Elucidating the role of T-cell exhaustion-related genes in colorectal cancer: a single-cell bioinformatics perspective

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.

Unveiling NRlncRNAs as prognostic biomarkers and therapeutic targets for head-and-neck squamous cell carcinoma through machine learning

Head-and-neck squamous cell carcinoma (HNSCC) patients often exhibit insensitivity to immunotherapy, leading to treatment failure. Identifying potential biomarkers that can predict prognosis and improve the efficacy of treatment is crucial. In this study, we aimed to identify necroptosis-related long noncoding RNAs (NRlncRNAs) as potential therapeutic targets to improve the prognosis of HNSCC patients. By exploring the Genotype-Tissue Expression Project (GTEx) and the Cancer Genome Atlas (TCGA) databases, we identified NRlncRNAs and developed a risk model comprising 17 NRlncRNAs to predict the prognosis of HNSCC patients and to classify patients into two clusters based on their expression levels. We conducted various analyses, such as the Kaplan-Meier analysis, GSEA and IC50 prediction, to evaluate the differences in sensitivity to immunotherapy between the two clusters. Our findings suggest that NRlncRNAs have potential as therapeutic targets for improving the prognosis of HNSCC patients, and that individualized treatment approaches based on NRlncRNA expression levels can improve the sensitivity of immunotherapy and overall treatment outcomes. This study highlights new perspectives within clinical cancer informatics and provides insight into potential therapeutic strategies for HNSCC patients.

Novel disulfidptosis-derived gene blueprint stratifying patients with breast cancer

INTRODUCTION

Breast cancer remains the predominant cancer among females, accounting for about 24.2% of all cancer cases. Alarmingly, it is the primary cause of cancer-related mortality in women under 45.

METHODS

This research analyzed RNA sequencing data from 1082 TCGA-BRCA and 107 GSE58812 breast cancer patients. Single-cell RNA data from five patients in the GSE118389 data set were also studied. Using Random forest and COX regression, we developed a prognostic model. Pathway analysis employed GSVA and GO, while immune profiles were assessed via ssGSEA and MCPcounter. Mutation patterns utilized maftools, and drug sensitivity scores were derived from the GDSC database with oncoPredict.

RESULTS

Analysis of the GSE118389 data set identified three distinct cell types: immune, epithelial, and stromal. P53 and VEGF were notably enriched. Five key genes (TMEM251, ADAMTSL2, CDC123, PSMD1, TLE1) were pinpointed for their prognostic significance. We introduced a disulfidptosis-associated score as a novel risk factor for breast cancer prognosis. Survival outcomes varied significantly between training and validation sets. Comprehensive immune profiling revealed no difference in activated CD8-positive T cells between risk groups, but a positive correlation of NK cells, neutrophils, cytotoxic lymphocytes, and monocytic cells with the riskscore was noted. Importantly, a negative association between the drug Nelarabine and riskscore was identified.

CONCLUSION

This research underscores the significance of a disulfidptosis-associated gene signature in breast cancer prognosis.

Genomic and immune landscape in hepatocellular carcinoma: Implications for personalized therapeutics

Hepatocellular carcinoma (HCC) is a globally prevalent malignancy, marked by genetic heterogeneity and intricate tumor microenvironment interactions. In this study, we undertook a detailed single-cell analysis of six active HCC patients, highlighting strong correlations between gene expression levels and cellular characteristics. UMAP clustering revealed seven distinct cell categories with associated gene expressions. A divergence was observed in tumor cells into high and low cuproptosis groups, each associated with distinct pathways: oxidative stress for the high cuproptosis group and inflammatory and angiogenesis pathways for the low group. CellChat analysis on the TCGA-LIHC cohort displayed unique intercellular interactions among hepatocytes, T cells, and other cells, with pathways like COLLAGEN and VEGF being pivotal. Functional enrichment analyses exposed pathways enriched between cuproptosis groups, with KEGG emphasizing diseases like Parkinson's. COX survival analysis identified key prognostic genes, revealing distinct survival rates between risk groups in TCGA and GSE14520 cohorts. Mutation data highlighted missense mutations, with TTN, TP53, and CTNNB1 being the most mutated in HCC. Immune infiltration analysis via CIBERSORTx indicated differences between risk groups in NK cells, neutrophils, and other cells. Our drug sensitivity investigation showed significant correlations between model genes and drug responsiveness, emphasizing the importance of patient risk stratification for therapeutic approaches. Further, ATP6V1G1 was recognized in its role in apoptosis and migration in HCC cells. In conclusion, our findings illuminate the complexities of HCC progression, potential predictive genetic markers for drug response, and the pivotal role of ATP6V1G1, suggesting avenues for targeted therapeutic strategies in HCC.

Exploring the role of lactylation-related genes in osteosarcoma: A deep dive into prognostic significance and therapeutic potential

Osteosarcoma (OS), notorious for its complex pathogenesis and formidable prognosis, represents a significant medical quandary. This research embarked on a quest to unravel the implications of lactylation-related genes (LRGs) in OS, offering a novel lens through which to interpret its intricacies. A meticulous evaluation of 329 LRGs within the TARGET dataset spotlighted 27 paramount genes, intricately intertwined with survival. These genes highlighted metabolic processes-particularly amino acid metabolism-as key players, as evidenced in both GO and KEGG analyses. Utilizing consensus clustering and principal component analysis, the 93 OS samples were segmented into two distinct groups, differing notably in overall and event-free survival. Cluster 2 demonstrated a heightened immune response, contrasting the other cluster. Machine learning techniques, like generalized boosted model, CoxBoost, and RSF, spotlighted MYC and GOT2 as critical genes. Using multivariate Cox regression, a risk model was developed, categorizing patients into high and low-risk groups, each displaying varied survival patterns. Additionally, a contrast was observed between MYC and GOT2's associations with HLA molecules, emphasizing their distinct roles in antigen presentation. Potential therapeutic avenues were identified for each risk group, with special attention to mutations in MYC, particularly amplifications, hinting at its role in tumor progression. Finally, delving deeper into the role of MYC, Western blot analyses exhibited amplified myc protein levels in OS cells U-2 and MG-63 when juxtaposed against human osteoblastic cells Hfob1.19. A focused knockdown of myc in OS cells subsequently confirmed its influence on cell proliferation and migration, with reduced myc expression resulting in inhibited cell activities. Furthermore, immunofluorescence assays corroborated myc's heightened expression in OS cells relative to normal osteoblastic cells. In summary, this study accentuates the vital role of LRGs and specifically MYC in OS, ushering in a horizon of tailored therapeutic strategies.

Integration of single-nuclei RNA-sequencing, spatial transcriptomics and histochemistry defines the complex microenvironment of NF1-associated plexiform neurofibromas

Plexiform neurofibroma (PN) is a leading cause of morbidity in children with the genetic condition Neurofibromatosis Type 1 (NF1), often disfiguring or threatening vital structures. During formation of PN, a complex tumor microenvironment (TME) develops, with recruitment of neoplastic and non-neoplastic cell types being critical for growth and progression. Due to the cohesive cellularity of PN, single-cell RNA-sequencing is difficult and may result in a loss of detection of critical cellular subpopulations. To bypass this barrier, we performed single-nuclei RNA-sequencing (snRNA-seq) on 8 frozen PN samples, and integrated this with spatial transcriptomics (ST) in 4 PN samples and immunohistochemistry to provide morphological context to transcriptomic data. SnRNA-seq analysis definitively charted the heterogeneous cellular subpopulations in the PN TME, with the predominant fraction being fibroblast subtypes. PN showed a remarkable amount of inter-sample homogeneity regarding cellular subpopulation proportions despite being resected from a variety of anatomical locations. ST analysis identified distinct cellular subpopulations which were annotated using snRNA-seq data and correlated with histological features. Schwann cell/fibroblast interactions were identified by receptor/ligand interaction analysis demonstrating a high probability of Neurexin 1/Neuroligin 1 (NRXN1/NLGN1) receptor-ligand cross-talk predicted between fibroblasts and non-myelinated Schwann cells (NM-SC) and subtypes, respectively. We observed aberrant expression of NRXN1 and NLGN1 in our PN snRNA-seq data compared to a normal mouse sciatic nerve single-cell RNA-seq dataset. This pathway has never been described in PN and may indicate a clear and direct communication pathway between putative NM-SC cells of origin and surrounding fibroblasts, potentially driving disease progression. SnRNA-seq integrated with spatial transcriptomics advances our understanding of the complex cellular heterogeneity of PN TME and identify potential novel communication pathways that may drive disease progression, a finding that could provide translational therapy options for patients with these devastating tumors of childhood and early adulthood.

Role of exosome-mediated molecules SNORD91A and SLC40A1 in M2 macrophage polarization and prognosis of ESCC

BACKGROUND

Exosome-mediated interaction serves as a significant regulatory factor for M2 macrophage polarization in cancer.

METHODS

All accessible data were acquired from The Cancer Genome Atlas (TCGA) database and analyzed using R software. Molecules implicated in exocrine secretion were amassed from the ExoCarta database. Our research initially quantified the immune microenvironment in Esophageal Squamous Cell Carcinoma (ESCC) patients based on the expression profile sourced from the TCGA database. Additionally, we delved into the biological role of M2 macrophages in ESCC via Gene Set Enrichment Analysis (GSEA).

RESULTS

We observed that patients with high M2 macrophage infiltration typically have a poorer prognosis. Subsequently, a total of 1457 molecules were identified, with 103 of these molecules believed to function through exocrine mechanisms, as supported by data from the ExoCarta database. SNORD91A and SLC40A1 were ultimately pinpointed due to their correlation with patient prognosis. Moreover, we investigated their potential roles in ESCC, including biological enrichment, immune infiltration, and genomic instability analysis.

CONCLUSIONS

Our study identified exosome-associated molecules, namely SNORD91A and SLC40A1, which notably impact ESCC prognosis and local M2 macrophage recruitment, thereby presenting potential therapeutic targets for ESCC.

Comprehensive analysis of Cuproplasia and immune microenvironment in lung adenocarcinoma

Background: Trace elements such as copper are essential for human health. Recently the journal Nat Rev Cancer has put forward the concept of Cuproplasia, a way of promoting tumor growth through reliance on copper. We attempted to conduct a comprehensive analysis of Cuproplasia-related genes in lung adenocarcinoma (LUAD) to explore the mechanism of action of Cuproplasia-related genes in LUAD. Method: Transcriptome data and clinical information of LUAD were obtained from TCGA-LUAD and GSE31210, and prognostic models of Cuproplasia-related genes were constructed and verified by regression analysis of GSVA, WGCNA, univariate COX and lasso. The signal pathways affected by Cuproplasia-related genes were analyzed by GO, KEGG and hallmarK pathway enrichment methods. Five immunocell infiltration algorithms and IMVIGOR210 data were used to analyze immune cell content and immunotherapy outcomes in the high-low risk group. Results: In the results of WGCNA, BROWN and TURQUOISE were identified as modules closely related to Cuproplasia score. In the end, lasso regression analysis established a Cuproplasia-related signature (CRS) based on 24 genes, and the prognosis of high-risk populations was worse in TCGA-LUAD and GSE31210 datasets. The enrichment analysis showed that copper proliferation was mainly through chromosome, cell cycle, dna replication, g2m checkpoint and other pathways. Immunoinfiltration analysis showed that there were differences in the content of macrophages among the four algorithms. And IMVIGOR210 found that the lower the score, the more effective the immunotherapy was. Conclusion: The Cuproplasia related gene can be used to predict the prognosis and immunotherapy outcome of LUAD patients, and may exert its effect by affecting chromosome-related pathways and macrophages.

Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic-associated molecular subtypes and genomic mutations

Objective: Due to a lack of effective therapy, triple-negative breast cancer (TNBC) is extremely poor prognosis. Metabolic reprogramming is an important hallmark in tumorigenesis, cancer diagnosis, prognosis, and treatment. Categorizing metabolic patterns in TNBC is critical to combat heterogeneity and targeted therapeutics. Methods: 115 TNBC patients from TCGA were combined into a virtual cohort and verified by other verification sets, discovering differentially expressed genes (DEGs). To identify reliable metabolic features, we applied the same procedures to five independent datasets to verify the identified TNBC subtypes, which differed in terms of prognosis, metabolic characteristics, immune infiltration, clinical features, somatic mutation, and drug sensitivity. Results: In general, TNBC could be classified into two metabolically distinct subtypes. C1 had high immune checkpoint genes expression and immune and stromal scores, demonstrating sensitivity to the treatment of PD-1 inhibitors. On the other hand, C2 displayed a high variation in metabolism pathways involved in carbohydrate, lipid, and amino acid metabolism. More importantly, C2 was a lack of immune signatures, with late pathological stage, low immune infiltration and poor prognosis. Interestingly, C2 had a high mutation frequency in PIK3CA, KMT2D, and KMT2C and displayed significant activation of the PI3K and angiogenesis pathways. As a final output, we created a 100-gene classifier to reliably differentiate the TNBC subtypes and AKR1B10 was a potential biomarker for C2 subtypes. Conclusion: In conclusion, we identified two subtypes with distinct metabolic phenotypes, provided novel insights into TNBC heterogeneity, and provided a theoretical foundation for therapeutic strategies.