tissues through polymerase chain reaction and western blotting. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were conducted to explore signaling pathways affecting the overall survival of patients with BC. CIBERSORT was used to quantify the infiltration of 22 immune cell types. We identified 6 genes (EGFR, FADS1, ISCU, PGRMC1, PTPN6, and TRIM26) to construct the prognostic risk model. The high-risk group had a poorer overall survival than the low-risk group. Receiver operating characteristic curves demonstrated excellent predictive accuracy. The validation cohort and 3 independent datasets confirmed the models' general applicability and stability. BC tissues had elevated FADS1, PTPN6, and TRIM26 mRNA and protein levels and decreased ISCU levels. Enrichment analysis indicated that neurosecretory activity might be the main pathway affecting the overall survival. High- and low-risk groups had significantly different immune cell infiltration. Specific ferroptosis-related gene expression was associated with immune cell infiltration levels. The risk score was significantly correlated with patients' clinical characteristics. A novel, widely applicable risk model with independent predictive value for the prognosis of patients with BC was established; candidate molecules for future BC research were identified.

Comprehensive pan-cancer analysis of FUTs family as prognostic and immunity markers based on multi-omics data

BACKGROUND

The dysregulation of fucosyltransferases (FUTs) contributes to alterations in fucosylated epitope expression, which serve as distinctive features of cancer cells. Nonetheless, a comprehensive elucidation of the prognostic biological marker and therapeutic target of the FUTs family in pan-cancer remains elusive.

METHODS

Over 10,000 individuals' profiling information was examined, including information on 750 small molecule drugs, 33 types of cancer, and 24 types of immune cells. We focused on POFUT2's function and applied GSVA (Gene Set Variation Analysis) to calculate the FUT score. Survival and cancer pathways were found to be correlated with this score. After deriving a signature via univariate Cox and LASSO regression, we generated and analyzed the ROC curve and developed a nomogram.

RESULTS

Our comprehensive analysis revealed epigenetic, genomic, and immunogenomic changes in FUTs, particularly POFUT2, resulting in aberrant expression. Elevated frequencies of CNV (Copy number variation), SNV (Single Nucleotide Variant), and hypermethylation were observed in FUTs. Additionally, the survival of patients with various types of cancers may be predicted by FUT expression. Immune response and prognosis in numerous types of cancer were found to be strongly linked to aberrant POFUT2 expression. Pathway analysis unveiled the role of FUTs in apoptosis, epithelial-to-mesenchymal transition (EMT), cell cycle, DNA damage response, RAS/MAPK, TSC/mTOR, PI3K/AKT, AR, ER, and RTK. A prognostic index for patients diagnosed with adrenocortical carcinoma (ACC) was established by applying a risk model incorporating nine FUTs and based on the findings of the GSVA.

CONCLUSIONS

FUTs, particularly POFUT2, emerge as candidate targets for improving the outcomes of immune therapy. The significance of aberrant MUC12 expression, cancer immune therapy, and patient survival in the context of diverse malignancies is enhanced by the strong correlation observed among these factors. Our five-gene risk signature provides patients with ACC with an independent prognostic indicator, emphasizing the critical function of these genes in inhibiting the immune system's response in ACC.

Pan-cancer analysis and experimental validation of FPR3 as a prognostic and immune infiltration-related biomarker for glioma

Formyl peptide receptor 3 (FPR3) is known to have implications in the progression of various cancer types. Despite this, its biological significance within pan-cancer datasets has yet to be investigated. In this investigation, we scrutinized FPR3's expression profiles, genetic alterations, prognostic significance, immune-related characteristics, methylation status, tumor mutation burden (TMB), and microsatellite instability (MSI) across different types of cancer. We utilized TISCH's single-cell data to identify immune cells closely associated with FPR3. The predictive significance of FPR3 was evaluated independently in gliomas using data from TCGA and CGGA datasets, leading to the development of a prognostic nomogram. Immunohistochemistry and Western blot analysis confirmed FPR3 expression in gliomas. Lastly, the CCK-8 and wound-healing assays were employed to assess the impact of FPR3 on the proliferation and metastasis of GBM cell lines. In numerous cancer types, heightened FPR3 expression correlated with adverse outcomes, immune cell infiltration, immune checkpoints, TMB, and MSI. In glioma, FPR3 emerged as a notable risk factor, with the prognostic model effectively forecasting patient results. The potential biological relevance of FPR3 was confirmed in glioma, and it was shown to have significant involvement in the processes of glioma growth, immune infiltration, and metastasis. Our results imply a potential association of FPR3 with tumor immunity, indicating its viability as a prognostic indicator in glioma.

A disulfidptosis-related lncRNA signature for analyzing tumor microenvironment and clinical prognosis in hepatocellular carcinoma

Introduction

Disulfidptosis is a recently identified form of non-apoptotic programmed cell death which distinguishes itself from classical cell death pathways. However, the prognostic implications of disulfidptosis-related long non-coding RNAs (DRLs) and their underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unexplored.

Methods

In this study, we leveraged RNA-sequencing data and clinical information of HCC patients from the TCGA database. Through expression correlation and prognostic correlation analyses, we identified a set of top-performing long non-coding RNAs. Subsequently, a 5-DRLs predictive signature was established by conducting a Lasso regression analysis.

Results

This signature effectively stratified patients into high- and low-risk groups, revealing notable differences in survival outcomes. Further validation through univariate and multivariate Cox regression analyses confirmed that the risk score derived from our signature independently predicted the prognosis of HCC patients. Moreover, we observed significant disparities in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups, shedding light on the potential connection between immune-related mechanisms and disulfidptosis. Notably, the signature also exhibited predictive value in the context of chemotherapeutic drug sensitivity and immunotherapy efficacy for HCC patients. Finally, we performed experimental validation at both cellular and patient levels and successfully induced a disulfidptosis phenotype in HCC cells.

Discussion

In general, this multifaceted approach provides a comprehensive overview of DRLs profiles in HCC, culminating in the establishment of a novel risk signature that holds promise for predicting prognosis and therapy outcomes of HCC patients.

Predictive value of ZFHX4 mutation for the efficacy of immune checkpoint inhibitors in non-small cell lung cancer and melanoma

Studies have shown that the Zinc finger homeobox 4 (ZFHX4) might be a factor in the prognosis of malignancies. However, little is known about the association between the ZFHX4 mutation and the effectiveness of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) and melanoma. Three public ICIs-treated NSCLC cohorts were divided into discovery cohort (n=75) and validation cohort (n=62), which were used to evaluate the relationship between ZFHX4 mutation and ICIs effectiveness in NSCLC. Seven ICIs-treated melanoma cohorts (n = 418) were used to analyze the relationship between ZFHX4 mutation and immunotherapy efficacy in melanoma. NSCLC and skin cutaneous melanoma (SKCM) cohorts from The Cancer Genome Atlas (TCGA) were used to investigate underlying mechanism. Patients with ZFHX4 mutant-type (ZFHX4-Mut) showed a superior objective response rate (ORR) (P < 0.01) and longer progression-free survival (PFS) (P < 0.05) than patients with ZFHX4 wild-type (ZFHX4-WT) in NSCLC cohorts. In the melanoma cohorts, patients carrying ZFHX4-Mut had a higher ORR (P = 0.042) and longer overall survival (OS) (P = 0.011). Besides, patients with NSCLC and melanoma harboring ZFHX4-Mut had a higher tumor mutation burden (TMB) (P<0.001) and tumor neoantigen burden (TNB) (P<0.001) than those harboring ZFHX4-WT. ZFHX4 mutation was associated with higher levels of plasma B cells, activated CD4+ memory T cells, and CD8+ T cells. Seven DNA damage repair pathways were significantly enriched in the ZFHX4-Mut group. ZFHX4 mutation could serve as a predicter for the efficacy of ICIs therapy in NSCLC and melanoma.

Lymphocyte activation gene 3 served as a potential prognostic and immunological biomarker across various cancer types: a clinical and pan-cancer analysis

Objectives

Lymphocyte activation gene 3 (LAG3), an inhibitory receptor in T-cell activation, is a negative prognostic factor. However, its impact on tumours has yet to be comprehensively elucidated on a pan-cancer scale. Thus, we aim to reveal its role at the pan-cancer level.

Methods

We performed IHC staining on a retrospective cohort of 370 patients. Then we assessed the prognostic effect of LAG3 using Kaplan-Meier survival analysis and multivariate Cox regression analysis. In pan-cancer analysis, we constructed competing endogenous RNA and protein-protein interaction networks, conducted gene set enrichment analysis and identified correlations between LAG3 gene expression and various factors, including clinical characteristics, tumour purity, mutations, tumour immunity and drug sensitivity across 33 cancer types.

Results

LAG3 was expressed higher in normal kidney tissues than in tumours. A high level of LAG3 gene expression was an independent prognostic factor for OS (HR = 6.60, 95% CI = 2.43-17.90, P < 0.001) and PFS (HR = 3.44, 95% CI = 1.68-7.10, P < 0.001). In pan-cancer analysis, LAG3 exhibited robust correlations with survival and tumour stages in various cancers. Moreover, LAG3 was strongly associated with immune-related genes, proteins and signalling pathways. LAG3 gene expression was positively associated with increased infiltration of activated immune cells and decreased infiltration of several resting cells. LAG3 gene expression was associated with tumour mutation burden and microsatellite instability in multiple cancers.

Conclusion

High LAG3 gene expression was an independent risk factor in kidney neoplasms. It also functioned as a biomarker for prognosis, TIME and immunotherapy efficacy in the pan-cancer dimension.

Prognostic value of a lactate metabolism gene signature in lung adenocarcinoma and its associations with immune checkpoint blockade therapy response

Lung adenocarcinoma (LUAD) is a study that examines the prognostic value of lactate metabolism genes in tumor cells, which are associated with clinical prognosis. We analyzed the expression and clinical data for LUAD from The Cancer Genome Atlas database, using the GSE68465 dataset from the Gene Expression Omnibus and the MSigDB database. LASSO Cox regression and stepwise Cox regression were used to identify the optimal lactate metabolism gene signature. Differences in immune infiltration, tumor mutation burden (TMB), and response to immune checkpoint blockade (ICB) therapy were evaluated between groups. LASSO and Cox regression analyses showed an eight-lactate metabolism gene signature for model construction in both TCGA cohort and GSE68465 data, with higher survival outcomes in high-risk groups. The lactate metabolism risk score had an independent prognostic value (hazard ratio: 2.279 [1.652-3.146], P < .001). Immune cell infiltration differed between the risk groups, such as CD8+ T cells, macrophages, dendritic cells, mast cells, and neutrophils. The high-risk group had higher tumor purity, lower immune and stromal scores, and higher TMB. High-risk samples had high tumor immune dysfunction and exclusion (TIDE) scores and low cytolytic activity (CYT) scores, indicating a poor response to ICB therapy. Similarly, most immune checkpoint molecules, immune inhibitors/stimulators, and major histocompatibility complex (MHC) molecules were highly expressed in the high-risk group. The 8-lactate metabolism gene-based prognostic model predicts patient survival, immune infiltration, and ICB response in patients with LUAD, driving the development of therapeutic strategies to target lactate metabolism.

Advances of immune-checkpoint inhibition of CTLA-4 in pancreatic cancer

Targeting checkpoints for immune cell activation has been acknowledged known as one of the most effective way to activate anti-tumor immune responses. Among them, drugs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) are approved for clinical treatment though several more are in advanced stages of development, which demonstrated durable response rates and manageable safety profile. However, its therapy efficacy is unsatisfactory in pancreatic cancer (PC), which can be limited by the overall condition of patients, the pathological type of PC, the expression level of tumor related genes, etc. To improve clinical efficiency, various researches have been conducted, and the efficacy of combination therapy showed significantly improvement compared to monotherapy. This review analyzed current strategies based on anti-CTLA-4 combination immunotherapy, providing totally new idea for future research.

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Mutational Landscape of Gastric Adenocarcinoma of the Fundic Gland Type Revealed by Whole Genome Sequencing

BACKGROUND

Gastric adenocarcinoma of the fundic gland type (GA-FG) is a newly described variant of gastric adenocarcinoma with lack of knowledges regarding its genetic features.

METHODS

We performed whole-genome sequencing (WGS) in formalin-fixed paraffin-embedded (FFPE) tumor tissues and matched adjacent noncancerous specimens from 21 patients with GA-FG, and integrated published datasets from 1105 patients with traditional gastric adenocarcinoma with the purpose of dissecting genetic determinants both common to conventional gastric adenocarcinoma and unique to GA-FG disease.

RESULTS

We characterized the genomic architecture of GA-FG disease, revealing the predominant proportion of C > T substitution among the six types of SNVs. GNAS was the most significantly mutated driver gene (14.29%). 42.8% of samples harbored "Kataegis." Distinct genomic alterations between GA-FG and conventional gastric cancer were identified. Specifically, low mutational burden and relatively moderate mutational frequencies of significantly mutated driver genes, coupled with the absence of non-silent alterations of formerly well-known drivers such as TP53, PIK3CA and KRAS were identified in GA-FG patients. Oncogenic signaling pathway analysis revealed mutational processes associated with focal adhesions and proteoglycans in cancer, highlighting both common and specific procedures during the development of GA-FG and conventional gastric cancer.

CONCLUSION

Our study is the first to comprehensively depict the genomic landscape highlighting the multidimensional perturbations in GA-FG patients. These discoveries offered mechanistic insights for novel diagnostic and therapeutic strategies for patients with such disease.

Translocation in bone and soft tissue sarcomas: a comprehensive epidemiological investigation

BACKGROUND

Limited epidemiological research has focused on translocations in soft tissue sarcomas, with no studies on bone sarcomas. This study aimed to clarify the epidemiology, prognosis, and genetic information of translocation-related sarcoma (TRS) and non-TRS patients.

MATERIALS AND METHODS

This retrospective cohort study used data from the Bone and Soft Tissue Tumor Registry in Japan (BSTTRJ) (2001-2019), the Kyushu University Hospital (KUH) repository (2001-2021), and a publicly available online dataset (MSK). The patients were categorized into TRS and non-TRS groups, and epidemiological, prognostic, and mutational diversity were compared.

RESULTS

This study included 25 383 participants, of whom 4864 (19.2%) were TRS and 20 519 (80.8%) were non-TRS patients. TRS patients had significantly younger onset ages (median: 43 years, interquartile range: 29-59 years) than non-TRS patients (median: 63 years, interquartile range: 46-73 years). In the MSK cohort, microsatellite instability and tumor mutation burden scores in non-TRS were higher than in TRS, although they were rather low compared with the pan-cancer analysis. In the BSTTRJ cohort, survival analyses with the propensity score matching revealed that patients with TRS had better overall [hazard ratio (HR): 0.71, 95% confidence interval (CI) 0.63-0.81], metastasis-free (HR: 0.75, 95% CI 0.67-0.84), and recurrence-free (HR: 0.47, 95% CI 0.39-0.57) survival.

CONCLUSIONS

This study highlights differences in the epidemiology and genetic rearrangements of sarcoma.

Tumour mutational burden: clinical utility, challenges and emerging improvements

Tumour mutational burden (TMB), defined as the total number of somatic non-synonymous mutations present within the cancer genome, varies across and within cancer types. A first wave of retrospective and prospective research identified TMB as a predictive biomarker of response to immune-checkpoint inhibitors and culminated in the disease-agnostic approval of pembrolizumab for patients with TMB-high tumours based on data from the Keynote-158 trial. Although the applicability of outcomes from this trial to all cancer types and the optimal thresholds for TMB are yet to be ascertained, research into TMB is advancing along three principal avenues: enhancement of TMB assessments through rigorous quality control measures within the laboratory process, including the mitigation of confounding factors such as limited panel scope and low tumour purity; refinement of the traditional TMB framework through the incorporation of innovative concepts such as clonal, persistent or HLA-corrected TMB, tumour neoantigen load and mutational signatures; and integration of TMB with established and emerging biomarkers such as PD-L1 expression, microsatellite instability, immune gene expression profiles and the tumour immune contexture. Given its pivotal functions in both the pathogenesis of cancer and the ability of the immune system to recognize tumours, a profound comprehension of the foundational principles and the continued evolution of TMB are of paramount relevance for the field of oncology.

Five latent factors underlie response to immunotherapy

Only a subset of patients treated with immune checkpoint inhibitors (CPIs) respond to the treatment, and distinguishing responders from non-responders is a major challenge. Many proposed biomarkers of CPI response and survival probably represent alternative measurements of the same aspects of the tumor, its microenvironment or the host. Thus, we currently ignore how many truly independent biomarkers there are. With an unbiased analysis of genomics, transcriptomics and clinical data of a cohort of patients with metastatic tumors (n = 479), we discovered five orthogonal latent factors: tumor mutation burden, T cell effective infiltration, transforming growth factor-beta activity in the microenvironment, prior treatment and tumor proliferative potential. Their association with CPI response and survival was observed across all tumor types and validated across six independent cohorts (n = 1,491). These five latent factors constitute a frame of reference to organize current and future knowledge on biomarkers of CPI response and survival.

Exploring the molecular landscape of cancer of unknown primary: A comparative analysis with other metastatic cancers

Cancer of unknown primary (CUP) tumors are biologically very heterogeneous, which complicates stratification of patients for treatment. Consequently, these patients face limited treatment options and a poor prognosis. With this study, we aim to expand on the current knowledge of CUP biology by analyzing two cohorts: a well-characterized cohort of 44 CUP patients, and 213 metastatic patients with known primary. These cohorts were treated at the same institution and characterized by identical molecular assessments. Through comparative analysis of genomic and transcriptomic data, we found that CUP tumors were characterized by high expression of immune-related genes and pathways compared to other metastatic tumors. Moreover, CUP tumors uniformly demonstrated high levels of tumor-infiltrating leukocytes and circulating T cells, indicating a strong immune response. Finally, the genetic landscape of CUP tumors resembled that of other metastatic cancers and demonstrated mutations in established cancer genes. In conclusion, CUP tumors possess a distinct immunophenotype that distinguishes them from other metastatic cancers. These results may suggest an immune response in CUP that facilitates metastatic tumor growth while limiting growth of the primary tumor.

Establishment and validation of a cuproptosis-related lncRNA signature that predicts prognosis and potential targeted therapy in hepatocellular carcinoma

BACKGROUND

Cuproptosis is a recently identified form of programmed cell death and could be a new direction for tumour therapy, and it has important clinical implications. Long non-coding RNAs (lncRNAs) can intervene in diverse biological processes and have a decisive role in hepatocellular carcinoma (HCC). However, how cuproptosis-related lncRNAs (CRLs) participate in regulating HCC has yet to be recognised. This study aimed to establish and validate a prognostic signature of CRLs and to analyse their clinical value in HCC patients.

METHODS

To analyse the function of CRLs in the prognosis of HCC, RNA sequencing data, mutation data, and clinically relevant data were collected from the Cancer Genome Atlas Database (TCGA). Then, TCGA cohort was randomly divided into training and test sets. The training set was utilized to define prognostic signature of CRLs using bioinformatics methods. Subsequently, we verified the accuracy of this prognostic signature in the test set. Finally, we performed immune-related analysis, the half-maximal inhibitory concentration (IC50) prediction, gene set enrichment analysis, and tumour mutational burden (TMB) analysis.

RESULTS

We established a prognostic signature for the CRLs (SNHG4, AC026412.3, AL590705.3, and CDKN2A-DT). This signature-based risk group displayed an accurate predictive ability for the survival time of patients with HCC. We observed discrepancies in immune cells, immune function, the expression level of genes related to immune checkpoints, and TMB in high- and low-risk groups.

CONCLUSION

This CRLs prognostic signature could predict clinical outcomes in patients with HCC as well as the efficacy of targeted and therapy immunotherapy.

EPDR1 promotes PD-L1 expression and tumor immune evasion by inhibiting TRIM21-dependent ubiquitylation of IkappaB kinase-β

While immune checkpoint blockade (ICB) has shown promise for clinical cancer therapy, its efficacy has only been observed in a limited subset of patients and the underlying mechanisms regulating innate and acquired resistance to ICB of tumor cells remain poorly understood. Here, we identified ependymin-related protein 1 (EPDR1) as an important tumor-intrinsic regulator of PD-L1 expression and tumor immune evasion. Aberrant expression of EPDR1 in hepatocellular carcinoma is associated with immunosuppression. Mechanistically, EPDR1 binds to E3 ligase TRIM21 and disrupts its interaction with IkappaB kinase-b, suppressing its ubiquitylation and autophagosomal degradation and enhancing NF-κB-mediated transcriptional activation of PD-L1. Further, we validated through a mouse liver cancer model that EPDR1 mediates exhaustion of CD8+ T cells and promotes tumor progression. In addition, we observed a positive correlation between EPDR1 and PD-L1 expression in both human and mouse liver cancer samples. Collectively, our study reveals a previously unappreciated role of EPDR1 in orchestrating tumor immune evasion and cancer progression.

Advances in Immune Checkpoint Therapy in Hepatocellular Carcinoma

The incidence and lethality of hepatocellular carcinoma (HCC) are increasing annually, and traditional treatments have been proven to be ineffective for patients with advanced stages of the disease. In recent years, immune checkpoint therapy has rapidly evolved, demonstrating promising results across a wide range of cancers and offering new hope for cancer treatment. However, the efficacy of immune checkpoint therapy in HCC varies greatly among individuals, with only a small proportion of HCC patients responding positively. A major cause of immune resistance and poor efficacy in HCC patients is immune evasion, which is often due to insufficient infiltration of immune cells. Understanding the mechanisms underlying immune evasion is crucial for enhancing the efficacy of immune therapies. In this review, we aim to summarize the mechanisms of immune evasion observed during immune checkpoint therapy and discuss future directions for this therapeutic approach. Our goal is to provide insights that could help overcome immune evasion, thereby improving the efficacy of immune therapies and extending patient survival time.

Research progress and challenges of the PD-1/PD-L1 axis in gliomas

The emergence of programmed death-1 (PD-1) and programmed death ligand 1 (PD-L1) immunosuppressants provides new therapeutic directions for various advanced malignant cancers. At present, PD-1/PD-L1 immunosuppressants have made significant progress in clinical trials of some gliomas, but PD-1/PD-L1 inhibitors have not yet shown convincing clinical efficacy in gliomas. This article summarizes the research progress of the PD-1 /PD-L1 pathway in gliomas through the following three aspects. It mainly includes the complex expression levels and regulatory mechanisms of PD-1/PD-L1 in the glioma microenvironment, the immune infiltration in glioma immunosuppressive microenvironment, and research progress on the application of PD-1/PD-L1 immunosuppressants in clinical treatment trials for gliomas. This will help to understand the current treatment progress and future research directions better.

The Comparative Oncology of Canine Malignant Melanoma in Targeted Therapy: A Systematic Review of In Vitro Experiments and Animal Model Reports

Canine malignant melanoma (CMM) is highly aggressive and mostly located in the oral cavity. CMM is the predominant type of canine oral malignancy and shows striking homologies with human mucosal melanoma. In comparative oncology, canine oral melanomas (COMs), as spontaneous tumor models, have the potential to acquire a unique value as a translational model of rare human melanoma subtypes. This review aims to provide a comprehensive summary of targeted therapies for canine malignant melanoma and to enrich the field of comparative oncology. Following the PRISMA guidelines, a comprehensive literature search was conducted across databases for studies from 1976 to April 2024. Studies were selected based on their relevance to targeted treatments. A total of 30 studies met the inclusion criteria. Based on the treatment approaches, the studies were further categorized into immunotherapies, small molecule signaling inhibitors, indirect kinase inhibitors, and other alternative strategies. Some treatments have been shown to result in stable disease or partial response, accounting for 29% (monoclonal antibody) and 76.5% (micro-RNA therapies) in clinical trials. Moreover, in vitro experiments of small molecule inhibitors, including cell signaling inhibitors and indirect kinase inhibitors, have shown the potential to be an effective treatment option for the development of therapeutic strategies in canine malignant melanoma. The observed response in in vitro experiments of CMM (particularly the oral and certain cutaneous subtypes) to drugs used in the treatment of human melanoma underlines the resemblance to human melanoma, therefore supporting the notion that CMM may be a valuable model for understanding rare human melanoma subtypes and exploring potential therapeutic avenues in preclinical trials. Finally, this literature review serves as a valuable resource for the development of therapeutic strategies for CMM and highlights the potential for translating these findings to human cancer treatment.

An integrative pan-cancer analysis of MASP1 and the potential clinical implications for the tumor immune microenvironment

Mannose-binding lectin-associated serine protease 1 (MASP1) plays a crucial role in the complement lectin pathway and the mediation of immune responses. However, comprehensive research on MASP1 across various cancer types has not been performed to date. This study aimed to evaluate the significance of MASP1 in pan-cancer. The Cancer Genome Atlas (TCGA), UCSC Xena and Genotype Tissue Expression (GTEx) databases were used to evaluate the expression profiles, genomic features, prognostic relevance, and immune microenvironment associations of MASP1 across 33 cancer types. We observed significant dysregulation of MASP1 expression in multiple cancers, with strong associations between MASP1 expression levels and diagnostic value as well as patient prognosis. Mechanistic insights revealed significant correlations between MASP1 levels and various immunological and genomic factors, including tumor-infiltrating immune cells (TIICs), immune-related genes, mismatch repair (MMR), tumor mutation burden (TMB), and microsatellite instability (MSI), highlighting a critical regulatory function of MASP1 within the tumor immune microenvironment (TIME). In vitro and in vivo experiments demonstrated that MASP1 expression was markedly decreased in liver hepatocellular carcinoma (LIHC). Moreover, the overexpression of MASP1 in hepatocellular carcinoma (HCC) cell lines significantly inhibited their proliferation, invasion and migration. In conclusion, MASP1 exhibits differential expression in the pan-cancer analyses and might play an important role in TIME. MASP1 is a promising prognostic biomarker and a potential target for immunological research, particularly in LIHC.

Development and validation of a mitotic catastrophe-related genes prognostic model for breast cancer

Background

Breast cancer has become the most common malignant tumor in women worldwide. Mitotic catastrophe (MC) is a way of cell death that plays an important role in the development of tumors. However, the exact relationship between MC-related genes (MCRGs) and the development of breast cancer is still unclear, and further research is needed to elucidate this complexity.

Methods

Transcriptome data and clinical data of breast cancer were downloaded from the Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. We identified differential expression of MCRGs by comparing tumor tissue with normal tissue. Subsequently, we used COX regression analysis and LASSO regression analysis to construct the prognosis risk model of MCRGs. Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used to evaluate the predictive ability of prognostic model. Moreover, the clinical relevance, gene set enrichment analysis (GSEA), immune landscape, tumor mutation burden (TMB), and immunotherapy and drug sensitivity analysis between high-risk and low-risk groups were systematically investigated. Finally, we validated the expression levels of genes involved in constructing the prognostic model through real-time quantitative polymerase chain reaction (RT-qPCR) at the cellular and tissue levels.

Results

We identified 12 prognostic associated MCRGs, four of which were selected to construct prognostic model. The Kaplan-Meier analysis suggested that patients in the high-risk group had a shorter overall survival (OS). The Cox regression analysis and ROC analysis indicated that risk model had independent and excellent ability in predicting prognosis of breast cancer patients. Mechanistically, a remarkable difference was observed in clinical relevance, GSEA, immune landscape, TMB, immunotherapy response, and drug sensitivity analysis. RT-qPCR results showed that genes involved in constructing the prognostic model showed significant abnormal expressions and the expression change trends were consistent with the bioinformatics results.

Conclusions

We established a prognosis risk model based on four MCRGs that had the ability to predict clinical prognosis and immune landscape, proposing potential therapeutic targets for breast cancer.

Biomarkers for Immunotherapy Efficacy in Advanced Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC), the most common primary liver malignancy and the sixth most common cancer globally, remains fatal for many patients with inappropriate responses to treatment. Recent advancements in immunotherapy have transformed the treatment landscape for advanced HCC. However, variability in patient responses to immunotherapy highlights the need for biomarkers that can predict treatment outcomes. This manuscript comprehensively reviews the evolving role of biomarkers in immunotherapy efficacy, spanning from blood-derived indicators-alpha-fetoprotein, inflammatory markers, cytokines, circulating tumor cells, and their DNA-to tissue-derived indicators-programmed cell death ligand 1 expression, tumor mutational burden, microsatellite instability, and tumor-infiltrating lymphocytes. The current body of evidence suggests that these biomarkers hold promise for improving patient selection and predicting immunotherapy outcomes. Each biomarker offers unique insights into disease biology and the immune landscape of HCC, potentially enhancing the precision of treatment strategies. However, challenges such as methodological variability, high costs, inconsistent findings, and the need for large-scale validation in well-powered two-arm trial studies persist, making them currently unsuitable for integration into standard care. Addressing these challenges through standardized techniques and implementation of further studies will be critical for the future incorporation of these biomarkers into clinical practice for advanced HCC.

Integrated profile of tumor stage and mutational burden predicts disparate clinical responses to immune checkpoint inhibitors: A risk-benefit study

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, having demonstrated efficacy and leading to regulatory approvals of ICIs in cancers characterized by high tumor mutation burden (TMB). However, there remains a gap in determining their applicability and risk-benefit profile, across the broad spectrum of patients whose tumors harbor varying TMB levels across distinct tumor stages. By interrogating a large contemporary cohort comprised of 10,233 patients with a diagnosis of cancer across all tumor stages and TMB levels, this study revealed significantly improved overall survival (OS) following ICI therapy (P < .0001) in patients with a combination of ≥10 mut/Mb and stage IV disease. In contrast, ICI therapy is associated with markedly worse OS in patients with low TMB levels <10 mut/Mb and stages I, II, and III cancer. These findings highlight the critical interplay between TMB, tumor stage, and ICI treatment outcomes, underscoring the importance of integrating clinical and genetic characteristics in weighing the risk-benefit balance of ICI therapy. Although maximizing therapeutic benefits is crucial, it is equally important to identify and manage potential risks that may not be immediately apparent. This may require enrolling patients with less-severe or early-stage disease to enable long-term follow-up with effective clinical surveillance. By comprehensively evaluating the added benefit of improved treatment efficacy and the potential risk of adverse treatment outcome, a risk-benefit profile can optimize immunotherapy regimens, with profound implications for clinical decision-making and regulatory approvals of ICI.

Disulfidptosis and ferroptosis related genes define the immune microenvironment and NUBPL serves as a potential biomarker for predicting prognosis and immunotherapy response in bladder cancer

Background

Ferroptosis and disulfidptosis are regulatory forms of cell death that play an important role in tumorigenesis and progression. However, few biomarkers about disulfidptosis and ferroptosis related genes (DFRGs) have been developed to predict the prognosis of bladder cancer (BC).

Methods

We conducted a bioinformatics analysis using public BC datasets to examine the prognostic significance of differentially expressed DFRGs. A Lasso regression was employed to create a prognostic prediction model from these DFRGs. Hub DFRGs that play a role in immunotherapy response and immunoregulation were pinpointed. Immunohistochemistry (IHC) experiment was performed to assess NUBPL and c-MYC expression in BC patients who underwent surgery or received immune checkpoint inhibitor (ICI) immunotherapy at Peking University Cancer Hospital.

Results

We constructed a valid model to predict the prognosis of BC based on DFRGs and performed relevant validation, the results demonstrated that the model was an independent prognostic factor for BC. Further analysis indicated that the model score, combined with the expression of various immune factors and tumor mutation burden (TMB), could predict the prognosis for BC. In addition, we also found that NUBPL was strongly associated with prognosis and response to ICI treatment, and NUBPL may influence BC malignant progression through the c-MYC pathway.

Conclusions

Our research findings highlight the satisfactory predictive value of DFRGs in the immune microenvironment and suggest that NUBPL may be a highly promising biomarker for predicting the prognosis and efficacy of ICI treatment in BC patients.

Construction and validation of a TAMRGs prognostic signature for gliomas by integrated analysis of scRNA and bulk RNA sequencing data

BACKGROUND

This study aimed to construct and validate a prognostic model based on tumor associated macrophage-related genes (TAMRGs) by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data.

METHODS

The scRNA-seq data of three inhouse glioma tissues were used to identify the tumor-associated macrophages (TAMs) marker genes, the DEGs from the The Cancer Genome Atlas (TCGA) - Genotype-Tissue Expression (GTEx) dataset were used to further select TAMs marker genes. Subsequently, a TAMRG-score was constructed by Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis in the TCGA dataset and validated in the Chinese Glioma Genome Atlas (CGGA) dataset.

RESULTS

We identified 186 TAMs marker genes, and a total of 6 optimal prognostic genes including CKS2, LITAF, CTSB, TWISTNB, PPIF and G0S2 were selected to construct a TAMRG-score. The high TAMRG-score was significantly associated with worse prognosis (log-rank test, P<0.001). Moreover, the TAMRG-score outperformed the other three models with AUC of 0.808. Immune cell infiltration, TME scores, immune checkpoints, TMB and drug susceptibility were significantly different between TAMRG-score groups. In addition, a nomogram were constructed by combing the TAMRG-score and clinical information (Age, Grade, IDH mutation and 1p19q codeletion) to predict the survival of glioma patients with AUC of 0.909 for 1-year survival.

CONCLUSION

The high TAMRG-score group was associated with a poor prognosis. A nomogram by incorporating TMARG-score could precisely predict glioma survival, and provide evidence for personalized treatment of glioma.

Epigenetic regulation of cell state by H2AFY governs immunogenicity in high-risk neuroblastoma

Childhood neuroblastoma with MYCN amplification is classified as high risk and often relapses after intensive treatments. Immune checkpoint blockade therapy against the PD-1/L1 axis shows limited efficacy in patients with neuroblastoma, and the cancer intrinsic immune regulatory network is poorly understood. Here, we leverage genome-wide CRISPR/Cas9 screens and identify H2AFY as a resistance gene to the clinically approved PD-1 blocking antibody nivolumab. Analysis of single-cell RNA-Seq datasets reveals that H2AFY mRNA is enriched in adrenergic cancer cells and is associated with worse patient survival. Genetic deletion of H2afy in MYCN-driven neuroblastoma cells reverts in vivo resistance to PD-1 blockade by eliciting activation of the adaptive and innate immunity. Mapping of the epigenetic and translational landscape demonstrates that H2afy deletion promotes cell transition to a mesenchymal-like state. With a multiomics approach, we uncovered H2AFY-associated genes that are functionally relevant and prognostic in patients. Altogether, our study elucidates the role of H2AFY as an epigenetic gatekeeper for cell states and immunogenicity in high-risk neuroblastoma.

Comprehensive analysis of LD-related genes signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

BACKGROUND

Lipid droplets (LD) in renal clear cell carcinoma (ccRCC)play a crucial role in lipid metabolism and immune response modulation. The purpose of this study was to create a LD-related signature to predict prognosis and guide the immunotherapy and targeted therapy in ccRCC patients.

METHODS

We conducted a comprehensive analysis using transcriptional profiles and clinical data obtained from The Cancer Genome Atlas (TCGA). LD-related genes were identified from existing literature and the GeneCards database, and differentially expressed genes were determined. Sequentially, we conducted Cox regression analysis and Lasso regression analysis, to establish a prognostic risk model. The performance of the risk model was evaluated using Kaplan-Meier (KM) analysis and time-dependent receiver operating characteristic (ROC) analysis. Additionally, gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and immunophenoscore (IPS) algorithm were used to assess the tumor microenvironment (TME) and treatment response.

RESULTS

We constructed a risk signature with four LD-related genes in the TCGA dataset, which could be an independent prognostic factor in ccRCC patients. Then, patients were classified into two risk groups and exhibited notable differences in overall survival (OS), progression-free survival (PFS), and TME characteristics. Furthermore, we developed a comprehensive nomogram based on clinical features, which demonstrated good prognostic predictive value. According to the results of GSEA analysis, immune-related pathways were found to be significantly enriched in the high-risk group. Additionally, the high-risk group displayed high levels of immune cell infiltration, TMB and IPS scores, indicating better efficacy of immune checkpoint inhibitors (ICIs). Finally, high-risk demonstrated reduced IC50 values compared to the low-risk counterpart for specific targeted and chemotherapeutic drugs, suggesting that the patients receiving these targeted drugs in high-risk group had better treatment outcomes.

CONCLUSIONS

Our findings suggested that the LD-related gene signature could potentially predict the prognosis of ccRCC patients. Additionally, it showed promise for predicting responses to immunotherapy and targeted therapy in ccRCC patients. These insights might potentially have guided the clinical management of these patients, but further validation and broader data analysis are needed to confirm these preliminary observations.