Construction of a 5-methylcytosine-Related Molecular Signature to Inform the Prognosis and Immunotherapy of Lung Squamous Cell Carcinoma

Epigenetic regulation in cancer

Epigenetic modifications are defined as heritable changes in gene activity that do not involve changes in the underlying DNA sequence. The oncogenic process is driven by the accumulation of alterations that impact genome's structure and function. Genetic mutations, which directly disrupt the DNA sequence, are complemented by epigenetic modifications that modulate gene expression, thereby facilitating the acquisition of malignant characteristics. Principals among these epigenetic changes are shifts in DNA methylation and histone mark patterns, which promote tumor development and metastasis. Notably, the reversible nature of epigenetic alterations, as opposed to the permanence of genetic changes, positions the epigenetic machinery as a prime target in the discovery of novel therapeutics. Our review delves into the complexities of epigenetic regulation, exploring its profound effects on tumor initiation, metastatic behavior, metabolic pathways, and the tumor microenvironment. We place a particular emphasis on the dysregulation at each level of epigenetic modulation, including but not limited to, the aberrations in enzymes responsible for DNA methylation and histone modification, subunit loss or fusions in chromatin remodeling complexes, and the disturbances in higher-order chromatin structure. Finally, we also evaluate therapeutic approaches that leverage the growing understanding of chromatin dysregulation, offering new avenues for cancer treatment.

Plasma Cell-Free Tumor Methylome as a Biomarker in Solid Tumors: Biology and Applications

DNA methylation is a fundamental mechanism of epigenetic control in cells and its dysregulation is strongly implicated in cancer development. Cancers possess an extensively hypomethylated genome with focal regions of hypermethylation at CPG islands. Due to the highly conserved nature of cancer-specific methylation, its detection in cell-free DNA in plasma using liquid biopsies constitutes an area of interest in biomarker research. The advent of next-generation sequencing and newer computational technologies have allowed for the development of diagnostic and prognostic biomarkers that utilize methylation profiling to diagnose disease and stratify risk. Methylome-based predictive biomarkers can determine the response to anti-cancer therapy. An additional emerging application of these biomarkers is in minimal residual disease monitoring. Several key challenges need to be addressed before cfDNA-based methylation biomarkers become fully integrated into practice. The first relates to the biology and stability of cfDNA. The second concerns the clinical validity and generalizability of methylation-based assays, many of which are cancer type-specific. The third involves their practicability, which is a stumbling block for translating technologies from bench to clinic. Future work on developing pan-cancer assays with their respective validities confirmed using well-designed, prospective clinical trials is crucial in pushing for the greater use of these tools in oncology.

Prediction of lymph node metastasis of lung squamous cell carcinoma by machine learning algorithm classifiers

BACKGROUND

Lymph node metastasis (LNM) is an essential factor affecting the prognosis of patients with lung squamous cell carcinoma (LUSC), as well as a critical consideration for the choice of treatment strategy. Exploring effective methods for predicting LNM in LUSC may benefit clinical decision making.

MATERIALS AND METHODS

We used data collected from the Surveillance, Epidemiology, and End Results (SEER) database to develop machine learning algorithm classifiers, including boosted trees (BTs), based on the primary clinical parameters of patients to predict LNM in LUSC. Training on a large-sample training cohort (n = 8,063) allowed for the construction of several concise classifiers for LNM prediction in LUSC, which were then validated using test and in-house cohorts (n = 2,017 and 57, respectively).

RESULTS

The six classifiers established in this research enabled distinction between patients with and without LNM. Among these classifiers, the BT classifier was the top performer, with accuracy, F1 scores, precision, recall, sensitivity, and specificity values of 0.654, 0.621, 0.654, 0.592, 0.592, and 0.711, respectively. The precision recall (PR) and receiver operating characteristic (ROC) (with area under the curve = 0.714) curves also supported this result, which was validated by the in-house cohort. Notably, the tumor stage was a critical factor in determining LNM in patients with LUSC.

CONCLUSIONS

The use of classifiers, especially the BT classifier, may serve as a useful tool for improving clinical precision and individualized treatment of patients with LUSC.

Identifying the cancer-associated fibroblast signature to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma

Cancer-associated fibroblasts (CAFs) are an important component of the tumor microenvironment that contribute toward the development of tumors. This study aimed to establish a new algorithm based on CAF scores to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma (LUSC). The RNA-seq data of LUSC patients were obtained from two databases and merged after removing inter-batch differences. The CAF-related data for each sample were obtained through three different algorithms. Consistency cluster analysis was performed to obtain different CAF clusters, which were analyzed to identify differentially expressed genes. These were subjected to uniform cluster analysis to obtain different gene clusters. The Boruta algorithm was used to calculate the CAF score. Three CAF clusters and two gene clusters were obtained, all of which differed in their patient prognoses and the content of infiltrating immune cells. Patients with high CAF scores exhibited worse overall survival, higher expression of biomarkers related to immune checkpoints and immune activity, and lower tumor mutation burden. The CAF score could also predict the immunotherapy response of patients. This study suggests that the CAF score can accurately predict the prognosis and immunotherapy response of LUSC patients.

Genomic analysis of immunogenic cell death-related subtypes for predicting prognosis and immunotherapy outcomes in glioblastoma multiforme

Immunogenic cell death (ICD), a unique form of cancer cell death, has therapeutic potential in anti-tumour immunotherapy. The aim of this study is to explore the predictive potential of ICD in the prognosis and immunotherapy outcomes of glioblastoma multiforme (GBM). RNA sequencing data and clinical information were downloaded from three databases. Unsupervised consistency clustering analysis was used to identify ICD-related clusters and gene clusters. Additionally, the ICD scores were determined using principal component analysis and the Boruta algorithm via dimensionality reduction techniques. Subsequently, three ICD-related clusters and three gene clusters with different prognoses were identified, with differences in specific tumour immune infiltration-related lymphocytes in these clusters. Moreover, the ICD score was well differentiated among patients with GBM, and the ICD score was considered an independent prognostic factor for patients with GBM. Furthermore, two datasets were used for the external validation of ICD scores as predictors of prognosis and immunotherapy outcomes. The validation analysis suggested that patients with high ICD scores had a worse prognosis. Additionally, a higher proportion of patients with high ICD scores were non-responsive to immunotherapy. Thus, the ICD score has the potential as a biomarker to predict the prognosis and immunotherapy outcomes of patients with GBM.

Epigenetic regulation in the tumor microenvironment: molecular mechanisms and therapeutic targets

Over decades, researchers have focused on the epigenetic control of DNA-templated processes. Histone modification, DNA methylation, chromatin remodeling, RNA modification, and noncoding RNAs modulate many biological processes that are crucial to the development of cancers. Dysregulation of the epigenome drives aberrant transcriptional programs. A growing body of evidence suggests that the mechanisms of epigenetic modification are dysregulated in human cancers and might be excellent targets for tumor treatment. Epigenetics has also been shown to influence tumor immunogenicity and immune cells involved in antitumor responses. Thus, the development and application of epigenetic therapy and cancer immunotherapy and their combinations may have important implications for cancer treatment. Here, we present an up-to-date and thorough description of how epigenetic modifications in tumor cells influence immune cell responses in the tumor microenvironment (TME) and how epigenetics influence immune cells internally to modify the TME. Additionally, we highlight the therapeutic potential of targeting epigenetic regulators for cancer immunotherapy. Harnessing the complex interplay between epigenetics and cancer immunology to develop therapeutics that combine thereof is challenging but could yield significant benefits. The purpose of this review is to assist researchers in understanding how epigenetics impact immune responses in the TME, so that better cancer immunotherapies can be developed.

Bioinformatics construction and experimental validation of a cuproptosis-related lncRNA prognostic model in lung adenocarcinoma for immunotherapy response prediction

Cuproptosis is a newly form of cell death. Cuproptosis related lncRNA in lung adenocarcinoma (LUAD) has also not been fully elucidated. In the present study, we aimed to construct a prognostic signature based on cuproptosis-related lncRNA in LUAD and investigate its association with immunotherapy response. The RNA-sequencing data, clinical information and simple nucleotide variation of LUAD patients were obtained from TCGA database. The LASSO Cox regression was used to construct a prognostic signature. The CIBERSORT, ESTIMATE and ssGSEA algorithms were applied to assess the association between risk score and TME. TIDE score was applied to reflect the efficiency of immunotherapy response. The influence of overexpression of lncRNA TMPO-AS1 on A549 cell was also assessed by in vitro experiments. The lncRNA prognostic signature included AL606834.1, AL138778.1, AP000302.1, AC007384.1, AL161431.1, TMPO-AS1 and KIAA1671-AS1. Low-risk group exhibited much higher immune score, stromal score and ESTIMATE score, but lower tumor purity compared with high-risk groups. Also, low-risk group was associated with a much higher score of immune cells and immune related function sets, indicating an immune activation state. Low-risk patients had relative higher TIDE score and lower TMB. External validation using IMvigor210 immunotherapy cohort demonstrated that low-risk group had a better prognosis and might more easily benefit from immunotherapy. Overexpression of lncRNA TMPO-AS1 promoted the proliferation, migration and invasion of A549 cell line. The novel cuproptosis-related lncRNA signature could predict the prognosis of LUAD patients, and helped clinicians stratify patients appropriate for immunotherapy and determine individual therapeutic strategies.

A meta-validated immune infiltration-related gene model predicts prognosis and immunotherapy sensitivity in HNSCC

BACKGROUND

Tumor microenvironment (TME) is of great importance to regulate the initiation and advance of cancer. The immune infiltration patterns of TME have been considered to impact the prognosis and immunotherapy sensitivity in Head and Neck squamous cell carcinoma (HNSCC). Whereas, specific molecular targets and cell components involved in the HNSCC tumor microenvironment remain a twilight zone.

METHODS

Immune scores of TCGA-HNSCC patients were calculated via ESTIMATE algorithm, followed by weighted gene co-expression network analysis (WGCNA) to filter immune infiltration-related gene modules. Univariate, the least absolute shrinkage and selection operator (LASSO), and multivariate cox regression were applied to construct the prognostic model. The predictive capacity was validated by meta-analysis including external dataset GSE65858, GSE41613 and GSE686. Model candidate genes were verified at mRNA and protein levels using public database and independent specimens of immunohistochemistry. Immunotherapy-treated cohort GSE159067, TIDE and CIBERSORT were used to evaluate the features of immunotherapy responsiveness and immune infiltration in HNSCC.

RESULTS

Immune microenvironment was significantly associated with the prognosis of HNSCC patients. Total 277 immune infiltration-related genes were filtered by WGCNA and involved in various immune processes. Cox regression identified nine prognostic immune infiltration-related genes (MORF4L2, CTSL1, TBC1D2, C5orf15, LIPA, WIPF1, CXCL13, TMEM173, ISG20) to build a risk score. Most candidate genes were highly expressed in HNSCC tissues at mRNA and protein levels. Survival meta-analysis illustrated high prognostic accuracy of the model in the discovery cohort and validation cohort. Higher proportion of progression-free outcomes, lower TIDE scores and higher expression levels of immune checkpoint genes indicated enhanced immunotherapy responsiveness in low-risk patients. Decreased memory B cells, CD8+ T cells, follicular helper T cells, regulatory T cells, and increased activated dendritic cells and activated mast cells were identified as crucial immune cells in the TME of high-risk patients.

CONCLUSIONS

The immune infiltration-related gene model was well-qualified and provided novel biomarkers for the prognosis of HNSCC.

miR-139-5p and miR-451a as a Diagnostic Biomarker in LUSC

Background

Lung squamous cell carcinoma (LUSC) is a type of lung cancer that originates from segmental or subsegmental bronchial mucosa. There is evidence that miRNA plays an important role in the occurrence and progression of tumors.

Methods

In this study, plasma samples of patients with early LUSC and healthy volunteers were subjected to miRNA sequencing, and the levels of differentially expressed miRNAs (DEMs) in LUSC tissues were analyzed using R language. Cox regression and Kaplan-Meier (K-M) survival curve analyses were performed to determine the relationship between DEMs and prognosis in LUSC, and PCR method was verified for the plasma expression level of DEMs in patients with LUSC. The levels of CYFRA21-1 and SCC-Ag in plasma were measured, and area under curve (AUC) was used to evaluate the diagnostic value of the DEMs.

Results

A total of 21 DEMs were screened out by sequencing. The expression levels of DEMs in tissue samples in the TCGA database were analyzed, and four DEMs with consistent expression levels were further screened from plasma and tissue samples. Regression analysis and K-M curve were performed to select two DEMs (miR-139-5p, miR-451a) that were correlated with the prognosis. PCR verification results showed that the levels of miR-451a and miR-139-5p were low in patients, and the level of miR-139-5p in late stages III & IV with the patients of LUSC was higher than that in stages I & II. The AUC values of the four indicators (SCC-Ag, CYFRA21-1, miR-451a and miR-139-5p) in the diagnosis of LUSC, early and late cases were 0.884, 0.935 and 0.778, respectively.

Conclusion

The detection of miR-139-5p and miR-451a levels in plasma has a certain potential in the non-invasive diagnosis, especially in patients with early stages of LUSC.