Multiple developmental pathways lead to the generation of CD4 T-cell memory

Unraveling disulfidptosis for prognostic modeling and personalized treatment strategies in lung adenocarcinoma

AIM

To construct and identify a prognostic and therapeutic signature based on disulfidptosis-related genes in lung adenocarcinoma.

METHODS

Bioinformatic analysis was performed to assess the differential expression of disulfidptosis-related genes between cancerous and control samples from The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) database. Survival analysis, immune cell infiltration assessment, and examination of oncogenic pathways were performed to uncover potential clinical implications of disulfidptosis gene expression. Differential gene expression analysis between subtypes facilitated the development of a prognostic model using a combination of genes associated with survival. A nomogram was further created using independent clinical and molecular factors.

RESULTS

We identified the significant upregulation of ten disulfidptosis-related genes and delineated two distinct subtypes, C1 and C2. Subtype C2 was associated with prolonged survival. Then, prognostic modeling utilizing six genes (TXNRD1, CPS1, S100P, SCGB3A1, CYP24A1, NAPSA) demonstrated predictive power in both training and validation datasets. The nomogram, incorporating the risk model with clinical features, provided a reliable tool for predicting one-year (AUC 0.77), three-year (AUC 0.75), and five-year (AUC 0.78) survival rates. Additionally, chemotherapy sensitivity analysis highlighted significant resistance in the high-risk group, primarily associated with subtype C1.

CONCLUSION

Our study reveals distinct LUAD subtypes, offers a robust prognostic model, and underscores clinical implications for personalized therapy based on disulfidptosis-related genes expression profiles.

Interferon regulatory factor 4 plays a pivotal role in the development of aGVHD-associated colitis

Interferon regulatory factor 4 (IRF4) is a master transcription factor that regulates T helper cell (Th) differentiation. It interacts with the Basic leucine zipper transcription factor, ATF-like (BATF), depletion of which in CD4+ T cells abrogates acute graft-versus-host disease (aGVHD)-induced colitis. Here, we investigated the immune-regulatory role of Irf4 in a mouse model of MHC-mismatched bone marrow transplantation. We found that recipients of allogenic Irf4-/- CD4+ T cells developed less GVHD-related symptoms. Transcriptome analysis of re-isolated donor Irf4-/- CD4+ T helper (Th) cells, revealed gene expression profiles consistent with loss of effector T helper cell signatures and enrichment of a regulatory T cell (Treg) gene expression signature. In line with these findings, we observed a high expression of the transcription factor BTB and CNC homolog 2; (BACH2) in Irf4-/- T cells, which is associated with the formation of Treg cells and suppression of Th subset differentiation. We also found an association between BACH2 expression and Treg differentiation in patients with intestinal GVHD. Finally, our results indicate that IRF4 and BACH2 act as counterparts in Th cell polarization and immune homeostasis during GVHD. In conclusion, targeting the BACH2/IRF4-axis could help to develop novel therapeutic approaches against GVHD.

Human endogenous retroviruses of the HERV-K (HML-2) family are expressed in the brain of healthy individuals and modify the composition of the brain-infiltrating immune cells

Human endogenous retroviruses (HERVs) are remnants of ancient retroviral infections in the human genome. RNA expression of individual HERVs has frequently been observed in various pathologic conditions, but some activity can also be seen in healthy individuals, e.g. in the blood. To quantitate the basal expression levels of HERVs in the brain, we now used high-throughput sequencing-based metagenomic analysis to characterize the expression profiles of the HERV-K (HML-2) family proviruses in different brain regions of healthy brain tissue. To this end, RNA-seq data from the Genotype-Tissue Expression (GTEx) project was used. The GTEx project is a public resource to study tissue-specific gene expression and regulation, consisting of a large selection of sequenced samples from different tissues. The GTEx data used in this study consisted of 378 samples taken from 13 brain regions from 55 individuals. The data demonstrated that out of 99 intact proviruses in the family 58 were expressed, but the expression profiles were highly divergent and there were no significant differences in the expression profiles between the various anatomic regions of the brain. It is known that the brain contains a variety of infiltrating immune cells, which are probably of great importance both in the normal defense mechanisms as well as in the various pathogenic processes. Digital cytometry (CIBERSORTx) was used to quantify the proportions of the infiltrating immune cells in the same brain samples. Six most abundant (>5 % of the total population) cell types were observed to be CD4 memory resting T cells, M0 macrophages, plasma cells, CD8 T cells, CD4 memory activated T cells, and monocytes. Analysis of the correlations between the individual HERVs and infiltrating cell types indicated that a cluster of 6 HERVs had a notable correlation signature between T cell type infiltrating cell proportions and HERV RNA expression intensity. The correlations between inflammatory type infiltrating cells were negative or weak. Taken together, these data indicate that the expression of HERVs is associated with a T cell type immunity.

Multi-omics identification of an immunogenic cell death-related signature for clear cell renal cell carcinoma in the context of 3P medicine and based on a 101-combination machine learning computational framework

Background

Clear cell renal cell carcinoma (ccRCC) is a prevalent urological malignancy associated with a high mortality rate. The lack of a reliable prognostic biomarker undermines the efficacy of its predictive, preventive, and personalized medicine (PPPM/3PM) approach. Immunogenic cell death (ICD) is a specific type of programmed cell death that is tightly associated with anti-cancer immunity. However, the role of ICD in ccRCC remains unclear.

Methods

Based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network (WGCNA) analyses, ICD-related genes were screened at both the single-cell and bulk transcriptome levels. We developed a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a consensus immunogenic cell death-related signature (ICDRS). ICDRS was evaluated in the training, internal validation, and external validation sets. An ICDRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Multi-omics analysis was performed, including genome, single-cell transcriptome, and bulk transcriptome, to gain a more comprehensive understanding of the prognosis signature. We evaluated the response of risk subgroups to immunotherapy and screened drugs that target specific risk subgroups for personalized medicine. Finally, the expression of ICD-related genes was validated by qRT-PCR.

Results

We identified 131 ICD-related genes at both the single-cell and bulk transcriptome levels, of which 39 were associated with overall survival (OS). A consensus ICDRS was constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. ICDRS can also be used to predict the occurrence, development, and metastasis of ccRCC. Multivariate analysis verified it as an independent prognostic factor for OS, progression-free survival (PFS), and disease-specific survival (DSS) of ccRCC. The ICDRS-integrated nomogram provided a quantitative tool in clinical practice. Moreover, we observed distinct biological functions, mutation landscapes, and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. Notably, the immunophenoscore (IPS) score showed a significant difference between risk subgroups, suggesting a better response to immunotherapy in the high-risk group. Potential drugs targeting specific risk subgroups were also identified.

Conclusion

Our study constructed an immunogenic cell death-related signature that can serve as a promising tool for prognosis prediction, targeted prevention, and personalized medicine in ccRCC. Incorporating ICD into the PPPM framework will provide a unique opportunity for clinical intelligence and new management approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00327-3.

Upregulation of TUBG1 expression promotes hepatocellular carcinoma development

Tubulin γ-1 (TUBG1) is a highly conserved component of the centrosome and its deregulation is involved in the development of several types of cancer. However, the role of TUBG1 in hepatocellular carcinoma (HCC) remains unclear. In this study, we found that TUBG1 was upregulated in human HCC cells and tissues and that TUBG1 upregulation was associated with promoter hypomethylation in HCC tissues. TUBG1 knockdown suppressed the proliferation, invasion, and migration of HCC cells. While TUBG1 expression was positively correlated with CD4 + memory T lymphocyte infiltration, it was negatively correlated with CD4 + regulatory T-cell infiltration in human HCC tissues. Furthermore, TUBG1 expression was positively correlated with the expression of genes involved in cell division. Noticeably, high expression of TUBG1 was associated with poor prognosis in patients with HCC. Overall, our findings revealed that TUBG1 promotes hepatocarcinogenesis by increasing proliferation, invasion, and migration of HCC cells and may regulate T lymphocyte infiltration. The current findings provide important insights into TUBG1 regulation in HCC, which could provide new therapeutic targets for hepatocarcinoma which has a very high incidence and mortality rate worldwide.

Cellular senescence-related long noncoding ribonucleic acids: Predicting prognosis in hepatocellular carcinoma

BACKGROUND

Due to their inherent role in cell function, long non-coding ribonucleic acids (lncRNAs) mediate changes in the microenvironment, and thereby participate in the development of cellular senescence.

AIMS

This study aimed to identify cellular senescence-related lncRNAs that could predict the prognosis of liver cancer.

METHODS AND RESULTS

Gene expression and clinical data were downloaded from the UCSC Xena platform, ICGC, and TCGA databases. Cox regression and LASSO regression were used to establish a cellular senescence-related lncRNA model. ROC curves and Kaplan-Meier survival curves were then constructed to predict patient prognosis. Cox regression analysis and clinical characteristics were used to evaluate the capability of the model. Tumor mutational burden and tumor-infiltrating immune cell analyses were subsequently performed in the risk subgroups and the samples in the entire cohort were reclustered. Finally, potential small molecule immune-targeted drugs were identified based on the model. The cellular senescence-related prognostic model that was constructed based on AGAP11 and FAM182B. Along with the results of Cox regression and Lasso regression, the risk score was found to be an independent factor for predicting overall survival in cohorts. In the subgroup analysis, the prognosis of the low-risk group in each cohort was significantly higher than that of the high-risk group; the area under temporal ROC curves and clinical ROC curves were all greater than 0.65, respectively. C-index shows that the risk scores are greater than 0.6, showing the stability of the model. The high-risk group demonstrated lower tumor microenvironment and higher tumor mutational burden scores, further verifying the reliability of the model grouping results. Analysis of tumor-infiltrating immune cells indicated that CD8+ and γδ T cells were more abundant among patients in the low-risk group; cluster reorganization indicated that the two groups had different prognoses and proportions of immune cells. The p value of potential drugs predicted based on the expression of model lncRNAs were all less than .05, demonstrating the potential of model lncRNAs as therapeutic targets to some extent.

CONCLUSION

A prognostic model based on cellular senescence-associated lncRNAs was established and this may be used as a potential biomarker for the prognosis assessment of liver cancer patients.

Composition of the infiltrating immune cells in the brain of healthy individuals: effect of aging

Immune cells infiltrating the central nervous system (CNS) are involved in the defense against invading microbes as well as in the pathogenesis of neuroinflammatory diseases. In these conditions, the presence of several types of immune and inflammatory cells have been demonstrated. However, some studies have also reported low amounts of immune cells that have been detected in the CNS of healthy individuals, but the cell types present have not been systematically analyzed. To do this, we now used brain samples from The Genotype- Tissue Expression (GTEx) project to analyze the relative abundance of 22 infiltrating leukocyte types using a digital cytometry tool (CIBERSORTx). To characterize cell proportions in different parts of the CNS, samples from 13 different anatomic brain regions were used. The data obtained demonstrated that several leukocyte types were present in the CNS. Six leukocyte types (CD4 memory resting T cells, M0 macrophages, plasma cells, CD8 T cells, CD4 memory activated T cells, and monocytes) were present with a proportion higher than 0.05, i.e. 5%. These six cell types were present in most brain regions with only insignificant variation. A consistent association with age was seen with monocytes, CD8 T cells, and follicular helper T cells. Taken together, these data show that several infiltrating immune cell types are present in the non-diseased CNS tissue and that the proportions of infiltrating cells are affected by age in a manner that is consistent with literature on immunosenecence and inflammaging.

Lower frequency of T stem cell memory (TSCM) cells in hepatitis B vaccine nonresponders

Despite the availability of an effective vaccine and antiviral treatments, hepatitis B is still a global public health problem. Hepatitis B vaccination can prevent the disease. Vaccination induces long-lasting protective immune memory, and the identification of memory cell subsets can indicate the effectiveness of vaccines. Here, we compared the frequency of CD4⁺ memory T cell subsets between responders and nonresponders to HB vaccination. Besides, the frequency of IFN-γ⁺ memory T cells was compared between studied groups. Study participants were grouped according to their anti-HBsAb titer. For restimulation of CD4⁺ memory T cells, peripheral blood mononuclear cells (PBMCs) were cultured in the presence of HBsAg and PHA for 48 h. Besides, PMA, ionomycin, and brefeldin were added during the last 5 h of incubation to induce IFN-γ production. Flow cytometry was used for analysis. There was a statistically significant difference in the frequency of CD4⁺CD95⁺, CD4⁺CD95^Hi, and CD4⁺CD95^low/med T stem cell memory (T_SCM) cells between responder and nonresponder groups. However, the comparison of the frequency of memory T cells producing IFN-γ showed no differences. Our results identified a possible defect of immunological CD4⁺ memory T cell formation in nonresponders due to their lower frequency of CD4⁺ T_SCM cells.