Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma

A Prognostic Model for Senescence-Related LncRNA in a Novel Colon Adenocarcinoma Based on WGCNA and LASSO Regression

Objective: This study aims to develop a prognostic model based on senescence-related long non-coding RNAs (lncRNAs) to predict the prognosis of patients with colon cancer and enhance their survival rates. Method: Differential expression analysis and Pearson correlation were employed to identify senescence-related lncRNAs in colon cancer. A risk prognosis model was constructed using univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The reliability of this model was validated through survival analysis, receiver operating characteristic (ROC) curves, bar charts, and calibration curves. Additionally, the relationship between the prognostic model, immune microenvironment, and drug sensitivity was explored. Results: A risk prognosis model comprising eight senescence-related lncRNAs (LINC02257, AL138921.1, ATP2B1-AS1, AC005332.7, AC007728.3, AC018755.4, AL390719.3, and THCAT158) was successfully established, demonstrating strong performance in predicting the overall survival rates of colon cancer patients (AUC = 0.733). A significant correlation was observed between the senescence-related lncRNA prognostic model and the tumor microenvironment, immune cell infiltration, and drug sensitivity (p < 0.05). Conclusions: The senescence-related lncRNA prognostic model developed in this work can accurately forecast the prognosis of colon cancer patients, offering new insights for personalized treatment approaches in colon cancer.

Single-cell and spatial transcriptomic analysis reveals tumor cell heterogeneity and underlying molecular program in colorectal cancer

Background

Colorectal cancer (CRC) is a highly heterogeneous tumor, with significant variation in malignant cells, posing challenges for treatment and prognosis. However, this heterogeneity offers opportunities for personalized therapy.

Methods

The consensus non-negative matrix factorization algorithm was employed to analyze single-cell transcriptomic data from CRC, which helped identify malignant cell expression programs (MCEPs). Subsequently, a crosstalk network linking MCEPs with immune/stromal cell trajectory development was constructed using Monocle3 and NicheNet. Additionally, bulk RNA-seq data were utilized to systematically explore the relationships between MCEPs, clinical features, and genetic mutations. A prognostic model was then established through Lasso and Cox regression analyses, integrating clinical data into a nomogram for personalized risk prediction. Furthermore, key genes associated with MCEPs and their potential therapeutic targets were identified using protein-protein interaction networks, followed by molecular docking to predict drug-binding affinity.

Results

We classified CRC malignant cell transcriptional states into eight distinct MCEPs and successfully constructed crosstalk networks between these MCEPs and immune or stromal cells. A prognostic model containing 15 genes was developed, demonstrating an AUC greater than 0.8 for prognostic evaluation over 1 to 10 years when combined with clinical features. A key drug target gene TIMP1 was identified, and several potential targeted drugs were discovered.

Conclusion

This study demonstrated that characterization of the malignant cell transcriptional programs could effectively reveal the biological features of highly heterogeneous tumors like CRC and exhibit significant potential in tumor prognosis assessment. Our research provides new theoretical and practical directions for CRC prognosis and targeted therapy.

A nicotinamide metabolism-related gene signature for predicting immunotherapy response and prognosis in lung adenocarcinoma patients

Background

Nicotinamide (NAM) metabolism fulfills crucial functions in tumor progression. The present study aims to establish a NAM metabolism-correlated gene (NMRG) signature to assess the immunotherapy response and prognosis of lung adenocarcinoma (LUAD).

Methods

The training set and validation set (the GSE31210 dataset) were collected The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. Molecular subtypes of LUAD were classified by consensus clustering. Mutation landscape of the top 20 somatic genes was visualized by maftools package. Subsequently, differential expression analysis was conducted using the limma package, and univariate, multivariate and LASSO regression analyses were performed on the screened genes to construct a risk model for LUAD. Next, the MCP-counter, TIMER and ESTIMATE algorithms were utilized to comprehensively assess the immune microenvironmental profile of LUAD patients in different risk groups. The efficacy of immunotherapy and chemotherapy drugs was evaluated by TIDE score and pRRophetic package. A nomogram was created by integrating RiskScore and clinical features. The mRNA expressions of independent prognostic NMRGs and the migration and invasion of LUAD cells were measured by carrying out cellular assays.

Results

Two subtypes (C1 and C2) of LUAD were classified, with C1 subtype showing a worse prognosis than C2. The top three genes with a high mutation frequency in C1 and C2 subtypes were TTN (45.25%), FLG (25.25%), and ZNF536 (19.8%). Four independent prognostic NMRGs (GJB3, CPA3, DKK1, KRT6A) were screened and used to construct a RiskScore model, which exhibited a strong predictive performance. High-risk group showed low immune cell infiltration, high TIDE score, and worse prognosis, and the patients in this group exhibited a high drug sensitivity to Cisplatin, Erlotinib, Paclitaxel, Saracatini, and CGP_082996. A nomogram was established with an accurate predictive and diagnostic performance. GJB3, DKK1, CPA3, and KRT6A were all high- expressed in LUAD cells, and silencing GJB3 inhibited the migration and invasion of LUAD cells.

Conclusion

A novel NMRG signature was developed, contributing to the prognostic evaluation and personalized treatment for LUAD patients.

NADPH oxidase activator 1 (NOXA1) suppresses ferroptosis and radiosensitization in colorectal cancer

Radiotherapy is one of the main treatments for colorectal cancer (CRC), but due to the intrinsic resistance of cells or resistance caused by long-term radiotherapy, the effectiveness of this treatment is limited for some CRC patients. Consequently, identifying novel sensitization strategies is essential. This study identifies Noxa1 as a marker linked to radiotherapy resistance in CRC, suggesting its potential as a prognostic biomarker for patients with CRC. The study found that Noxa1 was significantly overexpressed in radiotherapy-resistant colorectal cancer patients, correlating with a poor prognosis. Additionally, we discovered that the high expression of Noxa1 was negatively correlated with ferroptosis and primarily played a role through the glutathione metabolic pathway, as indicated by GSVA analysis. Experimental data indicated that the expression levels of NOXA1, SLC7A11, and GPX4 were significantly elevated in CRC cell lines resistant to radiotherapy. The expression of SLC7A11 and GPX4 decreased after the knockdown of Noxa1, leading to an increase in cellular ROS levels, which induced ferroptosis and sensitized the cells to radiotherapy. Therefore, Noxa1 might influence the radiotherapy sensitivity of CRC via regulating ferroptosis. Targeting Noxa1 could enhance radiotherapy sensitization and improve the prognosis of CRC patients.

PCPE-2 (procollagen C-proteinase enhancer-2): The non-identical twin of PCPE-1

PCPE-2 was discovered at the beginning of this century, and was soon identified as a close homolog of PCPE-1 (procollagen C-proteinase enhancer 1). After the demonstration that it could also stimulate the proteolytic maturation of fibrillar procollagens by BMP-1/tolloid-like proteinases (BTPs), PCPE-2 did not attract much attention as it was thought to fulfill the same functions as PCPE-1 which was already well-described. However, the tissue distribution of PCPE-2 shows both common points and significant differences with PCPE-1, suggesting that their activities are not fully overlapping. Also, the recently established connections between PCPE-2 (gene name PCOLCE2) and several important diseases such as atherosclerosis, inflammatory diseases and cancer have highlighted the need for a thorough reappraisal of the in vivo roles of this regulatory protein. In this context, the recent finding that, while retaining the ability to bind fibrillar procollagens and to activate their C-terminal maturation, PCPE-2 can also bind BTPs and inhibit their activity has substantially extended its potential functions. In this review, we describe the current knowledge about PCPE-2 with a focus on collagen fibrillogenesis, lipid metabolism and inflammation, and discuss how we could further advance our understanding of PCPE-2-dependent biological processes.

PCPE2: Expression of multifunctional extracellular glycoprotein associated with diverse cellular functions

Procollagen C-endopeptidase enhancer 2, known as PCPE2 or PCOC2 (gene name, PCOLCE2) is a glycoprotein that resides in the extracellular matrix, and is similar in domain organization to PCPE1/PCPE, PCOC1 (PCOLCE1/PCOLCE). Due to the many similarities between the two related proteins, PCPE2 has been assumed to have biological functions similar to PCPE. PCPE is a well-established enhancer of procollagen processing activating the enzyme, BMP-1. However, reports show that PCPE2 has a strikingly different tissue expression profile compared to PCPE. With that in mind and given the paucity of published studies on PCPE2, this review examines the current literature citing PCPE2 and its association with specific cell types and signaling pathways. Additionally, this review will present a brief history of PCPE2's discovery, highlighting structural and functional similarities and differences compared to PCPE. Considering the widespread use of RNA sequencing techniques to examine associations between cell-specific gene expression and disease states, we will show that PCPE2 is repeatedly found as a differentially regulated gene (DEG) significantly associated with a number of cellular processes, well beyond the scope of procollagen fibril processing.

A novel PCDscore based on programmed cell death-related genes can effectively predict prognosis and therapy responses of colon adenocarcinoma

Emerging evidence suggests a correlation between oncogenesis and programmed cell death (PCD). However, comprehensive studies that incorporate all identified PCD-related genes to guide colon adenocarcinoma (COAD) prognosis and precision treatment strategies are lacking. In this study, a series of bioinformatics analyses were comprehensively conducted using data from the TCGA-COAD, GSE17538, and GSE39582 cohorts. A total of 21 PCD-associated prognostic genes were identified through univariate Cox analysis. LASSO and multivariate Cox methods were employed to establish a prognostic gene signature (ALOX12, HSPA1A, IL13, MID2, RFFL, and SLC39A8) and the corresponding scoring system, termed PCDscore, which exhibited robust predictive ability. The ssGSEA and ESTIMATE algorithms were utilized to evaluate the tumor microenvironment of COAD. The high PCDscore group demonstrated a poorer prognosis, characterized by lower CD4+ T cell infiltration and a higher stromal score. In contrast, the low PCDscore group exhibited sensitivity to common chemotherapy drugs such as Cisplatin and 5-Fluorouracil. Single-cell sequencing analysis further revealed that the high-PCDscore group displayed a lower proportion of CD4+ T cells. Colorectal cancer samples from the years 2013-2017 were employed to validate the PCDscore, while those from 2018 to 2019 served as a temporal external validation set for the PCDscore. In vitro experimental results indicated that the overexpression of SLC39A8 inhibited the proliferation and invasion of colorectal cancer cells. The study developed a novel PCDscore system based on the analysis of genes related to all identified PCD types, providing valuable insights into clinical prognosis and drug sensitivity for patients with COAD.

Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data

Background

Colon cancer (CC) ranks as one of the leading causes of cancer-related mortality globally. Single-cell transcriptome sequencing (scRNA-seq) offers precise gene expression data for distinct cell types. This study aimed to utilize scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data from CC samples to develop a novel prognostic model.

Methods

scRNA-seq data was downloaded from the GSE161277 database. R packages including "Seurat", "Harmony", and "singleR" were employed to categorize eight major cell types within normal and tumor tissues. By comparing tumor and normal samples, differentially expressed genes (DEGs) across these major cell types were identified. Gene Ontology (GO) enrichment analyses of DEGs for each cell type were conducted using "Metascape". DEGs-based signature construction involved Cox regression and least absolute shrinkage operator (LASSO) analyses, performed on The Cancer Genome Atlas (TCGA) training cohort. Validation occurred in the GSE39582 and GSE33382 datasets. The expression pattern of prognostic genes was verified using spatial transcriptome sequencing (ST-seq) data. Ultimately, an established prognostic nomogram based on the gene signature and age was established and calibrated. Sensitivity to chemotherapeutic drugs was predicted with the "oncoPredict" R package.

Results

Using scRNA-Seq data, we examined 33,213 cells, categorizing them into eight cell types within normal and tumor samples. GO enrichment analysis revealed various cancer-related pathways across DEGs in these cell types. Among the 55 DEGs identified via univariate Cox regression, four independent prognostic genes emerged: PTPN6, CXCL13, SPINK4, and NPDC1. Expression validation through ST-seq confirmed PTPN6 and CXCL13 predominance in immune cells, while SPINK4 and NPDC1 were relatively epithelial cell-specific. Creating a four-gene prognostic signature, Kaplan-Meier survival analyses emphasized higher risk scores correlating with unfavorable prognoses, confirmed across training and validation cohorts. The risk score emerged as an independent prognostic factor, supported by a reliable nomogram. Intriguingly, drug sensitivity analysis unveiled contrasting anti-cancer drug responses in the two risk groups, suggesting significant clinical implications.

Conclusion

We developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC.