IL-7: A promising adjuvant ensuring effective T cell responses and memory in combination with cancer vaccines?

Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study

Background: Hepatocellular carcinoma (HCC), the predominant malignancy of the digestive tract, ranks as the third most common cause of cancer-related mortality globally, significantly impeding human health and lifespan. Emerging immunotherapeutic approaches have ignited fresh optimism for patient outcomes. This investigation probes the link between 731 immune cell phenotypes and HCC through Mendelian Randomization and single-cell sequencing, aiming to unearth viable drug targets and dissect HCC's etiology. Methods: We conducted an exhaustive two-sample Mendelian Randomization analysis to ascertain the causal links between immune cell features and HCC, utilizing publicly accessible genetic datasets to explore the causal connections of 731 immune cell traits with HCC susceptibility. The integrity, diversity, and potential horizontal pleiotropy of these findings were rigorously assessed through extensive sensitivity analyses. Furthermore, single-cell sequencing was employed to penetrate the pathogenic underpinnings of HCC. Results: Establishing a significance threshold of pval_Inverse.variance.weighted at 0.05, our study pinpointed five immune characteristics potentially elevating HCC risk: B cell % CD3- lymphocyte (TBNK panel), CD25 on IgD+ (B cell panel), HVEM on TD CD4+ (Maturation stages of T cell panel), CD14 on CD14+ CD16- monocyte (Monocyte panel), CD4 on CD39+ activated Treg ( Treg panel). Conversely, various cellular phenotypes tied to BAFF-R expression emerged as protective elements. Single-cell sequencing unveiled profound immune cell phenotype interactions, highlighting marked disparities in cell communication and metabolic activities. Conclusion: Leveraging MR and scRNA-seq techniques, our study elucidates potential associations between 731 immune cell phenotypes and HCC, offering a window into the molecular interplays among cellular phenotypes, and addressing the limitations of mono-antibody therapeutic targets.

Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

Experimentally validated oxidative stress -associated prognostic signatures describe the immune landscape and predict the drug response and prognosis of SKCM

Background

Skin Cutaneous Melanoma (SKCM) incidence is continually increasing, with chemotherapy and immunotherapy being among the most common cancer treatment modalities. This study aims to identify novel biomarkers for chemotherapy and immunotherapy response in SKCM and explore their association with oxidative stress.

Methods

Utilizing TCGA-SKCM RNA-seq data, we employed Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) networks to identify six core genes. Gene co-expression analysis and immune-related analysis were conducted, and specific markers associated with oxidative stress were identified using Gene Set Variation Analysis (GSVA). Single-cell analysis revealed the expression patterns of Oxidative Stress-Associated Genes (OSAG) in the tumor microenvironment. TIDE analysis was employed to explore the association between immune therapy response and OSAG, while CIBERSORT was used to analyze the tumor immune microenvironment. The BEST database demonstrated the impact of the Oxidative Stress signaling pathway on chemotherapy drug resistance. Immunohistochemical staining and ROC curve evaluation were performed to assess the protein expression levels of core genes in SKCM and normal samples, with survival analysis utilized to determine their diagnostic value.

Results

We identified six central genes associated with SKCM metastasis, among which the expression of DSC2 and DSC3 involved in the oxidative stress pathway was closely related to immune cell infiltration. DSC2 influenced drug resistance in SKMC patients. Furthermore, downregulation of DSC2 and DSC3 expression enhanced the response of SKCM patients to immunotherapy.

Conclusion

This study identified two Oxidative Stress-Associated genes as novel biomarkers for SKCM. Additionally, targeting the oxidative stress pathway may serve as a new strategy in clinical practice to enhance SKCM chemotherapy and sensitivity.

Association of the Advanced Lung Cancer Inflammation Index (ALI) and Gustave Roussy Immune (GRIm) score with immune checkpoint inhibitor efficacy in patients with gastrointestinal and lung cancer

OBJECTIVE

This study aimed to conduct a comprehensive analysis, evaluating the prognostic significance of the baseline Advanced Lung Cancer Inflammation Index (ALI) and Gustave Roussy Immune (GRIm) Score in patients undergoing immune checkpoint inhibitor (ICI) therapy.

METHODS

A comprehensive search was performed across various databases, including PubMed, the Cochrane Library, EMBASE, and Google Scholar, until October 21, 2023, to compile relevant articles for analysis. The investigation encompassed diverse clinical outcomes, including overall survival (OS) and progression-free survival (PFS).

RESULTS

This analysis included a total of 15 articles, comprising 19 studies involving 3335 patients. Among the 19 studies, nine studies focused on NSCLC, and six studies were conducted on HCC. Pooled results revealed that patients with elevated ALI levels experienced prolonged OS (HR: 0.51, 95% CI: 0.37-0.70, p < 0.001) and extended PFS (HR: 0.61, 95% CI: 0.52-0.72, p < 0.001). Furthermore, a GRIm score > 1 was associated with reduced OS (HR: 2.07, 95% CI: 1.47-2.92, p < 0.001) and diminished PFS (HR: 1.78, 95% CI: 1.35-2.34, p < 0.001) in cancer patients receiving ICIs. Subgroup analysis indicated that ALI cutoff values of 18 exhibited enhanced predictive potential. Additionally, for HCC patients, those with HCC-GRIm score > 2 showed a substantially decreased risk of mortality compared to individuals with HCC-GRIm score ≤ 2 (HR: 2.63, 95% CI: 1.89-3.65, p < 0.001).

CONCLUSION

The ALI and GRIm score served as dependable prognostic indicators for patients undergoing ICI therapy in the context of cancer treatment.

Pathways and molecules for overcoming immunotolerance in metastatic gastrointestinal tumors

Worldwide, gastrointestinal (GI) cancer is recognized as one of the leading malignancies diagnosed in both genders, with mortality largely attributed to metastatic dissemination. It has been identified that in GI cancer, a variety of signaling pathways and key molecules are modified, leading to the emergence of an immunotolerance phenotype. Such modifications are pivotal in the malignancy's evasion of immune detection. Thus, a thorough analysis of the pathways and molecules contributing to GI cancer's immunotolerance is vital for advancing our comprehension and propelling the creation of efficacious pharmacological treatments. In response to this necessity, our review illuminates a selection of groundbreaking cellular signaling pathways associated with immunotolerance in GI cancer, including the Phosphoinositide 3-kinases/Akt, Janus kinase/Signal Transducer and Activator of Transcription 3, Nuclear Factor kappa-light-chain-enhancer of activated B cells, Transforming Growth Factor-beta/Smad, Notch, Programmed Death-1/Programmed Death-Ligand 1, and Wingless and INT-1/beta-catenin-Interleukin 10. Additionally, we examine an array of pertinent molecules like Indoleamine-pyrrole 2,3-dioxygenase, Human Leukocyte Antigen G/E, Glycoprotein A Repetitions Predominant, Clever-1, Interferon regulatory factor 8/Osteopontin, T-cell immunoglobulin and mucin-domain containing-3, Carcinoembryonic antigen-related cell adhesion molecule 1, Cell division control protein 42 homolog, and caspases-1 and -12.

IL7 in combination with radiotherapy stimulates a memory T-cell response to improve outcomes in HNSCC models

Clinically approved head and neck squamous cell carcinoma (HNSCC) immunotherapies manipulate the immune checkpoint blockade (ICB) axis but have had limited success outside of recurrent/metastatic disease. Interleukin-7 (IL7) has been shown to be essential for effector T-cell survival, activation, and proliferation. Here, we show that IL7 in combination with radiotherapy (RT) is effective in activating CD8 + T-cells for reducing tumor growth. Our studies were conducted using both human papillomavirus related and unrelated orthotopic HNSCC murine models. Immune populations from the tumor, draining lymph nodes, and blood were compared between treatment groups and controls using flow cytometry, proteomics, immunofluorescence staining, and RNA sequencing. Treatment with RT and IL7 (RT + IL7) resulted in significant tumor growth reduction, high CD8 T-cell tumor infiltration, and increased proliferation of T-cell progenitors in the bone marrow. IL7 also expanded a memory-like subpopulation of CD8 T-cells. These results indicate that IL7 in combination with RT can serve as an effective immunotherapy strategy outside of the conventional ICB axis to drive the antitumor activity of CD8 T-cells.

The comprehensive landscape of prognosis, immunity, and function of the GLI family by pan-cancer and single-cell analysis

The Hedgehog (Hh) signaling pathway has been implicated in the pathogenesis of various cancers. However, the roles of the downstream GLI family (GLI1, GLI2, and GLI3) in tumorigenesis remain elusive. This study aimed to unravel the genetic alterations of GLI1/2/3 in cancer and their association with the immune microenvironment and related signaling pathways. Firstly, we evaluated the expression profiles of GLI1/2/3 in different cancer types, analyzed their prognostic and predictive values, and assessed their correlation with tumor-infiltrating immune cells. Secondly, we explored the relationships between GLI1/2/3 and genetic mutations, epigenetic modifications, and clinically relevant drugs. Finally, we performed enrichment analysis to decipher the underlying mechanisms of GLI1/2/3 in cancer initiation and progression. Our results revealed that the expression levels of GLI1/2/3 were positively correlated in most cancer tissues, suggesting a cooperative role of these factors in tumorigenesis. We also identified tissue-specific expression patterns of GLI1/2/3, which may reflect the distinct functions of these factors in different cell types. Furthermore, GLI1/2/3 expression displayed significant associations with poor prognosis in several cancers, indicating their potential as prognostic biomarkers and therapeutic targets. Importantly, we found that GLI1/2/3 modulated the immune microenvironment by regulating the recruitment, activation, and polarization of cancer-associated fibroblasts, endothelial cells, and macrophages. Additionally, functional enrichment analyses indicated that GLI1/2/3 are involved in the regulation of epithelial-mesenchymal transition (EMT). Together, our findings shed new light on the roles of GLI1/2/3 in tumorigenesis and provide a potential basis for the development of novel therapeutic strategies targeting GLI-mediated signaling pathways in cancer.

Vitreous Olink proteomics reveals inflammatory biomarkers for diagnosis and prognosis of traumatic proliferative vitreoretinopathy

Background

The aim of this study was to identify inflammatory biomarkers in traumatic proliferative vitreoretinopathy (TPVR) patients and further validate the expression curve of particular biomarkers in the rabbit TPVR model.

Methods

The Olink Inflammation Panel was used to compare the differentially expressed proteins (DEPs) in the vitreous of TPVR patients 7-14 days after open globe injury (OGI) (N = 19) and macular hole patients (N = 22), followed by correlation analysis between DEPs and clinical signs, protein-protein interaction (PPI) analysis, area under the receiver operating characteristic curve (AUC) analysis, and function enrichment analysis. A TPVR rabbit model was established and expression levels of candidate interleukin family members (IL-6, IL-7, and IL-33) were measured by enzyme-linked immunosorbent assay (ELISA) at 0, 1, 3, 7, 10, 14, and 28 days after OGI.

Results

Forty-eight DEPs were detected between the two groups. Correlation analysis showed that CXCL5, EN-RAGE, IL-7, ADA, CD5, CCL25, CASP8, TWEAK, and IL-33 were significantly correlated with clinical signs including ocular wound characteristics, PVR scoring, PVR recurrence, and final visual acuity (R = 0.467-0.699, p < 0.05), and all with optimal AUC values (0.7344-1). Correlations between DEP analysis and PPI analysis further verified that IL-6, IL-7, IL-8, IL-33, HGF, and CXCL5 were highly interactive (combined score: 0.669-0.983). These DEPs were enriched in novel pathways such as cancer signaling pathway (N = 14, p < 0.000). Vitreous levels of IL-6, IL-7, and IL-33 in the rabbit TPVR model displayed consistency with the trend in Olink data, all exhibiting marked differential expression 1 day following the OGI.

Conclusion

IL-7, IL-33, EN-RAGE, TWEAK, CXCL5, and CD5 may be potential biomarkers for TPVR pathogenesis and prognosis, and early post-injury may be an ideal time for TPVR intervention targeting interleukin family biomarkers.

Glycogen metabolism-mediated intercellular communication in the tumor microenvironment influences liver cancer prognosis

Glycogen metabolism plays a key role in the development of hepatocellular carcinoma (HCC), but the function of glycogen metabolism genes in the tumor microenvironment (TME) is still to be elucidated. Single-cell RNA-seq data were obtained from ten HCC tumor samples totaling 64,545 cells, and 65 glycogen metabolism genes were analyzed by a nonnegative matrix factorization (NMF). The prognosis and immune response of new glycogen TME cell clusters were predicted by using HCC and immunotherapy cohorts from public databases. HCC single-cell analysis was divided into fibroblasts, NT T cells, macrophages, endothelial cells, and B cells, which were separately divided into new cell clusters by glycogen metabolism gene annotation. Pseudo-temporal trajectory analysis demonstrated the temporal differentiation trajectory of different glycogen subtype cell clusters. Cellular communication analysis revealed extensive interactions between endothelial cells with glycogen metabolizing TME cell-related subtypes and different glycogen subtype cell clusters. SCENIC analysis of transcription factors upstream of TME cell clusters with different glycogen metabolism. In addition, TME cell clusters of glycogen metabolism were found to be enriched in expression in CAF subtypes, CD8 depleted, M1, and M2 types. Bulk-seq analysis showed the prognostic significance of glycogen metabolism-mediated TME cell clusters in HCC, while a significant immune response was found in the immunotherapy cohort in patients treated with immune checkpoint blockade (ICB), especially for CAFs, T cells, and macrophages. In summary, our study reveals for the first time that glycogen metabolism mediates intercellular communication in the hepatocellular carcinoma microenvironment while elucidating the anti-tumor mechanisms and immune prognostic responses of different subtypes of cell clusters.

Comprehending the cuproptosis and cancer-immunity cycle network: delving into the immune landscape and its predictive role in breast cancer immunotherapy responses and clinical endpoints

Background

The role of cuproptosis, a phenomenon associated with tumor metabolism and immunological identification, remains underexplored, particularly in relation to the cancer-immunity cycle (CIC) network. This study aims to rigorously examine the impact of the cuproptosis-CIC nexus on immune reactions and prognostic outcomes in patients with breast cancer (BC), striving to establish a comprehensive prognostic model.

Methods

In the study, we segregated data obtained from TCGA, GEO, and ICGC using CICs retrieved from the TIP database. We constructed a genetic prognostic framework using the LASSO-Cox model, followed by its validation through Cox proportional hazards regression. This framework's validity was further confirmed with data from ICGC and GEO. Explorations of the tumor microenvironment were carried out through the application of ESTIMATE and CIBERSORT algorithms, as well as machine learning techniques, to identify potential treatment strategies. Single-cell sequencing methods were utilized to delineate the spatial distribution of key genes within the various cell types in the tumor milieu. To explore the critical role of the identified CICs, experiments were conducted focusing on cell survival and migration abilities.

Results

In our research, we identified a set of 4 crucial cuproptosis-CICs that have a profound impact on patient longevity and their response to immunotherapy. By leveraging these identified CICs, we constructed a predictive model that efficiently estimates patient prognoses. Detailed analyses at the single-cell level showed that the significance of CICs. Experimental approaches, including CCK-8, Transwell, and wound healing assays, revealed that the protein HSPA9 restricts the growth and movement of breast cancer cells. Furthermore, our studies using immunofluorescence techniques demonstrated that suppressing HSPA9 leads to a notable increase in ceramide levels.

Conclusion

This research outlines a network of cuproptosis-CICs and constructs a predictive nomogram. Our model holds great promise for healthcare professionals to personalize treatment approaches for individuals with breast cancer. The work provides insights into the complex relationship between the cuproptosis-CIC network and the cancer immune microenvironment, setting the stage for novel approaches to cancer immunotherapy. By focusing on the essential gene HSPA9 within the cancer-immunity cycle, this strategy has the potential to significantly improve the efficacy of treatments against breast cancer.

Genetically predicted circulating levels of cytokines and the risk of oral cavity and pharyngeal cancer: a bidirectional mendelian-randomization study

Background: Epidemiological research has established associations between various inflammatory cytokines and the occurrence of oral cancer and oropharyngeal cancer (OCPC). We performed a Mendelian randomization (MR) analysis to systematically investigate the causal relationship between inflammatory cytokines and OCPC. Methods: We performed a bidirectional two-sample MR analysis using OCPC from 12 studies (6,034 cases and 6,585 controls) and genome-wide association study (GWAS) results for 41 serum cytokines from 8,293 Finns, respectively. Inverse variance weighting was used as the primary MR method and four additional MR methods (MR Egger, Weighted median, Simple mode, Weighted mode) were used to examine genetic associations between inflammatory traits and OCPC, and Cochran's Q test, MR-Egger intercept, leave-one-out analysis, funnel plot, and multivariate MR (MVMR) analysis were used to assess the MR results. Results: The results suggested a potential association between high gene expression of Macrophage inflammatory protein-1α (MIP1α/CCL3) and an increased risk of OCPC (Odds Ratio (OR): 1.71, 95% Confidence Interval (CI): 1.09-2.68, p = 0.019). Increasing the expression levels of the interleukin-7 (IL-7) gene by 1 standard deviation reduced the risk of OCPC (OR: 0.64, 95%CI: 0.48-0.86, p = 0.003). In addition, multivariate Mendelian randomization analysis also showed the same results (MIP1α/CCL3, OR: 1.002, 95% CI: 0.919-1.092, p = 0.044; IL-7, OR: 0.997, 95% CI: 0.994-0.999, p = 0.011). Conversely, there was a positive correlation between genetic susceptibility to OCPC and an increase in Interleukin-4 (IL-4) (OR: 1.04, 95%CI: 1.00-1.08, p = 0.027). Conclusion: Our study systematically assessed the association between inflammatory cytokines and the risk of OCPC. We identified two upstream regulatory factors (IL-7 and CCL3) and one downstream effector factor (IL-4) that were associated with OCPC, offering potential avenues for the development of novel treatments.

Research insights into the chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing family (CMTM): their roles in various tumors

The chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing (CMTM) family includes CMTM1-8 and CKLF, and they play key roles in the hematopoietic, immune, cardiovascular, and male reproductive systems, participating in the physiological functions, cancer, and other diseases associated with these systems. CMTM family members activate and chemoattract immune cells to affect the proliferation and invasion of tumor cells through a similar mechanism, the structural characteristics typical of chemokines and transmembrane 4 superfamily (TM4SF). In this review, we discuss each CMTM family member's chromosomal location, involved signaling pathways, expression patterns, and potential roles, and mechanisms of action in pancreatic, breast, gastric and liver cancers. Furthermore, we discuss several clinically applied tumor therapies targeted at the CMTM family, indicating that CMTM family members could be novel immune checkpoints and potential targets effective in tumor treatment.

Systematic analysis of the prognostic value and immunological function of LTBR in human cancer

Lymphotoxin beta receptor (LTBR) is a positive T cell proliferation regulator gene. It is closely associated with the tumor immune microenvironment. However, its role in cancer and immunotherapy is unclear. Firstly, the expression level and prognostic value of LTBR were analyzed. Secondly, the expression of LTBR in clinical stages, immune subtypes, and molecular subtypes was analyzed. The correlation between LTBR and immune regulatory genes, immune checkpoint genes, and RNA modification genes was then analyzed. Correlations between LTBR and immune cells, scores, cancer-related functional status, tumor stemness index, mismatch repair (MMR) genes, and DNA methyltransferase were also analyzed. In addition, we analyzed the role of LTBR in DNA methylation, mutational status, tumor mutation burden (TMB), and microsatellite instability (MSI). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the role of LTBR in pan-cancer. Finally, the drugs associated with LTBR were analyzed. The expression of LTBR was confirmed using quantitative real-time PCR and Western blot. LTBR is significantly overexpressed in most cancers and is associated with low patient survival. In addition, LTBR expression was strongly correlated with immune cells, score, cancer-related functional status, tumor stemness index, MMR genes, DNA methyltransferase, DNA methylation, mutational status, TMB, and MSI. Enrichment analysis revealed that LTBR was associated with apoptosis, necroptosis, and immune-related pathways. Finally, multiple drugs targeting LTBR were identified. LTBR is overexpressed in several tumors and is associated with a poor prognosis. It is related to immune-related genes and immune cell infiltration.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Machine learning-enhanced insights into sphingolipid-based prognostication: revealing the immunological landscape and predictive proficiency for immunomotherapy and chemotherapy responses in pancreatic carcinoma

Background: With a poor prognosis for affected individuals, pancreatic adenocarcinoma (PAAD) is known as a complicated and diverse illness. Immunocytes have become essential elements in the development of PAAD. Notably, sphingolipid metabolism has a dual function in the development of tumors and the invasion of the immune system. Despite these implications, research on the predictive ability of sphingolipid variables for PAAD prognosis is strikingly lacking, and it is yet unclear how they can affect PAAD immunotherapy and targeted pharmacotherapy. Methods: The investigation process included SPG detection while also being pertinent to the prognosis for PAAD. Both the analytical capability of CIBERSORT and the prognostic capability of the pRRophetic R package were used to evaluate the immunological environments of the various HCC subtypes. In addition, CCK-8 experiments on PAAD cell lines were carried out to confirm the accuracy of drug sensitivity estimates. The results of these trials, which also evaluated cell survival and migratory patterns, confirmed the usefulness of sphingolipid-associated genes (SPGs). Results: As a result of this thorough investigation, 32 SPGs were identified, each of which had a measurable influence on the dynamics of overall survival. This collection of genes served as the conceptual framework for the development of a prognostic model, which was carefully assembled from 10 chosen genes. It should be noted that this grouping of patients into cohorts with high and low risk was a sign of different immune profiles and therapy responses. The increased abundance of SPGs was identified as a possible sign of inadequate responses to immune-based treatment approaches. The careful CCK-8 testing carried out on PAAD cell lines was of the highest importance for providing clear confirmation of drug sensitivity estimates. Conclusion: The significance of Sphingolipid metabolism in the complex web of PAAD development is brought home by this study. The novel risk model, built on the complexity of sphingolipid-associated genes, advances our understanding of PAAD and offers doctors a powerful tool for developing personalised treatment plans that are specifically suited to the unique characteristics of each patient.

Associations of genetically predicted circulating levels of cytokines with telomere length: a Mendelian randomization study

Background

Telomere length (TL) has been regarded as a biomarker of aging, and TL shortening is associated with numerous chronic illnesses. The mounting evidence has shown that inflammatory cytokines are involved in maintaining or shortening TL, the causality of cytokines with TL remains unknown. Therefore, we performed a two-sample Mendelian randomization (MR) analysis to estimate the underlying correlations of circulating inflammatory cytokines with TL.

Methods

Genetic instrumental variables for inflammatory cytokines were identified through a genome-wide association study (GWAS) involving 8,293 European individuals. Summary statistics of TL were derived from a UK Bio-bank cohort comprising 472,174 samples of individuals with European descent. We employed the inverse-variance weighted (IVW) approach as our main analysis, and to ensure the reliability of our findings, we also conducted additional analyses including the weighted median, MR-Egger, MR pleiotropy residual sum and outlier test, and weighted model. Lastly, the reverse MR analyses were performed to estimate the likelihood of inverse causality between TL and the cytokines identified in the forward MR analysis. Cochran's Q test were employed to quantify the degree of heterogeneity.

Results

After applying Bonferroni correction, a higher circulating level of Interleukin-7 (IL-7) was suggestively associated with TL maintaining (OR:1.01, 95%CI:1.00-1.02, P=0.032 by IVW method). The study also revealed suggestive evidence indicating the involvement of Interleukin-2 receptor, alpha subunit (IL-2Rα) level was negatively associated with TL maintaining (OR:0.98, 95%CI:0.96-1.00, P=0.045 by IVW method), and the weighted median approach was consistent (OR:0.99, 95%CI:0.97-1.00, P=0.035). According to the findings of reverse MR analysis, no significant causal relationship between TL and cytokines was explored. Our analysis did not reveal any substantial heterogeneity in the Single nucleotide polymorphisms or horizontal pleiotropy.

Conclusions

Our MR analysis yielded suggestive evidence supporting the causality between circulating IL-7 and IL-2Rα and telomere length, necessitating further investigations to elucidate the mechanisms by which these inflammatory cytokines may impact the progression of telomeres.

Molecular characterization of cancer-intrinsic immune evasion genes indicates prognosis and tumour microenvironment infiltration in osteosarcoma

Cancer-intrinsic immune evasion (IE) to cells is a critical factor in tumour growth and progression, yet the molecular characterization of IE genes (IEGs) in osteosarcoma remains underexplored. In this study, 85 osteosarcoma patients were comprehensively analyzed based on 182 IEGs, leading to the identification of two IE clusters linked to distinct biological processes and clinical outcomes. In addition, two IE clusters demonstrated diverse immune cell infiltration patterns, with IEGcluster A displaying increased levels compared to IEGcluster B. Moreover, an IE score was identified as an independent prognostic factor and nomogram may serve as a practical tool for the individual prognostic evaluation of patients with osteosarcoma. Finally, GBP1, a potential biomarker with high expression in osteosarcoma was identified. The findings of this study highlight the presence of two IE clusters, each associated with differing patient outcomes and immune infiltration properties. The IE score may serve to assess individual patient IE characteristics, enhance comprehension of immune features, and guide more efficacious treatment approaches.

The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy

BACKGROUND

Research on immunogenic cell death (ICD) in lung adenocarcinoma (LUAD) has been relatively limited. This study aims to create ICD-related signatures for accurate survival prognosis prediction in LUAD patients, addressing the challenge of lacking reliable early prognostic indicators for this type of cancer.

METHODS

Using single-cell RNA sequencing (scRNA-seq) analysis, ICD activity in cells was calculated by AUCell algorithm, divided into high- and low-ICD groups according to median values, and key ICD regulatory genes were identified through differential analysis, and these genes were integrated into TCGA data to construct prognostic signatures using LASSO and COX regression analysis, and multi-dimensional analysis of ICD-related signatures in terms of prognosis, immunotherapy, tumor microenvironment (TME), and mutational landscape.

RESULTS

The constructed signature reveals a pronounced disparity in prognosis between the high- and low-risk groups of LUAD patients. The statistical discrepancies in survival times among LUAD patients from both the TCGA and GEO databases further corroborate this observation. Additionally, heightened levels of immune cell infiltration expression are evidenced in the low-risk group, suggesting a potential benefit from immunotherapeutic interventions for these patients. The expression levels of pivotal risk-associated genes in tissue samples were assessed utilizing qRT-PCR, thereby unveiling PITX3 as a plausible therapeutic target in the context of LUAD.

CONCLUSIONS

Our constructed ICD-related signatures provide help in predicting the prognosis and immunotherapy of LUAD patients, and to some extent guide the clinical treatment of LUAD patients.

Revealing the diagnostic value and immune infiltration of senescence-related genes in endometriosis: a combined single-cell and machine learning analysis

Introduction: Endometriosis is a prevalent and recurrent medical condition associated with symptoms such as pelvic discomfort, dysmenorrhea, and reproductive challenges. Furthermore, it has the potential to progress into a malignant state, significantly impacting the quality of life for affected individuals. Despite its significance, there is currently a lack of precise and non-invasive diagnostic techniques for this condition. Methods: In this study, we leveraged microarray datasets and employed a multifaceted approach. We conducted differential gene analysis, implemented weighted gene co-expression network analysis (WGCNA), and utilized machine learning algorithms, including random forest, support vector machine, and LASSO analysis, to comprehensively explore senescence-related genes (SRGs) associated with endometriosis. Discussion: Our comprehensive analysis, which also encompassed profiling of immune cell infiltration and single-cell analysis, highlights the therapeutic potential of this gene assemblage as promising targets for alleviating endometriosis. Furthermore, the integration of these biomarkers into diagnostic protocols promises to enhance diagnostic precision, offering a more effective diagnostic journey for future endometriosis patients in clinical settings. Results: Our meticulous investigation led to the identification of a cluster of genes, namely BAK1, LMNA, and FLT1, which emerged as potential discerning biomarkers for endometriosis. These biomarkers were subsequently utilized to construct an artificial neural network classifier model and were graphically represented in the form of a Nomogram.

Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy

Background: Uveal melanoma (UVM) is a primary intraocular malignancy that poses a significant threat to patients' visual function and life. The basement membrane (BM) is critical for establishing and maintaining cell polarity, adult function, embryonic and organ morphogenesis, and many other biological processes. Some basement membrane protein genes have been proven to be prognostic biomarkers for various cancers. This research aimed to develop a novel risk assessment system based on BMRGs that would serve as a theoretical foundation for tailored and accurate treatment. Methods: We used gene expression profiles and clinical data from the TCGA-UVM cohort of 80 UVM patients as a training set. 56 UVM patients from the combined cohort of GSE84976 and GSE22138 were employed as an external validation dataset. Prognostic characteristics of basement membrane protein-related genes (BMRGs) were characterized by Lasso, stepwise multifactorial Cox. Multivariate analysis revealed BMRGs to be independent predictors of UVM. The TISCH database probes the crosstalk of BMEGs in the tumor microenvironment at the single-cell level. Finally, we investigated the function of ITGA5 in UVM using multiple experimental techniques, including CCK8, transwell, wound healing assay, and colony formation assay. Results: There are three genes in the prognostic risk model (ADAMTS10, ADAMTS14, and ITGA5). After validation, we determined that the model is quite reliable and accurately forecasts the prognosis of UVM patients. Immunotherapy is more likely to be beneficial for UVM patients in the high-risk group, whereas the survival advantage may be greater for UVM patients in the low-risk group. Knockdown of ITGA5 expression was shown to inhibit the proliferation, migration, and invasive ability of UVM cells in vitro experiments. Conclusion: The 3-BMRGs feature model we constructed has excellent predictive performance which plays a key role in the prognosis, informing the individualized treatment of UVM patients. It also provides a new perspective for assessing pre-immune efficacy.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.

Reprogramming of Treg cells in the inflammatory microenvironment during immunotherapy: a literature review

Regulatory T cells (Treg), as members of CD4+ T cells, have garnered extensive attention in the research of tumor progression. Treg cells have the function of inhibiting the immune effector cells, preventing tissue damage, and suppressing inflammation. Under the stimulation of the tumor inflammatory microenvironment (IM), the reprogramming of Treg cells enhances their suppression of immune responses, ultimately promoting tumor immune escape or tumor progression. Reducing the number of Treg cells in the IM or lowering the activity of Treg cells while preventing their reprogramming, can help promote the body's anti-tumor immune responses. This review introduces a reprogramming mechanism of Treg cells in the IM; and discusses the regulation of Treg cells on tumor progression. The control of Treg cells and the response to Treg inflammatory reprogramming in tumor immunotherapy are analyzed and countermeasures are proposed. This work will provide a foundation for downregulating the immunosuppressive role of Treg in the inflammatory environment in future tumor immunotherapy.

Unveiling the role of regulatory T cells in the tumor microenvironment of pancreatic cancer through single-cell transcriptomics and in vitro experiments

Background

In order to investigate the impact of Treg cell infiltration on the immune response against pancreatic cancer within the tumor microenvironment (TME), and identify crucial mRNA markers associated with Treg cells in pancreatic cancer, our study aims to delve into the role of Treg cells in the anti-tumor immune response of pancreatic cancer.

Methods

The ordinary transcriptome data for this study was sourced from the GEO and TCGA databases. It was analyzed using single-cell sequencing analysis and machine learning. To assess the infiltration level of Treg cells in pancreatic cancer tissues, we employed the CIBERSORT method. The identification of genes most closely associated with Treg cells was accomplished through the implementation of weighted gene co-expression network analysis (WGCNA). Our analysis of single-cell sequencing data involved various quality control methods, followed by annotation and advanced analyses such as cell trajectory analysis and cell communication analysis to elucidate the role of Treg cells within the pancreatic cancer microenvironment. Additionally, we categorized the Treg cells into two subsets: Treg1 associated with favorable prognosis, and Treg2 associated with poor prognosis, based on the enrichment scores of the key genes. Employing the hdWGCNA method, we analyzed these two subsets to identify the critical signaling pathways governing their mutual transformation. Finally, we conducted PCR and immunofluorescence staining in vitro to validate the identified key genes.

Results

Based on the results of immune infiltration analysis, we observed significant infiltration of Treg cells in the pancreatic cancer microenvironment. Subsequently, utilizing the WGCNA and machine learning algorithms, we ultimately identified four Treg cell-related genes (TRGs), among which four genes exhibited significant correlations with the occurrence and progression of pancreatic cancer. Among them, CASP4, TOB1, and CLEC2B were associated with poorer prognosis in pancreatic cancer patients, while FYN showed a correlation with better prognosis. Notably, significant differences were found in the HIF-1 signaling pathway between Treg1 and Treg2 cells identified by the four genes. These conclusions were further validated through in vitro experiments.

Conclusion

Treg cells played a crucial role in the pancreatic cancer microenvironment, and their presence held a dual significance. Recognizing this characteristic was vital for understanding the limitations of Treg cell-targeted therapies. CASP4, FYN, TOB1, and CLEC2B exhibited close associations with infiltrating Treg cells in pancreatic cancer, suggesting their involvement in Treg cell functions. Further investigation was warranted to uncover the mechanisms underlying these associations. Notably, the HIF-1 signaling pathway emerged as a significant pathway contributing to the duality of Treg cells. Targeting this pathway could potentially revolutionize the existing treatment approaches for pancreatic cancer.

Revealing the role of regulatory T cells in the tumor microenvironment of lung adenocarcinoma: a novel prognostic and immunotherapeutic signature

Background

Regulatory T cells (Tregs), are a key class of cell types in the immune system. In the tumor microenvironment (TME), the presence of Tregs has important implications for immune response and tumor development. Relatively little is known about the role of Tregs in lung adenocarcinoma (LUAD).

Methods

Tregs were identified using but single-cell RNA sequencing (scRNA-seq) analysis and interactions between Tregs and other cells in the TME were investigated. Next, we used multiple bulk RNA-seq datasets to construct risk models based on marker genes of Tregs and explored differences in prognosis, mutational landscape, immune cell infiltration and immunotherapy between high- and low-risk groups, and finally, qRT-PCR and cell function experiments were performed to validate the model genes.

Results

The cellchat analysis showed that MIF-(CD74+CXCR4) pairs play a key role in the interaction of Tregs with other cell subpopulations, and the Tregs-associated signatures (TRAS) could well classify multiple LUAD cohorts into high- and low-risk groups. Immunotherapy may offer greater potential benefits to the low-risk group, as indicated by their superior survival, increased infiltration of immune cells, and heightened expression of immune checkpoints. Finally, the experiment verified that the model genes LTB and PTTG1 were relatively highly expressed in cancer tissues, while PTPRC was relatively highly expressed in paracancerous tissues. Colony Formation assay confirmed that knockdown of PTTG1 reduced the proliferation ability of LUAD cells.

Conclusion

TRAS were constructed using scRNA-seq and bulk RNA-seq to distinguish patient risk subgroups, which may provide assistance in the clinical management of LUAD patients.

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

Background

Gliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.

Methods

This study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.

Results

The TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).

Conclusion

Overall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.

A novel and robust pyroptosis-related prognostic signature predicts prognosis and response to immunotherapy in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a highly malignant gastrointestinal tumor, has a poor prognosis and high mortality rate. Pyroptosis could regulate tumor cell proliferation, invasion, and metastasis, thereby affecting the prognosis of cancer patients. However, the role of pyroptosis-related genes (PRGs) in ESCC remains unclear. This study selected 33 PRGs, and finally identified 29 PRGs that were differentially expressed between ESCC and normal esophageal tissues. The genetic mutation variation landscape of PRG in ESCC was also summarised. Based on consensus clustering for the 33 PRGs, all ESCC patients could be divided into two subtypes. Functional enrichment analysis revealed that these 33 PRGs were mainly involved in cytokine production, interleukin-1 production, and the NOD-like receptor signalling pathway. We created a prognostic PRG signature based on least absolute shrinkage and selection operator regression and Cox regression analysis with good survival prediction ability in both GEO and TCGA cohorts. Combined with the clinical characteristics, signature-based risk score was found to be an independent factor for predicting the OS of ESCC patients. A nomogram with enhanced precision for forecasting ESCC was established based on various independent prognostic elements. Significant correlation was observed between prognostic PRGs and immune-cell infiltration, tumor mutation burden, microsatellite instability, immune checkpoint, and drug sensitivity. Finally, we validated the expression of four PRGs in ESCC cell lines and tissues samples. In conclusion, the PRGs exerted significant effects on tumor immunity and prognosis of ESCC.

GADD45B regulates the carcinogenesis process of chronic atrophic gastritis and the metabolic pathways of gastric cancer

Background

Gastric cancer continues to be a significant global healthcare challenge, and its burden remains substantial. The development of gastric cancer (GC) is closely linked to chronic atrophic gastritis (CAG), yet there is a scarcity of research exploring the underlying mechanisms of CAG-induced carcinogenesis.

Methods

In this study, we conducted a comprehensive investigation into the oncogenes involved in CAG using both bulk transcriptome and single-cell transcriptome data. Our approach employed hdWGCNA to identify pathogenic genes specific to CAG, with non-atrophic gastritis (NAG) serving as the control group. Additionally, we compared CAG with GC, using normal gastric tissue as the control group in the single-cell transcriptome analysis. By intersecting the identified pathogenic genes, we pinpointed key network molecules through protein interaction network analysis. To further refine the gene selection, we applied LASSO, SVM-RFE, and RF techniques, which resulted in a set of cancer-related genes (CRGs) associated with CAG. To identify CRGs potentially linked to gastric cancer progression, we performed a univariate COX regression analysis on the gene set. Subsequently, we explored the relationship between CRGs and immune infiltration, drug sensitivity, and clinical characteristics in gastric cancer patients. We employed GSVA to investigate how CRGs regulated signaling pathways in gastric cancer cells, while an analysis of cell communication shed light on the impact of CRGs on signal transmission within the gastric cancer tumor microenvironment. Lastly, we analyzed changes in metabolic pathways throughout the progression of gastric cancer.

Results

Using hdWGCNA, we have identified a total of 143 pathogenic genes that were shared by CAG and GC. To further investigate the underlying mechanisms, we conducted protein interaction network analysis and employed machine learning screening techniques. As a result, we have identified 15 oncogenes that are specifically associated with chronic atrophic gastritis. By performing ROC reanalysis and prognostic analysis, we have determined that GADD45B is the most significant gene involved in the carcinogenesis of CAG. Immunohistochemical staining and differential analysis have revealed that GADD45B expression was low in GC tissues while high in normal gastric tissues. Moreover, based on prognostic analysis, high expression of GADD45B has been correlated with poor prognosis in GC patients. Additionally, an analysis of immune infiltration has shown a relationship between GADD45B and the infiltration of various immune cells. By correlating GADD45B with clinical characteristics, we have found that it primarily affects the depth of invasion in GC. Through cell communication analysis, we have discovered that the CD99 signaling pathway network and the CDH signaling pathway network are the main communication pathways that significantly alter the microenvironment of gastric tissue during the development of chronic atrophic gastritis. Specifically, GADD45B-low GC cells were predominantly involved in the network communication of the CDH signaling pathway, while GADD45B-high GC cells played a crucial role in both signaling pathways. Furthermore, we have identified several metabolic pathways, including D-Glutamine and D-glutamate metabolism and N-Glycan biosynthesis, among others, that played important roles in the occurrence and progression of GC, in addition to the six other metabolic pathways. In summary, our study highlighted the discovery of 143 pathogenic genes shared by CAG and GC, with a specific focus on 15 oncogenes associated with CAG. We have identified GADD45B as the most important gene in the carcinogenesis of CAG, which exhibited differential expression in GC tissues compared to normal gastric tissues. Moreover, GADD45B expression was correlated with patient prognosis and is associated with immune cell infiltration. Our findings also emphasized the impact of the CD99 and CDH signaling pathway networks on the microenvironment of gastric tissue during the development of CAG. Additionally, we have identified key metabolic pathways involved in GC progression.

Conclusion

GADD45B, an oncogene implicated in chronic atrophic gastritis, played a critical role in GC development. Decreased expression of GADD45B was associated with the onset of GC. Moreover, GADD45B expression levels were closely tied to poor prognosis in GC patients, influencing the infiltration patterns of various cells within the tumor microenvironment, as well as impacting the metabolic pathways involved in GC progression.

Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) represents a prominent gastrointestinal malignancy with a grim clinical outlook. In this regard, the discovery of novel early biomarkers holds substantial promise for ameliorating HCC-associated mortality. Efferocytosis, a vital immunological process, assumes a central position in the elimination of apoptotic cells. However, comprehensive investigations exploring the role of efferocytosis-related genes (EFRGs) in HCC are sparse, and their regulatory influence on HCC immunotherapy and targeted drug interventions remain poorly understood.

Methods

RNA sequencing data and clinical characteristics of HCC patients were acquired from the TCGA database. To identify prognostically significant genes in HCC, we performed the limma package and conducted univariate Cox regression analysis. Subsequently, machine learning algorithms were employed to identify hub genes. To assess the immunological landscape of different HCC subtypes, we employed the CIBERSORT algorithm. Furthermore, single-cell RNA sequencing (scRNA-seq) was utilized to investigate the expression levels of ERFGs in immune cells and to explore intercellular communication within HCC tissues. The migratory capacity of HCC cells was evaluated using CCK-8 assays, while drug sensitivity prediction reliability was determined through wound-healing assays.

Results

We have successfully identified a set of nine genes, termed EFRGs, that hold significant potential for the establishment of a hepatocellular carcinoma-specific prognostic model. Furthermore, leveraging the individual risk scores derived from this model, we were able to stratify patients into two distinct risk groups, unveiling notable disparities in terms of immune infiltration patterns and response to immunotherapy. Notably, the model's capacity to accurately predict drug responses was substantiated through comprehensive experimental investigations, encompassing wound-healing assay, and CCK8 experiments conducted on the HepG2 and Huh7 cell lines.

Conclusions

We constructed an EFRGs model that serves as valuable tools for prognostic assessment and decision-making support in the context of immunotherapy and chemotherapy.

Prognostic model for hepatocellular carcinoma based on anoikis-related genes: immune landscape analysis and prediction of drug sensitivity

Background

Hepatocellular carcinoma (HCC) represents a complex ailment characterized by an unfavorable prognosis in advanced stages. The involvement of immune cells in HCC progression is of significant importance. Moreover, metastasis poses a substantial impediment to enhanced prognostication for HCC patients, with anoikis playing an indispensable role in facilitating the distant metastasis of tumor cells. Nevertheless, limited investigations have been conducted regarding the utilization of anoikis factors for predicting HCC prognosis and assessing immune infiltration. This present study aims to identify hepatocellular carcinoma-associated anoikis-related genes (ANRGs), establish a robust prognostic model for HCC, and delineate distinct immune characteristics based on the anoikis signature. Cell migration and cytotoxicity experiments were performed to validate the accuracy of the ANRGs model.

Methods

Consensus clustering based on ANRGs was employed in this investigation to categorize HCC samples obtained from both TCGA and Gene Expression Omnibus (GEO) cohorts. To assess the differentially expressed genes, Cox regression analysis was conducted, and subsequently, prognostic gene signatures were constructed using LASSO-Cox methodology. External validation was performed at the International Cancer Genome Conference. The tumor microenvironment (TME) was characterized utilizing ESTIMATE and CIBERSORT algorithms, while machine learning techniques facilitated the identification of potential target drugs. The wound healing assay and CCK-8 assay were employed to evaluate the migratory capacity and drug sensitivity of HCC cell lines, respectively.

Results

Utilizing the TCGA-LIHC dataset, we devised a nomogram integrating a ten-gene signature with diverse clinicopathological features. Furthermore, the discriminative potential and clinical utility of the ten-gene signature and nomogram were substantiated through ROC analysis and DCA. Subsequently, we devised a prognostic framework leveraging gene expression data from distinct risk cohorts to predict the drug responsiveness of HCC subtypes.

Conclusion

In this study, we have established a promising HCC prognostic ANRGs model, which can serve as a valuable tool for clinicians in selecting targeted therapeutic drugs, thereby improving overall patient survival rates. Additionally, this model has also revealed a strong connection between anoikis and immune cells, providing a potential avenue for elucidating the mechanisms underlying immune cell infiltration regulated by anoikis.

An immune-related gene prognostic index for predicting prognosis in patients with colorectal cancer

Background

Colorectal cancer (CRC) is one of the most common solid malignant burdens worldwide. Cancer immunology and immunotherapy have become fundamental areas in CRC research and treatment. Currently, the method of generating Immune-Related Gene Prognostic Indices (IRGPIs) has been found to predict patient prognosis as an immune-related prognostic biomarker in a variety of tumors. However, their role in patients with CRC remains mostly unknown. Therefore, we aimed to establish an IRGPI for prognosis evaluation in CRC.

Methods

RNA-sequencing data and clinical information of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) databases as training and validation sets, respectively. Immune-related gene data was obtained from the ImmPort and InnateDB databases. The weighted gene co-expression network analysis (WGCNA) was used to identify hub immune-related genes. An IRGPI was then constructed using Cox regression methods. Based on the median risk score of IRGPI, patients could be divided into high-risk and low-risk groups. To further investigate the immunologic differences, Gene set variation analysis (GSVA) studies were conducted. In addition, immune cell infiltration and related functional analysis were used to identify the differential immune cell subsets and related functional pathways.

Results

We identified 49 immune-related genes associated with the prognosis of CRC, 17 of which were selected for an IRGPI. The IRGPI model significantly differentiates the survival rates of CRC patients in the different groups. The IRGPI as an independent prognostic factor significantly correlates with clinico-pathological factors such as age and tumor stage. Furthermore, we developed a nomogram to improve the clinical utility of the IRGPI score. Immuno-correlation analysis in different IRGPI groups revealed distinct immune cell infiltration (CD4⁺ T cells resting memory) and associated pathways (macrophages, Type I IFNs responses, iDCs.), providing new insights into the tumor microenvironment. At last, drug sensitivity analysis revealed that the high-risk IRGPI group was sensitive to 11 and resistant to 15 drugs.

Conclusion

Our study established a promising immune-related risk model for predicting survival in CRC patients. This could help to better understand the correlation between immunity and the prognosis of CRC providing a new perspective for personalized treatment of CRC.

Efferocytosis signatures as prognostic markers for revealing immune landscape and predicting immunotherapy response in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a highly lethal liver cancer with late diagnosis; therefore, the identification of new early biomarkers could help reduce mortality. Efferocytosis, a process in which one cell engulfs another cell, including macrophages, dendritic cells, NK cells, etc., plays a complex role in tumorigenesis, sometimes promoting and sometimes inhibiting tumor development. However, the role of efferocytosis-related genes (ERGs) in HCC progression has been poorly studied, and their regulatory effects in HCC immunotherapy and drug targeting have not been reported. Methods: We downloaded efferocytosis-related genes from the Genecards database and screened for ERGs that showed significant expression changes between HCC and normal tissues and were associated with HCC prognosis. Machine learning algorithms were used to study prognostic gene features. CIBERSORT and pRRophetic R packages were used to evaluate the immune environment of HCC subtypes and predict treatment response. CCK-8 experiments conducted on HCC cells were used to assess the reliability of drug sensitivity prediction. Results: We constructed a prognostic prediction model composed of six genes, and the ROC curve showed good predictive accuracy of the risk model. In addition, two ERG-related subgroups in HCC showed significant differences in tumor immune landscape, immune response, and prognostic stratification. The CCK-8 experiment conducted on HCC cells confirmed the reliability of drug sensitivity prediction. Conclusion: Our study emphasizes the importance of efferocytosis in HCC progression. The risk model based on efferocytosis-related genes developed in our study provides a novel precision medicine approach for HCC patients, allowing clinicians to customize treatment plans based on unique patient characteristics. The results of our investigation carry noteworthy implications for the development of individualized treatment approaches involving immunotherapy and chemotherapy, thereby potentially facilitating the realization of personalized and more efficacious therapeutic interventions for HCC.

Multi-omic analysis of glycolytic signatures: exploring the predictive significance of heterogeneity and stemness in immunotherapy response and outcomes in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a global health challenge with complex pathophysiology, characterized by high mortality rates and poor early detection due to significant tumor heterogeneity. Stemness significantly contributes to the heterogeneity of HCC tumors, and glycolysis is crucial for maintaining stemness. However, the predictive significance of glycolysis-related metabolic genes (GMGs) in HCC remains unknown. Therefore, this study aimed to identify critical GMGs and establish a reliable model for HCC prognosis. Methods: GMGs associated with prognosis were identified by evaluating genes with notable expression changes between HCC and normal tissues retrieved from the MsigDB database. Prognostic gene characteristics were established using univariate and multivariate Cox regression studies for prognosis prediction and risk stratification. The "CIBERSORT" and "pRRophetic" R packages were respectively used to evaluate the immunological environment and predict treatment response in HCC subtypes. The HCC stemness score was obtained using the OCLR technique. The precision of drug sensitivity prediction was evaluated using CCK-8 experiments performed on HCC cells. The miagration and invasion ability of HCC cell lines with different riskscores were assessed using Transwell and wound healing assays. Results: The risk model based on 10 gene characteristics showed high prediction accuracy as indicated by the receiver operating characteristic (ROC) curves. Moreover, the two GMG-related subgroups showed considerable variation in the risk of HCC with respect to tumor stemness, immune landscape, and prognostic stratification. The in vitro validation of the model's ability to predict medication response further demonstrated its reliability. Conclusion: Our study highlights the importance of stemness variability and inter-individual variation in determining the HCC risk landscape. The risk model we developed provides HCC patients with a novel method for precision medicine that enables clinical doctors to customize treatment plans based on unique patient characteristics. Our findings have significant implications for tailored immunotherapy and chemotherapy methods, and may pave the way for more personalized and effective treatment strategies for HCC.

Identification and validation of TME-related signatures to predict prognosis and response to anti-tumor therapies in skin cutaneous melanoma

The tumor microenvironment (TME) dynamically regulates cancer progression and affects clinical outcomes. This study aimed to identify molecular subtypes and construct a prognostic risk model based on TME-related signatures in skin cutaneous melanoma (SKCM) patients. We categorized SKCM patients based on transcriptome data of SKCM from The Cancer Genome Atlas (TCGA) database and 29 TME-related gene signatures. Differentially expressed genes were identified using univariate Cox regression and Lasso regression analysis, which were used for risk model construction. The robustness of this model was validated in independent external cohorts. Genetic landscape alterations, immune characteristics, and responsiveness to immunotherapy/chemotherapy were evaluated. Three TME-related subtypes were identified, and subtype C3 exhibited the most favorable prognosis, had enriched immune-related pathways, and possessed more infiltration of T_cells_CD8, T_cells_CD4_memory_activated, and Macrophages_M1 but a lower TumorPurity, whereas Macrophages_M2 were increased in subtype C1 and subtype C2. Subtype C1 was more sensitive to Cisplatin, subtype C2 was more sensitive to Temozolomide, and subtype C3 was more sensitive to Paclitaxel; 8 TME-related genes (NOTCH3, HEYL, ZNF703, ABCC2, PAEP, CCL8, HAPLN3, and HPDL) were screened for risk model construction. High-risk patients had dismal prognosis with good prediction performance. Moreover, low-risk patients were more sensitive to Paclitaxel and Temozolomide, whereas high-risk patients were more sensitive to Cisplatin. This risk model had robustness in predicting prognosis in SKCM patients. The results facilitate the understanding of TME-related genes in SKCM and provide a TME-related genes-based predictive model in prognosis and direction of personalized options for SKCM patients.

Novel chemokine related LncRNA signature correlates with the prognosis, immune landscape, and therapeutic sensitivity of esophageal squamous cell cancer

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is closely correlated with malignant biological characteristics and poor survival. Recently, chemokines have been reported to be involved in the progression of tumors, and they can also regulate the tumor microenvironment. However, it is unclear whether chemokine-related long noncoding RNAs (lncRNAs) affect the prognosis of ESCC.

METHODS

We downloaded RNA-seq and clinical data from the Gene Expression Omnibus (GEO database. Chemokine-related lncRNAs were screened by differential analysis and Pearson correlation analysis. Then, prognosis-related lncRNAs were screened by using univariate COX regression, and risk models were constructed after the least absolute shrinkage and selection operator (LASSO) regression and multivariate COX regression. The predictive value of the signature was assessed using Kaplan-Meier test, time-dependent receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and calibration curve. Moreover, a nomogram to predict patients' 1-year 3-year and 5-year prognosis was constructed. Gene set enrichment analyses (GSEA), Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG), evaluation of immune cell infiltration, and estimation of drug sensitivity were also conducted.

RESULTS

In this study, 677 chemokine-related lncRNAs were first obtained by differential analysis and Pearson correlation. Then, six chemokine-related lncRNAs were obtained by using univariate COX, LASSO and multivariate COX to construct a novel chemokine-related lncRNAs risk model. The signature manifested favorable predictive validity and accuracy both in the testing and training cohorts. The chemokine-related signature could classify ESCC patients into two risk groups well, which indicated that high-risk group exhibited poor prognostic outcome. In addition, this risk model played an important role in predicting signaling pathways, immune cell infiltration, stromal score, and drug sensitivity in ESCC patients.

CONCLUSIONS

These findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thus throwing light on prognostic evaluation and therapeutic targets for ESCC patients.

Identification of novel prognostic biomarkers in the TF-enhancer-target regulatory network in hepatocellular carcinoma and immune infiltration analysis

Background: Hepatocellular carcinoma (HCC) remains notorious for its high malignancy, poor prognosis and high mortality. The exploration of novel therapeutic agents for HCC has remained challenging due to its complex aetiology. Therefore, it is necessary to elucidate the pathogenesis and mechanism of HCC for clinical intervention.

Methods: We collected data from several public data portals and systematically analysed the association between transcription factors (TFs), eRNA-associated enhancers and downstream targets. We next filtered the prognostic genes and established a novel prognosis-related nomogram model. Moreover, we explored the potential mechanisms of the identified prognostic genes. The expression level was validated by several ways.

Results: We first constructed a significant TF-enhancer-target regulatory network and identified DAPK1 as a coregulatory differentially expressed prognosis-related gene. We combined common clinicopathological factors and built a prognostic nomogram model for HCC. We found that our regulatory network was correlated with the processes of synthesizing various substances. Moreover, we explored the role of DAPK1 in HCC and found that it was associated with immune cell infiltration and DNA methylation. Several immunostimulators and targeting drugs could be promising immune therapy targets. The tumor immune microenvironment was analyzed. Finally, the lower DAPK1 expression in HCC was validated via the GEO database, UALCAN cohort, and qRT-PCR.

Conclusion: In conclusion, we established a significant TF-enhancer-target regulatory network and identified downregulated DAPK1 as an important prognostic and diagnostic gene in HCC. Its potential biological functions and mechanisms were annotated using bioinformatics tools.

Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a complex disease with a poor outlook for patients in advanced stages. Immune cells play an important role in the progression of HCC. The metabolism of sphingolipids functions in both tumor growth and immune infiltration. However, little research has focused on using sphingolipid factors to predict HCC prognosis. This study aimed to identify the key sphingolipids genes (SPGs) in HCC and develop a reliable prognostic model based on these genes.

Methods

The TCGA, GEO, and ICGC datasets were grouped using SPGs obtained from the InnateDB portal. A prognostic gene signature was created by applying LASSO-Cox analysis and evaluating it with Cox regression. The validity of the signature was verified using ICGC and GEO datasets. The tumor microenvironment (TME) was examined using ESTIMATE and CIBERSORT, and potential therapeutic targets were identified through machine learning. Single-cell sequencing was used to examine the distribution of signature genes in cells within the TME. Cell viability and migration were tested to confirm the role of the key SPGs.

Results

We identified 28 SPGs that have an impact on survival. Using clinicopathological features and 6 genes, we developed a nomogram for HCC. The high- and low-risk groups were found to have distinct immune characteristics and response to drugs. Unlike CD8 T cells, M0 and M2 macrophages were found to be highly infiltrated in the TME of the high-risk subgroup. High levels of SPGs were found to be a good indicator of response to immunotherapy. In cell function experiments, SMPD2 and CSTA were found to enhance survival and migration of Huh7 cells, while silencing these genes increased the sensitivity of Huh7 cells to lapatinib.

Conclusion

The study presents a six-gene signature and a nomogram that can aid clinicians in choosing personalized treatments for HCC patients. Furthermore, it uncovers the connection between sphingolipid-related genes and the immune microenvironment, offering a novel approach for immunotherapy. By focusing on crucial sphingolipid genes like SMPD2 and CSTA, the efficacy of anti-tumor therapy can be increased in HCC cells.

An m7G-related lncRNA signature predicts prognosis and reveals the immune microenvironment in bladder cancer

Bladder cancer (BC) is a representative malignant tumor type, and the significance of N7-methyguanosine (m7G)-related lncRNAs in BC is still unclear. Utilizing m7G-related lncRNAs, we developed a prognostic model to evaluate BC's prognosis and tumor immunity. First, we selected prognostic lncRNAs related to m7G by co-expression analysis and univariate Cox regression and identified two clusters by consensus clustering. The two clusters differed significantly in terms of overall survival, clinicopathological factors, and immune microenvironment. Then, we further constructed a linear stepwise regression signature by multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients fell into high-risk (HR) and low-risk (LR) groups considering the train group risk score. HR group had worse prognoses when stratified by clinicopathological factors. The receiver operating curve (ROC) suggested that the signature had a better prognostic value. Tumor mutation burden (TMB) showed a negative relevance to the risk score, and patients with low TMB presented a better prognosis. Validation of the signature was carried out with multivariate and univariate Cox regression analysis, nomogram, principal component analysis (PCA), C-Index, and quantitative reverse transcriptase PCR (qRT-PCR). Finally, the gene set enrichment analysis (GSEA) demonstrated the enrichment of tumor-related pathways in HR groups, and single-sample gene set enrichment analysis (ssGSEA) indicated a close association of risk score with tumor immunity. According to the drug sensitivity test, the signature could predict the effects of conventional chemotherapy drugs. In conclusion, our study indicates the close relevance of m7G-related lncRNAs to BC, and the established risk signature can effectively evaluate patient prognosis and tumor immunity and is expected to become a novel prognostic marker for BC patients.

By integrating single-cell RNA-seq and bulk RNA-seq in sphingolipid metabolism, CACYBP was identified as a potential therapeutic target in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a heterogeneous disease with a dismal prognosis for advanced tumors. Immune-associated cells in the microenvironment substantially impact LUAD formation and progression, which has gained increased attention in recent decades. Sphingolipids have a profound impact on tumor formation and immune infiltration. However, few researchers have focused on the utilization of sphingolipid variables in the prediction of LUAD prognosis. The goal of this work was to identify the major sphingolipid-related genes (SRGs) in LUAD and develop a valid prognostic model based on SRGs.

Methods

The most significant genes for sphingolipid metabolism (SM) were identified using the AUCell and WGCNA algorithms in conjunction with single-cell and bulk RNA-seq. LASSO and COX regression analysis was used to develop risk models, and patients were divided into high-and low-risk categories. External nine provided cohorts evaluated the correctness of the models. Differences in immune infiltration, mutation landscape, pathway enrichment, immune checkpoint expression, and immunotherapy were also further investigated in distinct subgroups. Finally, cell function assay was used to verify the role of CACYBP in LUAD cells.

Results

A total of 334 genes were selected as being most linked with SM activity for further investigation, and a risk model consisting of 11 genes was established using lasso and cox regression. According to the median risk value, patients were split into high- and low-risk groups, and the high-risk group had a worse prognosis. The low-risk group had more immune cell infiltration and higher expression of immune checkpoints, which illustrated that the low-risk group was more likely to benefit from immunotherapy. It was verified that CACYBP could increase the ability of LUAD cells to proliferate, invade, and migrate.

Conclusion

The eleven-gene signature identified in this research may help physicians create individualized care plans for LUAD patients. CACYBP may be a new therapeutic target for patients with advanced LUAD.

Exosomes: A potential tool for immunotherapy of ovarian cancer

Ovarian cancer is a malignant tumor of the female reproductive system, with a very poor prognosis and high mortality rates. Chemotherapy and radiotherapy are the most common treatments for ovarian cancer, with unsatisfactory results. Exosomes are a subpopulation of extracellular vesicles, which have a diameter of approximately 30-100 nm and are secreted by many different types of cells in various body fluids. Exosomes are highly stable and are effective carriers of immunotherapeutic drugs. Recent studies have shown that exosomes are involved in various cellular responses in the tumor microenvironment, influencing the development and therapeutic efficacy of ovarian cancer, and exhibiting dual roles in inhibiting and promoting tumor development. Exosomes also contain a variety of genes related to ovarian cancer immunotherapy that could be potential biomarkers for ovarian cancer diagnosis and prognosis. Undoubtedly, exosomes have great therapeutic potential in the field of ovarian cancer immunotherapy. However, translation of this idea to the clinic has not occurred. Therefore, it is important to understand how exosomes could be used in ovarian cancer immunotherapy to regulate tumor progression. In this review, we summarize the biomarkers of exosomes in different body fluids related to immunotherapy in ovarian cancer and the potential mechanisms by which exosomes influence immunotherapeutic response. We also discuss the prospects for clinical application of exosome-based immunotherapy in ovarian cancer.

Prognostic value of SLC4A4 and its correlation with the microsatellite instability in colorectal cancer

Objective

To explore new biomarkers related to microsatellite instability in order to better predict prognosis and guide medication.

Methods

The "limma" R package was used to identify differentially expressed genes in GSE24514, and then weighted correlation network analysis was used to select key genes. Different cell types in the tumor microenvironment were identified and analyzed by single-cell sequencing, with a Lasso regression model used to screen prognostic variables. Furthermore, the correlation between microsatellite instability and potential prognostic variables was explored, as well as the expression characteristics and clinical characteristics of the prognostic variables in the TCGA, UALCAN, and HPA databases. PCR assay was used to investigate the expression of SLC4A4 in colorectal cancer cell lines. Finally, we further verified the expression of SLC4A4 by immunohistochemistry.

Results

First, 844 differentially expressed genes in GSE24514 were identified. Subsequently, weighted co-expression network analysis (WGCNA) of GSE24514 obtained all the genes significantly associated with microsatellite instability (MSI), a total of 1452. Analysis of GSE166555 single cell sequencing data set yielded 1564 differentially expressed genes. The gene sets obtained from the above three analysis processes were intersected, and 174 genes were finally obtained. The Lasso regression model revealed two potential prognostic genes, TIMP1 and SLC4A4, of which, there was a stronger correlation between microsatellite instability and SLC4A4. The mRNA and protein expression of SLC4A4 was significantly decreased in tumors, and patients with low SLC4A4 expression had a poor prognosis. In addition, SLC4A4 was specifically expressed in epithelial cells. In the microenvironment of colorectal cancer, malignant cells have a strong interaction with different stromal cells. PCR showed that SLC4A4 was significantly down-regulated in colorectal cancer cell lines Caco-2, HCT116 and HT29 compared with normal control NCM460 cell lines. Immunohistochemistry also showed low expression of SLC4A4 in colorectal cancer.

Conclusion

SLC4A4, as a tumor suppressor gene, is significantly downregulated and positively correlated with microsatellite instability, thus it may be combined with microsatellite instability to guide colorectal cancer treatment.