Identification and verification of a PANoptosis-related long noncoding ribonucleic acid signature for predicting the clinical outcomes and immune landscape in lung adenocarcinoma.

PANoptosis is a type of programmed cell death (PCD) characterised by apoptosis, necroptosis and pyroptosis. Long non-coding ribonucleic acids (lncRNAs) are participating in the malignant behaviour of tumours regulated by PCD. Nevertheless, the function of PANoptosis-associated lncRNAs in lung adenocarcinoma remains to be investigated. In this work, a PANoptosis-related lncRNA signature (PRLSig) was developed based on the least absolute shrinkage and selection operator algorithm. The stability and fitness of PRLSig were confirmed by systematic evaluation of Kaplan-Meier, Cox analysis algorithm, receiver operating characteristic analysis, stratification analysis. In addition, ESTIMATE, single sample gene set enrichment analysis, immune checkpoints and the cancer immunome database confirmed the predictive value of the PRLSig in immune microenvironment and helped to identify populations for which immunotherapy is advantageous. The present research provides novel insights to facilitate risk stratification and optimise personalised treatment for LUAD.

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma.

INTRODUCTION: Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC). METHODS: Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related Scoring System (TRSSys). GSE140520, ICGC-LIRI and CHCC cohorts were used for the validation of TRSSys. Kaplan-Meier, ROC, and Cox regression were utilised for the evaluation of TRSSys. The ESTIMATE, TIMER 2.0, and ssGSEA algorithm were utilised to determine the value of TRSSys in predicting the TIME. GSVA, GO, KEGG, and TMB analyses were used for mechanistic exploration. Finally, the value of TRSSys in predicting drug sensitivity was evaluated based on the oncoPredict algorithm. RESULTS: Comprehensive validation showed that TRSSys had good prognostic predictive efficacy and applicability. Additionally, ssGSEA, TIMER and ESTIMATE algorithm suggested that TRSSys could help to distinguish different TIME subtypes and determine the beneficiary population of immunotherapy. Finally, the oncoPredict algorithm suggests that TRSSys provides a basis for individualised treatment. CONCLUSIONS: TRSSys constructed in the current study is a novel HCC prognostic prediction biomarker with good predictive efficacy and stability. Additionally, risk stratification based on TRSSys can help to identify the TIME landscape subtypes and provide a basis for individualized treatment options.

Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing.

BACKGROUND: Cancer-associated fibroblasts (CAFs) regulate the malignant biological behaviour of hepatocellular carcinoma (HCC) as a significant component of the tumour immune microenvironment (TIME). This study aimed to develop a CAFs-based scoring system to predict the prognosis and TIME of patients with HCC. METHODS: Data for the TCGA-LIHC and GSE14520 cohorts were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Single-cell RNA-sequencing data for HCC samples were retrieved from the GSE166635 cohort. The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss). The predictive value of the CAFRss was determined using Kaplan-Meier, Cox regression and Receiver Operating Characteristic curves. Additionally, the TIMER platform, single sample Gene Set Enrichment Analysis and the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data algorithms were performed to determine the TIME landscape. Finally, the pRRophic algorithm was utilised for drug sensitivity analysis. RESULTS: The evaluation of the CAFRss system demonstrated its superior ability to predict the clinical outcome of patients with HCC. Additionally, CAFRss effectively distinguished HCC populations with distinct TIME landscapes. Furthermore, CAFRss-based risk stratification identified individuals with immune 'hot tumours' and predicted the survival of patients treated with ICBs. CONCLUSIONS: The developed CAFRss can serve as a predictive tool for determining the clinical outcome of HCC and differentiating populations with diverse TIME characteristics.

A tetrahedral DNA nanostructure-decorated electrochemical platform for simple and ultrasensitive EGFR genotyping of plasma ctDNA.

Genotyping of the epidermal growth factor receptor (EGFR) mutation status is of great importance in the screening of appropriate patients with advanced non-small cell lung carcinoma (NSCLC) to receive superior tyrosine kinase inhibitor (TKIs) therapy. Yet conventional assays are generally costly with a relatively long turnaround time for obtaining results, which can lead to a bottleneck for immediately starting TKI therapy in late-staged patients. In this study, we propose an on-site electrochemical platform for sensitive simultaneous genotyping of the two major EGFR mutations (19del and L858R) through plasma ctDNA based on tetrahedral DNA nanostructure decorated screen-printed electrodes (SPE). Linear-after-the-exponential (LATE)-PCR combined with the amplification refractory mutation system (ARMS) was adopted to produce abundant biotin-labeled single-stranded DNA with high amplification efficiency and specificity. Disposable SPE decorated with self-assembled tetrahedral nanostructured DNA probes that showed ordered orientation and good target accessibility enabled the highly efficient hybridization of the specific amplicons through a sandwich-type and quantitatively translated the interfacial hybridization event into electrochemical signals via enzymatic amplification. Taking advantage of the ARMS-based LATE-PCR and the tetrahedral nanostructure-decorated SPE platform, we achieved the accurate detection of around 30 pg DNA of 19del or L858R, or as low as 0.1% of them in the presence of wild-type DNA. Moreover, the EGFR mutation profiles of 13 NSCLC patients we enlisted were accurately genotyped by our electrochemical platform, the results of which were in good agreement with those of commercial genetic detection methods.

An immune-related gene signature predicts prognosis of gastric cancer.

BACKGROUND: Although the outcome of patients with gastric cancer (GC) has improved significantly with the recent implementation of annual screening programs. Reliable prognostic biomarkers are still needed due to the disease heterogeneity. Increasing pieces of evidence revealed an association between immune signature and GC prognosis. Thus, we aim to build an immune-related signature that can estimate prognosis for GC. METHODS: For identification of a prognostic immune-related gene signature (IRGS), gene expression profiles and clinical information of patients with GC were collected from 3 public cohorts, divided into training cohort (n = 300) and 2 independent validation cohorts (n = 277 and 433 respectively). RESULTS: Within 1811 immune genes, a prognostic IRGS consisting of 16 unique genes was constructed which was significantly associated with survival (hazard ratio [HR], 3.9 [2.78-5.47]; P < 1.0 × 10). In the validation cohorts, the IRGS significantly stratified patients into high- vs low-risk groups in terms of prognosis across (HR, 1.84 [1.47-2.30]; P = 6.59 × 10) and within subpopulations with stage I&II disease (HR, 1.96 [1.34-2.89]; P = 4.73 × 10) and was prognostic in univariate and multivariate analyses. Several biological processes, including TGF-ß and EMT signaling pathways, were enriched in the high-risk group. T cells CD4 memory resting and Macrophage M2 were significantly higher in the high-risk risk group compared with the low-risk group. CONCLUSION: In short, we developed a prognostic IRGS for estimating prognosis in GC, including stage I&II disease, providing new insights into the identification of patients with GC with a high risk of mortality.