Immune Score Research Articles

Construction of thyroid cancer classification and iodine metabolism related diagnostic model using thyroid differentiation score and bioinformation.

To more accurately diagnose and treat patients with different subtypes of thyroid cancer, we constructed a diagnostic model related to the iodine metabolism of THCA subtypes. THCA expression profiles, corresponding clinicopathological information, and single-cell RNA-seq were downloaded from TCGA and GEO databases. Genes related to thyroid differentiation score were obtained by GSVA. Through logistic analyses, the diagnostic model was finally constructed. DCA curve, ROC curve, machine learning, and K-M analysis were used to verify the accuracy of the model. qRT-PCR was used to verify the expression of hub genes in vitro. There were 104 crossover genes between different TDS and THCA subtypes. Finally, 5 genes (ABAT, CHEK1, GPX3, NME5, and PRKCQ) that could independently predict the TDS subpopulation were obtained, and a diagnostic model was constructed. ROC, DCA, and RCS curves exhibited that the model has accurate prediction ability. K-M and subgroup analysis results showed that low model scores were strongly associated with poor PFI in THCA patients. The model score was significantly negatively correlated with T cell follicular helper. In addition, the diagnostic model was significantly negatively correlated with immune scores. Finally, the results of qRT-PCR corresponded with bioinformatics results. This diagnostic model has good diagnostic and prognostic value for THCA patients, and can be used as an independent prognostic indicator for THCA patients.

SPRED3 regulates the NF-κB signaling pathway in thyroid cancer and promotes the proliferation

SPRED3 (Sprouty-related EVH1 domain containing 3) mutants are depicted in various cancers, however, nothing is known about its biofunction in thyroid cancer (THCA). Bioinformatic analyses were conducted to ascertain the level of SPRED3 expression in THCA tissues and its importance in the prognosis of THCA patients. Flag-SPRED3 plasmid and SPRED3-knockout vector were developed to overexpress or deplete the SPRED3 expression in THCA cells. The function of SPRED3 on THCA cell proliferation was examined using the colony formation assay and CCK8 assay. The effect of SPRED3 expression on the transcriptional activity of NF-κB was also examined using luciferase reporter assays. High SPRED3 expression was associated with unfavorable clinical outcomes, advanced tumor characteristics, and traditional molecular markers of papillary thyroid cancer in THCA patients. Genetic analysis revealed differences in mutation rates in key genes between SPRED3-high and SPRED3-low THCA cases. It is also revealed that SPRED3 influenced the immune microenvironment, with increased stromal and immune scores and altered immune cell infiltration. Functionally, SPRED3 overexpression enhanced THCA cell viability and colony formation, while its depletion reduced cell growth and proliferation. In vivo experiments in mice confirmed the inhibitory effect of SPRED3 depletion on tumor growth. Mechanically, we found that SPRED3 activated the NF-κB signaling. For the first time, we found that SPRED3 promotes THCA cell proliferation via the NF-κB signaling pathway. This finding may provide insight into SPRED3’s prognostic potential in thyroid cancer and provide the rationale for SPRED3-targeted druggable interventions.

The disruption of NEAT1-miR-125b-5p-SLC1A5 cascade defines the oncogenicity and differential immune profile in head and neck squamous cell carcinoma

Metabolic reprogramming sustains malignant head and neck squamous cell carcinoma (HNSCC) to overcome stressful microenvironments, and increased glutamine uptake is a common metabolic hallmark in cancers. Since metabolic reprogramming has been recognized as a new therapeutic target for tumor cells, understanding the regulatory axis of glutamine uptake in HNSCC and its potential downstream effects in its pathogenesis of HNSCC would be incredibly beneficial. Bioinformatic analysis of the Cancer Genome Atlas (TCGA)-HNSCC dataset and RNAseq analysis performed on HNSCC indicated that SLC1A5 was the most dysregulated transporter among the seven homologous glutamate or neutral amino acid transporters in the SLC1A family. To further clarify the role of SLC1A5 in HNSCC, we knocked down SLC1A5 expression. This knockdown decelerated cell growth, induced G0/G1 arrest, diminished tumorigenicity, and increased cleavage caspase3, LC3B, and intracellular Fe2+. Inhibitors against apoptosis, autophagy, or ferroptosis rescued the cell viability repressed by SLC1A5 knockdown. SLC1A5 knockdown also suppressed glutamine uptake, enhanced oxidative stress, and increased sensitivity to cisplatin. CRISPR/dCas9-mediated SLC1A5 induction conferred cisplatin resistance and reduced apoptosis, autophagy, and ferroptosis. Reporter assays and western blot data demonstrated that miR-125b-5p targets and attenuates SLC1A5, while the si-NEAT1 increases miR-125b-5p expression. Analysis of the TCGA-HNSCC databases showed concordant upregulation of NEAT1 and downregulation of miR-125b-5p, along with SLC1A5 upregulation in tumors. Analysis of transcriptomic data revealed that tumors harboring higher SLC1A5 expression had significantly lower immune scores in CD8+, monocytes, and dendritic cells, and higher scores in M0 and M1 macrophages. Disruptions in immune modulation, metabolism, and oxidative stress components were associated with SLC1A5 aberrations in HNSCC. This study concludes that the NEAT1/miR-125b-5p/SLC1A5 cascade modulates diverse activities in oncogenicity, treatment efficacy, and immune cell profiles in head and neck/oral carcinoma.

Conversion Surgery After Chemotherapy Plus Nivolumab as the First-Line Treatment for Unresectable Advanced or Recurrent Gastric Cancer and a Biomarker Study Using the Gustave Roussy Immune Score: A Multicenter Study.

There are few reports on conversion surgery (CS) after chemotherapy plus nivolumab as a first-line treatment in patients with unresectable advanced or recurrent gastric cancer (GC). This multicenter study was conducted to analyze real-world data on CS after chemotherapy plus nivolumab as a first-line treatment and to identify predictive biomarkers. This multicenter study included 104 patients who received chemotherapy plus nivolumab as primary treatment for unresectable advanced recurrent GC from 12 institutes. We investigated and analyzed patient characteristics and blood test data in the presence or absence of CS, the relationship between the Gustave Roussy Immune Score (GRIm-s) and CS, and the characteristics of CS cases. CS was performed in 12 patients (11.5%). Eastern Cooperative Oncology Group Performance Status (ECOG-PS) was significantly better in patients who underwent CS (p<0.0001). There were no CS cases with high-risk GRIm-s (0%), however there were 22 non-CS cases (23.9%). No high-risk GRIm-s cases were converted to CS. Minimally invasive surgery was performed in 50.0% of the cases, with R0 resection in all cases and only one case of urinary retention (Grade II) as a postoperative complication, indicating a good postoperative short-term outcome. There were two cases of postoperative recurrence (16.7%), both of which were grade 1b. The short-term postoperative results of CS after chemotherapy plus nivolumab as the first-line treatment for GC were acceptable in this study. There were no high-risk GRIm-s cases among those who underwent CS, suggesting that the GRIm-s may be a predictor of CS.

Comprehensive scRNA-seq analysis to identify new markers of M2 macrophages for predicting the prognosis of prostate cancer

Background Prostate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages’ prognosis value in PCa. Methods Single-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured. Results A total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa (p < 0.05). Conclusions We uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.

Development of the TP53 mutation associated hypopharyngeal squamous cell carcinoma prognostic model through bulk multi-omics sequencing and single-cell sequencing

ObjectiveThe aim of this study was to construct a prognostic model based on the TP53 mutation to calculate prognostic risk scores of patients with HPSCC. MethodsTP53 mutation and transcriptome data were downloaded from the TCGA databases. Gene expression data from GSE65858, GSE41613, GSE3292, GSE31056, GSE39366, and GSE227156 datasets were downloaded from the GEO database. GSEA, univariate, multivariate Cox analyses, and LASSO analysis were employed to identify key genes and construct the prognostic model. ROC curves were utilized to validate the OS and RFS results obtained from the model. The associations between risk scores with various clinicopathological characteristics and immune scores were analyzed via ggplot2, corrplot package, and GSVA, respectively. Single-cell sequencing data was analyzed via unbiased clustering and SingleR cell annotations. ResultsInitially, two key genes, POLD2 and POLR2G, were identified and utilized to construct the prognostic model. Samples were divided into different risk groups via the risk scores obtained from the model, with high-risk group samples exhibiting poorer prognosis. Furthermore, the risk score exhibited a positive correlation with lymphatic metastasis in patients and the immune scores of CD4+ T, CD8+ T, dendritic cell, macrophage, and neutrophil. The immune responses also exhibited notable disparities between the high- and low-risk groups. The results of single-cell sequencing analysis demonstrated that epithelial cells and macrophages were relatively abundant in HPSCC samples. POLD2 and POLR2G exhibited higher expressions in epithelial cells, with most of the identified pathways also enriched in epithelial cells. ConclusionThe prognostic model exhibited a significant capacity for predicting the prognosis of HSPCC samples based on the TP53 mutation conditions and may also predict the cancer characteristics and immune infiltration scores of samples via different risk scores obtained from the model. Level of evidenceLevel 5.

The Enhanced Role of Eosinophils in Radiomics-Based Diagnosis of Microvascular Invasion and Its Association with the Immune Microenvironment in Hepatocellular Carcinoma.

To investigate the role of eosinophil counts (EC) in microvascular invasion (MVI) for enhancing the radiomics based diagnostic model. Additionally, its correlation with early recurrence and tumor immune microenvironment was explored. Propensity score matching was employed to evaluate on 462 cases whether EC was an independent risk factor for MVI. Subgroup analyses examined EC's effect on MVI across varying hypersplenism degrees. Univariate-multivariate logistic regression identified MVI's independent factors to develop a diagnostic model. Univariate-multivariate COX regression determined early recurrence factors. Co-detection by indexing (CODEX) constructed the immune score (IS), and Spearman correlation analyzed its association with peripheral immunity. EC was an independent risk factor for MVI (p=0.038, OR=1.304 (95% CI: 1.014-1.677)), and its effect on MVI disappeared with the severity of hypersplenism. The diagnostic model with EC was significantly improved (AUC=0.787 (95% CI: 0.737-0.836) vs AUC=0.748(95% CI: 0.694-0.802, p=0.005)). MVI was an independent risk factor for early recurrence (p<0.001, HR = 2.254 (95% CI: 1.557-3.263)). IS was negatively correlated with lymphocyte counts (R=-0.311, p=0.022), and positively correlated with EC (R=0.301, p=0.027) and RS (R = 0.315, p = 0.018). EC was an independent risk factor for MVI and was related to the tumor immune microenvironment. EC should be included in the diagnosis of MVI to improve diagnostic efficiency.

548P Simplified immune score based on CD8+ T-cells at the invasive margin provides comparable prognostic value to immune scores in non-metastatic colorectal cancer

548P Simplified immune score based on CD8+ T-cells at the invasive margin provides comparable prognostic value to immune scores in non-metastatic colorectal cancer

Abstract P454: Screening of risk genes for carotid vulnerable plaque based on transcriptomics and experimental verification

Purpose and background: This study aims to conduct an in-depth analysis of human carotid atherosclerotic plaques using transcriptomic data to elucidate the relationship between hypoxia-related genes and the stability of carotid artery plaques and explore their connection with immune infiltration. Methods: Four GEO datasets (GSE21545, GSE24495, GSE159677, and GSE189300) were used. Initially, gene expression data from carotid artery plaque samples in GSE21545 and GSE24495 were analyzed. Hypoxia-related gene sets were obtained from MSigDB, and the "HALLMARK_HYPOXIA" gene set was explicitly selected. GSEA software was used for ultimately selecting CDKN1C and HAS1 as characteristic genes. Subsequently, sample clustering analysis was conducted to determin the optimal number of clusters. Gene set enrichment and differential analysis were performed. Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting experiments were conducted for preliminary validation of CDKN1C and HAS1 expression levels in ApoE knockout mice at 16 and 6 weeks. Subsequently, single-cell data from GSE159677 were processed to determine the differential localization of CDKN1C in carotid atherosclerotic plaques, with functional analysis conducted using GSE189300. Results: CDKN1C and HAS1 genes were found to exhibit differential expression in plaques and were correlated with hypoxia and immune scores. Clustering analysis revealed two main clusters, with patients in cluster A showing a higher risk of adverse events and a stronger association with immunity. Genes with a positive effect on atherosclerosis were highly expressed in poorer prognosis cluster A. Validation dataset GSE24495 also confirmed the grouping ability of CDKN1C and HAS1 genes, showing enrichment in atherosclerosis-related pathways. RT-qPCR and protein immunoblotting experiments demonstrated no significant differences in HAS1 expression levels, while CDKN1C showed lower protein expression in 16-week ApoE knockout mice compared to 6 weeks. Single-cell data from GSE159677 localized differential expression of CDKN1C in plaque cells. Finally, immunofluorescence validation confirmed the coexistence of CDKN1C and vascular smooth muscle cells. Conclusion: This study comprehensively analyzed transcriptomics data from carotid atherosclerotic plaques, revealing the significant roles of CDKN1C and HAS1 genes. Immunofluorescence validation further supported the crucial role of these genes in carotid atherosclerosis.

Comprehensive Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Transcriptional Signatures of Macrophages in Intrahepatic Cholestasis of Pregnancy.

Intrahepatic cholestasis of pregnancy (ICP) is a disorder that characterized by maternal pruritus, abnormal liver function, and an elevation in total bile acid concentrations during pregnancy. Immune factors have been recognized as playing a vital role in the mechanism of ICP. However, the underlying mechanisms regulating dysfunctional immune cells and immune genes remain to be fully elucidated. Single-cell RNA sequencing and bulk RNA sequencing data of the placenta were downloaded from the SRA database. The AUCell package, Monocle package and SCENIC package were utilized to explored immune cell activity, cell trajectory and transcription factor, respectively. GO, KEGG, and GSEA were employed to explore potential biological mechanisms. Cell-cell communications were further investigated using the CellChat package. RT-PCR, and Western blot were used to verify the gene expression in placenta. In placenta cells, macrophages were found to be significantly increased in ICP. Additionally, macrophages exhibited the highest immune gene score and were divided into four subclusters (MF1-4). Our analysis revealed significant elevations in MF2, associated with LPS response and antigen presentation, and MF4, associated with TNF and cytokine production. MF3 displayed an anti-inflammatory phenotype. MF1, closely related to ribosomes and proteins, exhibited a sharp decrease. Although ICP maintained an anti-inflammatory state, macrophage trajectories showed a gradual progression toward inflammation. Subsequently, we confirmed that cytokine- and chemokine-related signaling pathways were emphasized in macrophages. Within the CXCL signaling pathway, the increased expression of CXCL1 in macrophages can interact with CXCR2 in neutrophils, potentially inducing macrophage infiltration, stimulating neutrophil chemotaxis, and leading to an inflammatory response and cellular damage. In conclusion, we firstly revealed the transcriptional signatures of macrophages in ICP and discovered a tendency toward an inflammatory state. This study also provides new evidence that the CXCL1-CXCR2 axis may play an important role in the pathogenesis of ICP.

Exploration of Key Genes Combining with Immune Infiltration Level andTumor Mutational Burden in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a lethal malignancy due to its heterogeneity and aggressive behavior. Recently, somatic mutations and tumor cell interactions with the surrounding tumor immune microenvironment (TIME) have been reported to participate in HCC carcinogenesis and predict HCC progression. In this study, we aimed to investigate the association between tumor mutational burden (TMB) and TIME in HCC. Additionally, we sought to identify differentially expressed genes (DEGs) associated with HCC prognosis and progression. The expression, clinical, and mutational data were downloaded from the cancer genome atlas (TCGA) database. The immune infiltration levels and TMB levels of the HCC samples were estimated and the samples were divided into immune cluster (ICR)-1 and 2 based on immune infiltration score and high and low TMB groups based on TMB score. Thereafter, differential gene expression analysis was conducted to identify the DEGs in the ICR1/2 and high/low TMB groups, and the intersecting DEGs were selected. Thereafter, Cox regression analysis was performed on 89 significant DEGs, among which 19 were associated with prognosis. These 19 DEGs were then used to construct a prognostic model based on their expression levels and regression coefficients. Thereafter, we analyzed the DEGs in mutant and wildtype TP53 HCC samples and identified high BCL10 and TRAF3 expression in the mutant TP53 samples. BCL10 and TRAF3 expression was detected by real-time quantitative reverse transcription PCR and immunohistochemistry, and their clinical correlation, biological function, and immune infiltration levels were analyzed by chi-square analyses, Gene Set Enrichment Analysis (GSEA), and "ssGSEA", respectively. The results of our study revealed that immune infiltration level was correlated with TMB and that they synergistically predicted poor prognosis of HCC patients. DEGs enriched in immune-related pathways could serve as indicators of immunotherapy response in HCC. Among these DEGs, BCL10 and TRAF3 were highly expressed in HCC tissues, especially in the mutant TP53 group, and they co-operatively exhibited immunological function, thereby affecting HCC progression and prognosis. In this study, we identified BCL10 and TRAF3 as potential prognostic indicators in HCC patients. Additionally, we found that BCL10 and TRAF3 influence TMB and TIME in HCC patients and can be used for the development of immune-based therapies for improving the long-term survival of HCC patients.

Revealing the key role of cuproptosis in osteoporosis via the bioinformatic analysis and experimental validation of cuproptosis-related genes.

The incidence of osteoporosis has rapidly increased owing to the ageing population. Cuproptosis, a novel mechanism that regulates cell death, may be a new therapeutic approach. However, the relevance of cuproptosis in the immune microenvironment and osteoporosis immunotherapy is still unknown. We intersected the differentially expressed genes from osteoporotic samples with 75 cuproptosis-related genes to identify 16 significantly expressed cuproptosis genes. We further explored the connection between the cuproptosis pattern, immune microenvironment, and immunotherapy. The weighted gene co-expression network analysis algorithm was used to identify cuproptosis phenotype-associated genes, and we used quantitative real-time PCR and immunohistochemistry in mouse femur tissues to verify hub gene (MAP2K2, FDX1, COX19, VEGFA, CDKN2A, and NFE2L2) expression. Six hub genes and 59 cuproptosis phenotype-associated genes involved in immunisation were identified among the osteoporosis and control groups, and the majority of these 59 genes were enriched in the inflammatory response, as well as in signal transducers, Janus kinase, and transcription pathway activators. In addition, two different clusters of cuproptosis were found, and immune infiltration analysis showed that gene Cluster 1 had a greater immune score and immune infiltration level. Further analysis revealed that three key genes (COX19, MAP2K2, and FDX1) were highly correlated with immune cell infiltration, and external experiments validated the association of these three genes with the prognosis of osteoporosis. We used the three key mRNAs COX19, MAP2K2, and FDX1 as a classification model that may systematically elucidate the complex connection between cuproptosis and the immune microenvironment of osteoporosis. New insights into osteoporosis pathogenesis and immunotherapy prospects may be gained from this study.

628. GUSTAVE ROUSSY IMMUNE SCORE IS A PROGNOSTIC MARKER IN PATIENTS WITH ESOPHAGEAL CANCER AFTER NEOADJUVANT THERAPY: A MULTICENTER RETROSPECTIVE STUDY

Abstract Background GRIm-Score (Gustave Roussy Immune Score), a novel nutritional and inflammatory-based prognostic score, is an unfavorable prognostic factor in patients with esophageal carcinoma (EC). However, the prognostic importance of GRIm-Scores after neoadjuvant chemo-radiotherapy in these patients remains unclear. Methods A retrospective study including 432 EC patients undergoing surgical resection was performed. The GRIm-Score was calculated by lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), and albumin (ALB). The over survival (OS) and disease-free survival (DFS) were analyzed for the current study with Cox regression analyses, Kaplan-Meier methods, and propensity score matching (PSM). Results There were 374 (86.6%) men and 58 (13.4%) women with a mean age of 62.1 ± 7.7 years (range: 39-80 years). After PSM, cohorts consisted of 55 patients in the high GRIm-Score group and 55 in the low GRIm-Score group. Patient with a high GRIm-Score had poor OS (Cohort: p = 0.001; PSM: p = 0.007) and DFS (Cohort: p = 0.001; PSM: p = 0.008). The GRIm-Score, instead of NLR, LDH, or ALB in multivariate analyses, was an independent prognostic factor for OS (p = 0.019) before PSM. However, after PSM, it was a powerful independent prognostic factor for both OS (p = 0.028) and DFS (p = 0.039). Subgroup analyses showed that GRIm-Score could identify cases with worse OS or DFS among pT3-4 patients, indicating the complementary role of GRIm-Score in the decision-making of adjuvant therapy. Conclusion The GRIm-Score was an independent prognostic marker in patients with EC undergoing surgical resection after neoadjuvant therapy. Our study is also the first to discuss the prognostic value of GRIm-Score in patients with EC after neoadjuvant treatment.

Integrated machine learning survival framework to decipher diverse cell death patterns for predicting prognosis in lung adenocarcinoma.

Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and metastasis of tumors. Nevertheless, a comprehensive analysis of the diverse types of PCD in lung adenocarcinoma (LUAD) is currently lacking. The study encompassed a total of 1481 genes associated with the regulation of 13 distinct PCD patterns. Ten machine learning algorithms were amalgamated into 101 combinations, from which the optimal algorithm was chosen to formulate an artificial intelligence-derived prognostic signature based on the average C-index across four multicenter cohorts. The established optimal cell death index (CDI) model emerged as an independent risk factor for overall survival, demonstrating robust and consistent performance. Notably, CDI exhibited significantly higher accuracy compared to traditional clinical variables and molecular features. It exhibited superior performance than other published models. By integrating CDI with relevant clinical features, a nomogram with excellent predictive performance was developed. LUAD patients with low CDI score had a higher immune modulators, TIDE scores and immune scores, indicating a better immunotherapy benefit. More importantly, we found that the regulation of antigen presentation is the crucial mechanism of PCD. SCG2 is a key molecule that inhibits the malignant progression of LUAD. CDI holds great potential as a robust and promising tool for enhancing clinical outcomes in patients with LUAD.

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.

Exploring the prognosis value, immune correlation, and drug responsiveness prediction of homeobox C6 (HOXC6) in lung adenocarcinoma

BackgroundsHomeobox C6 (HOXC6) is a gene that encodes for a transcription factor involved in various cellular processes, including development and differentiation, and regulates cancer progression. However, the carcinogenesis and effect of HOXC6 in lung adenocarcinoma (LUAD) still need further investigation.MethodsThe differential HOXC6 expression levels at the mRNA and protein level were explored in multiple public datasets, including The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) dataset. Gene Expression Omnibus (GSE31210), International Cancer Genome Consortium (ICGC) datasets and the LUAD sample from Affiliated Hospital of Guangxi Medical University. We also investigated the relation between HOXC6 expression and clinicopathologic indexes. Furthermore, the correlation of immune infiltration, drug responsiveness and HOXC6 were explored.ResultsThe upregulated HOXC6 expressions at mRNA and protein levels were found in LUAD tissues compared to the normal lung tissues. Besides, the relatively shorter overall survival time, worse T and N stages, and lower immune scores were found in the high-expression HOXC6 subgroup. Notably, T cells regulatory (Tregs), Macrophages M0, and Plasma cells had the higher infiltration levels in the high-HOXC6 expression subgroup, while NK cells activated, Monocytes, Dendritic cells resting, and Mast cells resting had the lower infiltration levels. In drug sensitivity analysis, we revealed that LUAD patients with high-HOXC6 expression may be more susceptible to Camptothecin, Cytarabine, Docetaxel, Elesclomol, Rapamycin, Sorafinib, Temsirolimus, and Vorinostat.ConclusionsTaken together, there is a great potential for HOXC6 to become a prognosis biomarker and contribute to develop treatment strategies for LUAD patients. Further mechanism exploration and drug development for HOXC6 are needed.

Integrated single-cell and bulk RNA-seq analysis identifies a prognostic T-cell signature in colorectal cancer

Colorectal cancer (CRC) is a major contributor to global morbidity and mortality, necessitating more effective therapeutic approaches. T cells, prominent in the tumor microenvironment, exert a crucial role in modulating immunotherapeutic responses and clinical outcomes in CRC. This study introduces a pioneering method for characterizing the CRC immune microenvironment using single-cell sequencing data. Unlike previous approaches, which focused on individual T-cell signature genes, we utilized overall infiltration levels of colorectal cancer signature T-cells. Through weighted gene co-expression network analysis, Lasso regression, and StepCox analysis, we developed a prognostic risk model, TRGS (T-cell related genes signatures), based on six T cell-related genes. Multivariate Cox analysis identified TRGS as an independent prognostic factor for CRC, showcasing its superior predictive efficacy compared to existing immune-related prognostic models. Immunoreactivity analysis revealed higher Immunophenoscore and lower Tumor Immune Dysfunction and Exclusion scores in the low-risk group, indicating potential responsiveness to immune checkpoint inhibitor therapy. Additionally, patients in the low-risk group demonstrated heightened sensitivity to 5-fluorouracil-based chemotherapy regimens. In summary, TRGS emerges as a standalone prognostic biomarker for CRC, offering insights to optimize patient responses to immunotherapy and chemotherapy, thereby laying the groundwork for personalized tumor management strategies.

Evidence for Radiation Therapy in Stage III Locoregionally Advanced Cutaneous Melanoma in the Post-Immunotherapy Era: A Literature Review.

In the landscape of Stage III locoregionally advanced cutaneous melanoma treatment, the post-immunotherapy era has sparked a number of questions on the management of the nodal basin. However, much of the available literature is not focused on radiation therapy as an adjuvant therapy. This literature review aims to illuminate the evidence surrounding radiation therapy's potential to mitigate regional recurrences in the adjuvant setting for melanoma. Additionally, it seeks to identify adjunct systemic therapy options and explore the synergy between systemic therapy and radiation. Despite strides in surgical techniques and systemic therapies, controlling regional Stage III melanoma remains a formidable clinical hurdle. While historical data strongly suggest the efficacy of adjuvant radiation therapy in reducing regional recurrence risk, its evaluation predates the advent of MAPK pathway inhibitors and robust immunotherapy options. Notably, clinical trials have yet to definitively demonstrate a survival advantage with adjuvant radiation therapy. Additional research should focus on refining the definition of high risk for regional recurrence through gene expression profiling or tumor immune profiling scores and elucidate the optimal role of adjuvant radiation therapy in patients treated with neoadjuvant systemic therapy.

The construction and experimental verification of a 6-LncRNA model based on Lactic acid metabolism in the tumor microenvironment of Wilms tumor

BackgroundLactic acid (LA) can promote the malignant progression of tumors through the crosstalk with the tumor microenvironment (TME). However, the function of long non-coding RNAs (lncRNAs) related to LA metabolism in Wilms tumor (WT) remains unclear. MethodsGene expression data and clinical data of WT patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Through the ESTIMATE algorithm and Pearson correlation analysis, lncRNAs related to tumor immunity and LA metabolism were screened. Subsequently, Cox regression analysis and Lasso Cox regression analysis were used to construct a model. Furthermore, candidate genes were identified and a competitive endogenous RNA (ceRNA) network was conducted to explore the specific mechanism of characteristic genes. Finally, based on the strong clinical relevance of UNC5B-AS1, its expression and function were experimentally verified. ResultsThe immune score and stromal score were found to be closely related to the prognosis of WT. Eventually, a prognostic model (TME-LA-LM) consisting of 6 lncRNAs was successfully identified. The model demonstrated favorable predictive ability and accuracy, with significant variation in immune infiltration and drug susceptibility observed between risk groups. Additionally, the study revealed the involvement of 2 candidate genes and 5 microRNAs (miRNAs) in the tumor’s development. Notably, UNC5B-AS1 was highly expressed and found to promote the proliferation and migration of tumor cells. ConclusionThis study, for the first time, elucidated the prognostic signatures of WT using lncRNAs related to TME and LA metabolism. The fundings of this research offer valuable insights for future studies on immunotherapy, personalized chemotherapy and mechanism research.

Development and Validation of a Comprehensive Analysis of the Competing Endogenous circRNA/miRNA/mRNA Network for the Identification of Immune-Related Targets in Esophageal Squamous Cell Carcinoma.

Immunotherapy for esophageal squamous cell carcinoma (ESCC) exhibits notable variability in efficacy. Concurrently, recent research emphasizes circRNAs' impact on the ESCC tumor microenvironment. To further explore the relationship, we leveraged circRNA, microRNA, and mRNA sequence datasets to construct a comprehensive immune-related circRNA-microRNA-mRNA network, revealing competing endogenous RNA (ceRNA) roles in ESCC. The network comprises 16 circular RNAs, 13 microRNAs, and 1,560 mRNAs. Weighted gene co-expression analysis identified immune-related modules, notably cancer-associated fibroblast (CAF) and myeloid-derived suppressor cell modules, correlating significantly with immune and stemness scores. Among them, the CAF module plays a crucial role in extracellular matrix function and effectively discriminates ESCC patients. Four hub collagen family genes within CAF correlated robustly with CAF, macrophage infiltration, and T-cell exclusion. In-house sequencing and RT-qPCR validated their elevated expression. We also identified CAF module-targeting drugs as potential ESCC treatments. In summary, we established an immune-related circRNA-miRNA-mRNA network that not only illuminates ceRNA functionality but also highlights circRNAs' involvement in the CAF through collagen gene targeting. These findings hold promise to predict ESCC immune landscapes and therapy responses, ultimately aiding in more personalized and effective clinical decision-making.