Prognostic and Immunotherapeutic Roles of KRAS in Pan-Cancer

Unraveling the clinical impact of differential DNA methylation in PDAC: A systematic review

INTRODUCTION

Despite significant efforts to improve clinical outcomes, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate. The poor prognosis associated with this disease is multifactorial and associated with a highly variable genetic profile associated with its pathogenesis. Epigenetic modifications including DNA methylation further affect the expression of genetic material. However, there is no comprehensive understanding of the clinical impact of DNA methylation in PDAC.

METHODS

A systematic literature review was registered on the International Prospective Register of Systematic Reviews database (CRD42023451955) and followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. An electronic search was conducted using the following databases: CINAHL Plus, Cochrane Library, Embase, Web of Science, Ovid Medline, and Google Scholar. Inclusion criteria included studies of patients with a PDAC diagnosis and information regarding genes or CpG sites that potentially affect diagnosis, prognosis, or survival of PDAC.

RESULTS

The initial search retrieved 2402 articles, and 423 duplicates were excluded. After exclusion criteria was applied, 19 studies were included. The most common genes recorded as affecting tumor pathogenesis were SFRP1 (n = 3/19, 15.7 %) and NPTX2 (n = 2/19, 10,5 %). Studies indicated that hypermethylation of SFRP1 and NPTX2 were associated with poor prognosis.

CONCLUSIONS

PDAC is associated with a range of epigenetic modifications. Methylation of specific genes related to PDAC may influence survival and prognosis and be a therapeutic target. Individual patient epigenetic analysis may be a future direction in directing PDAC treatment and prognosis.

Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma

BACKGROUND

Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients.

METHODS

DC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. Expression of key genes in clinical samples was examined by real-time q-PCR. Performance of the prognostic model, DCRGS, for the prognostic evaluation, was assessed using a multiple time-dependent receiver operating characteristic (ROC) curve, the R package, "timeROC", and validated using GEO datasets.

RESULTS

Analysis of scRNA-seq data showed that there is a significant upregulation of LGALS9 expression in DCs isolated from malignant pleural effusion samples. Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. A high predictive capability nomogram was constructed by combining DCRGS with clinical features. We found that patients with a high-DCRGS score had immunosuppression, activated tumor-associated pathways, and elevated somatic mutational load and copy number variant load. In contrast, patients in the low-DCRGS subgroup were resistant to chemotherapy but sensitive to the CTLA-4 immune checkpoint inhibitor and targeted therapy.

CONCLUSION

We have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. The model possesses a strong prognostic prediction performance with high accuracy and sensitivity and could be clinically useful to guide the management of LUAD. Furthermore, the findings of this study could provide an important reference for individualized clinical treatment and prognostic prediction of patients with LUAD.

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data

The stage prediction of kidney renal clear cell carcinoma (KIRC) is important for the diagnosis, personalized treatment, and prognosis of patients. Many prediction methods have been proposed, but most of them are based on unimodal gene data, and their accuracy is difficult to further improve. Therefore, we propose a novel multi-weighted dynamic cascade forest based on the bilinear feature extraction (MLW-BFECF) model for stage prediction of KIRC using multimodal gene data (RNA-seq, CNA, and methylation). The proposed model utilizes a dynamic cascade framework with shuffle layers to prevent early degradation of the model. In each cascade layer, a voting technique based on three gene selection algorithms is first employed to effectively retain gene features more relevant to KIRC and eliminate redundant information in gene features. Then, two new bilinear models based on the gated attention mechanism are proposed to better extract new intra-modal and inter-modal gene features; Finally, based on the idea of the bagging, a multi-weighted ensemble forest classifiers module is proposed to extract and fuse probabilistic features of the three-modal gene data. A series of experiments demonstrate that the MLW-BFECF model based on the three-modal KIRC dataset achieves the highest prediction performance with an accuracy of 88.9 %.

FAM20A: a potential diagnostic biomarker for lung squamous cell carcinoma

Background

Lung squamous cell carcinoma (LUSC) ranks among the carcinomas with the highest incidence and dismal survival rates, suffering from a lack of effective therapeutic strategies. Consequently, biomarkers facilitating early diagnosis of LUSC could significantly enhance patient survival. This study aims to identify novel biomarkers for LUSC.

Methods

Utilizing the TCGA, GTEx, and CGGA databases, we focused on the gene encoding Family with Sequence Similarity 20, Member A (FAM20A) across various cancers. We then corroborated these bioinformatic predictions with clinical samples. A range of analytical tools, including Kaplan-Meier, MethSurv database, Wilcoxon rank-sum, Kruskal-Wallis tests, Gene Set Enrichment Analysis, and TIMER database, were employed to assess the diagnostic and prognostic value of FAM20A in LUSC. These tools also helped evaluate immune cell infiltration, immune checkpoint genes, DNA repair-related genes, DNA methylation, and tumor-related pathways.

Results

FAM20A expression was found to be significantly reduced in LUSC, correlating with lower survival rates. It exhibited a negative correlation with key proteins in DNA repair signaling pathways, potentially contributing to LUSC's radiotherapy resistance. Additionally, FAM20A showed a positive correlation with immune checkpoints like CTLA-4, indicating potential heightened sensitivity to immunotherapies targeting these checkpoints.

Conclusion

FAM20A emerges as a promising diagnostic and prognostic biomarker for LUSC, offering potential clinical applications.

Unveiling the role of KRAS in tumor immune microenvironment

Kirsten rats sarcoma viral oncogene (KRAS), the first discovered human oncogene, has long been recognized as "undruggable". KRAS mutations frequently occur in multiple human cancers including non-small cell lung cancer(NSCLC), colorectal cancer(CRC) and pancreatic ductal adenocarcinoma(PDAC), functioning as a "molecule switch" determining the activation of various oncogenic signaling pathways. Except for its intrinsic pro-tumorigenic role, KRAS alteration also exhibits an unique immune signature characterized by elevated PD-L1 level and high tumor mutational burden(TMB). KRAS mutation shape an immune suppressive microenvironment by impeding effective T cells infiltration and recruiting suppressive immune cells including myeloid-derived suppressor cells(MDSCs), regulatory T cells(Tregs), cancer associated fibroblasts(CAFs). In immune checkpoint inhibitor(ICI) era, NSCLC patients with mutated KRAS tend to be more responsive to ICI than patients with intact KRAS. The hallmark for KRAS mutation is the existence of multiple kinds of co-mutations. Different types of co-alterations have distinct tumor microenvironment(TME) signatures and responses to ICI. TP53 co-mutation possess a "hot" TME and achieve higher response to immunotherapy while other loss of function mutation correlated with a "colder" TME and a poor outcome to ICI-based therapy. The groundbreaking discovery of KRAS G12C inhibitors significantly improved outcomes for this KRAS subtype even though efficacy was limited to NSCLC patients. KRAS G12C inhibitors also restore the suppressive TME, creating an opportunity for combinations with ICI. However, an inevitable challenge to KRAS inhibitors is drug resistance. Promising combination strategies such as combination with SHP2 is an approach deserve further exploration because of their immune modulatory effect.

Cuproptosis patterns and tumor immune infiltration characterization in colorectal cancer

Faced with the high heterogeneity and poor prognosis of colorectal cancer (CRC), this study sought to find new predictive prognostic strategies to improve the situation. Cuproptosis is a novel cell death mechanism that relies on copper regulation. However, the role of cuproptosis-related gene (CRG) in CRC remains to be elucidated. In this study, we comprehensively assessed the CRG landscape in CRC based on The Cancer Genome Atlas (TCGA). We identified differential expression and genetic alterations of CRG in CRC. CRG is highly correlated with initiation, progression, prognosis, and immune infiltration of CRC. We construct a risk score signature containing 3 CRGs based on LASSO. We explored the correlation of CRG-Score with clinicopathological features of CRC. Age, stage, and CRG-Score were integrated to construct a nomogram. The nomogram has robust predictive performance. We also understand the correlation of CRG-Score with CRC immune landscape. CRG-Score can effectively predict the immune landscape of CRC patients. Low-risk CRC patients have greater immunogenicity and higher immune checkpoint expression. Low-risk CRC patients may be better candidates for immunotherapy. At the same time, we also predicted more sensitive drugs in the high-risk CRC patients. In conclusion, the CRG risk score signature is a strong prognostic marker and may help provide new insights into the treatment of individuals with CRC.