Construction and Verification of a Fibroblast-Related Prognostic Signature Model for Colon Cancer

Identification and Validation of a Novel Anoikis-Related Gene Signature for Predicting Survival in Patients With Serous Ovarian Cancer

Background

Ovarian cancer is an extremely deadly gynecological malignancy, with a 5-year survival rate below 30%. Among the different histological subtypes, serous ovarian cancer (SOC) is the most common. Anoikis significantly contributes to the progression of ovarian cancer. Therefore, identifying an anoikis-related signature that can serve as potential prognostic predictors for SOC is of great significance.

Methods

We intersected 308 anoikis-related genes (ARGs) and identified those significantly associated with SOC prognosis using univariate Cox regression. A LASSO Cox regression model was constructed and evaluated using Kaplan-Meier and receiver operating characteristic (ROC) analyses in TCGA (The Cancer Genome Atlas) and GSE26193 cohorts. We conducted quantitative real-time polymerase chain reaction (qPCR) to assess mRNA levels and applied bioinformatics to investigate the correlation between risk groups and gene expression, mutations, pathways, tumor immune microenvironment (TIME), and drug sensitivity in SOC.

Results

Among 308 ARGs, 28 were significantly associated with SOC prognosis. A 13-gene prognostic model was established through LASSO Cox regression in TCGA cohort. High-risk group had poorer prognosis than low-risk group (median overall survival (mOS): 34.2 vs. 57.1 months, hazard ratio (HR): 2.590, 95% confidence interval (CI): 0.159 - 6.00, P < 0.001). The area under the curve (AUC) values of 0.63, 0.65, and 0.74 reflected the predictive performance for 3-, 5-, and 8-year overall survival (OS) in GSE26193 validation cohort. Functional enrichment, pathway analysis, and TIME analysis identified distinct characteristics between risk groups. Drug sensitivity analysis revealed potential drug advantages for each group. Furthermore, qPCR validation once again confirmed the effectiveness of the risk model in SOC patients.

Conclusions

We developed and validated a robust ARG model, which could be used to predict OS in SOC patients. By systematically analyzing the correlation between the risk score of the ARGs signature model and various patterns, including the TIME and drug sensitivity, our findings suggest that this prognostic model contributes to the advancement of personalized and precise therapeutic strategies. Nevertheless, further validation studies and investigations into the underlying mechanisms are warranted.

Development and verification of a combined immune- and cancer-associated fibroblast related prognostic signature for colon adenocarcinoma

Introduction

To better understand the role of immune escape and cancer-associated fibroblasts (CAFs) in colon adenocarcinoma (COAD), an integrative analysis of the tumor microenvironment was performed using a set of 12 immune- and CAF-related genes (ICRGs).

Methods

Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to establish a prognostic signature based on the expression of these 12 genes (S1PR5, AEN, IL20RB, FGF9, OSBPL1A, HSF4, PCAT6, FABP4, KIF15, ZNF792, CD1B and GLP2R). This signature was validated in both internal and external cohorts and was found to have a higher C-index than previous COAD signatures, confirming its robustness and reliability. To make use of this signature in clinical settings, a nomogram incorporating ICRG signatures and key clinical parameters, such as age and T stage, was developed. Finally, the role of S1PR5 in the immune response of COAD was validated through in vitro cytotoxicity experiments.

Results

The developed nomogram exhibited slightly improved predictive accuracy compared to the ICRG signature alone, as indicated by the areas under the receiver operating characteristic curves (AUC, nomogram:0.838; ICRGs:0.807). The study also evaluated the relationships between risk scores (RS) based on the expression of the ICRGs and other key immunotherapy variables, including immune checkpoint expression, immunophenoscore (IPS), and microsatellite instability (MSI). Integration of these variables led to more precise prediction of treatment efficacy, enabling personalized immunotherapy for COAD patients. Knocking down S1PR5 can enhance the efficacy of PD-1 monoclonal antibody, promoting the cytotoxicity of T cells against HCT116 cells ((p<0.05).

Discussion

These findings indicate that the ICRG signature may be a valuable tool for predicting prognostic risk, evaluating the efficacy of immunotherapy, and tailoring personalized treatment options for patients with COAD.

Identification of cancer-associated fibroblasts signature for predicting the prognosis and immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies globally with poor prognosis. Cancer-associated fibroblasts (CAFs) play multiple functions in the regulation of tumorigenesis, metastasis and therapeutic resistance of cancer. The current study aimed to explore the role of CAFs-related genes in the prognosis and immunotherapy response in HCC. CAFs-related genes were identified by using single-cell RNA-sequencing analysis. Least absolute shrinkage and selection operator (LASSO) analysis was conducted to develop a CAFs-related prognostic signature (FRPS) in TCGA dataset and verified in ICGC, GSE14520 and GSE76427 cohorts. Several tools, including Tumor Immune Dysfunction and Exclusion (TIDE) score, immunophenoscore, and Tumor Mutation Burden (TMB) score were used to evaluate the value of FRPS in predicting immunotherapy benefits. The FRPS constructed based on 10 genes (RGS5, CNN3, PALLD, FLNA, KLHL23, MYC, NDRG2, SERPINE1, CD151 CALU) served as an independent risk factor and showed stable and powerful performance in predicting the overall survival rate of HCC patients with an AUCs of 0. 734, 0.727, and 0.717 in 2-, 3-, and 4-year ROC curve in TCGA cohort. Low risk score indicated a higher abundance of CD8+ T cells and NK, and lower abundance of Treg. Moreover, HCC patients with low risk score had a higher PD1&CTLA4 immunophenoscore, higher TMB score, and lower TIDE score. Moreover, high risk score indicated a lower IC50 value of 5-fluorouracil, camptothecin, cisplatin, docetaxel, gemcitabine, paclitaxel, afatinib, crizotinib, dasatinib, erlotinib, erlotinib, gefitinib, lapatinib, and osimertinib in HCC. Our study develops a novel FRPS HCC. The FRPS acts as a risk factor for the prognosis of HCC patients and it can predict the immunotherapy benefits of HCC patients.

Identification and validation of cancer-associated fibroblast-related subtypes and the prognosis model of biochemical recurrence in prostate cancer based on single-cell and bulk RNA sequencing

BACKGROUND

Cancer-associated fibroblasts (CAFs) are an essential component of the tumor immune microenvironment that are involved in extracellular matrix (ECM) remodeling. We aim to investigate the characteristics of CAFs in prostate cancer and develop a biochemical recurrence (BCR)-related CAF signature for predicting the prognosis of PCa patients.

METHODS

The bulk RNA-seq and relevant clinical information were obtained from the TCGA and GEO databases, respectively. The infiltration scores of CAFs in prostate cancer patients were calculated using the MCP counter and EPIC algorithms. The single-cell RNA sequencing (scRNA-seq) was downloaded from the GEO database. Subsequently, univariate Cox regression analysis was employed to identify prognostic genes associated with CAFs. We identified two subtypes (C1 and C2) of prostate cancer that were associated with CAFs via non-negative matrix factorization (NMF) clustering. In addition, the BCR-related CAF signatures were constructed using Lasso regression analysis. Finally, a nomogram model was established based on the risk score and clinical characteristics of the patients.

RESULTS

Initially, we found that patients with high CAF infiltration scores had shorter biochemical recurrence-free survival (BCRFS) times. Subsequently, CAFs in four pairs of tumors and paracancerous tissues were identified. We discovered 253 significantly differentially expressed genes, of which 13 had prognostic significance. Using NMF clustering, we divided PCa patients into C1 and C2 subgroups, with the C1 subgroup having a worse prognosis and substantially enriched cell cycle, homologous recombination, and mismatch repair pathways. Furthermore, a BCR-related CAFs signature was established. Multivariate COX regression analysis confirmed that the BCR-related CAFs signature was an independent prognostic factor for BCR in PCa. In addition, the nomogram was based on the clinical characteristics and risk scores of the patient and demonstrated high accuracy and reliability for predicting BCR. Lastly, our findings indicate that the risk score may be a useful tool for predicting PCa patients' sensitivity to immunotherapy and drug treatment.

CONCLUSION

NMF clustering based on CAF-related genes revealed distinct TME immune characteristics between groups. The BCR-related CAF signature accurately predicted prognosis and immunotherapy response in prostate cancer patients, offering a promising new approach to cancer treatment.

Identification of CKS2 as a novel prognostic biomarker and potential therapeutic target for oral squamous cell carcinoma

Background

The cyclin-dependent kinase subunit 2 (CKS2) is recognized to have a substantial impact on the pathogenesis and advancement of several malignant neoplasms. Nevertheless, its biological function and prognostic significance in oral squamous cell carcinoma (OSCC) have yet to be thoroughly investigated. Our primary objective was to clarify the contribution of CKS2 in the progression and prognosis of OSCC.

Methods

We first conducted a thorough examination of online databases to investigate the expression of CKS2, and subsequently corroborated our discoveries by analyzing clinical specimens that we collected. According to the clinicopathological data, we then explored the prognostic significance of CKS2. Furthermore, we predicted the role of CKS2 in OSCC progression by employing weighted gene co-expression network analysis (WGCNA) in conjunction with functional enrichment analysis. We conducted functional experiments in vitro to confirm our speculations. Additionally, we explored other potential functions of CKS2 in immune infiltration, tumor mutation burden (TMB), and drug sensitivity. Finally, we established and validated a nomogram that effectively integrated CKS2-related genes and other relevant clinical factors.

Results

Our findings indicated a significant upregulation of CKS2 expression in OSCC tissues compared to normal groups, which was positively associated with poor clinical outcomes. We also predicted and validated the role of CKS2 in promoting proliferation by regulating the cell cycle. Additionally, its upregulation was significantly correlated to enhanced immune cell infiltration, high TMB, and increased sensitivity of anti-tumor agents. Following verification, the nomogram was conducted to quantify an individual's survival probability.

Conclusions

In general, our study indicates that CKS2 is a novel prognostic biomarker and potential therapeutic target in OSCC.

Cancer-associated fibroblast-related prognostic signature predicts prognosis and immunotherapy response in pancreatic adenocarcinoma based on single-cell and bulk RNA-sequencing

Cancer-associated fibroblasts (CAFs) influence many aspects of pancreatic adenocarcinoma (PAAD) carcinogenesis, including tumor cell proliferation, angiogenesis, invasion, and metastasis. A six-gene prognostic signature was constructed for PAAD based on the 189 CAF marker genes identified in single-cell RNA-sequencing data. Multivariate analyses showed that the risk score was independently prognostic for survival in the TCGA (P < 0.001) and ICGC (P = 0.004) cohorts. Tumor infiltration of CD8 T (P = 0.005) cells and naïve B cells (P = 0.001) was greater in the low-risk than in the high-risk group, with infiltration of these cells negatively correlated with risk score. Moreover, the TMB score was lower in the low-risk than in the high-risk group (P = 0.0051). Importantly, patients in low-risk group had better immunotherapy responses than in the high-risk group in an independent immunotherapy cohort (IMvigor210) (P = 0.039). The CAV1 and SOD3 were highly expressed in CAFs of PAAD tissues, which revealed by immunohistochemical staining. In summary, this comprehensive analysis resulted in the development of a novel prognostic signature, which was associated with immune cell infiltration, drug sensitivity, and TMB, and could predict the prognosis and immunotherapy response of patients with PAAD.

Three-dimensional heterotypic colorectal cancer spheroid models for evaluation of drug response

Colorectal cancer (CRC) is a leading cause of death worldwide. Improved preclinical tumor models are needed to make treatment screening clinically relevant and address disease mortality. Advancements in 3D cell culture have enabled a greater recapitulation of the architecture and heterogeneity of the tumor microenvironment (TME). This has enhanced their pathophysiological relevance and enabled more accurate predictions of tumor progression and drug response in patients. An increasing number of 3D CRC spheroid models include cell populations such as cancer-associated fibroblasts (CAFs), endothelial cells (ECs), immune cells, and gut bacteria to better mimic the in vivo regulation of signaling pathways. Furthermore, cell heterogeneity within the 3D spheroid models enables the identification of new therapeutic targets to develop alternative treatments and test TME-target therapies. In this mini review, we present the advances in mimicking tumor heterogeneity in 3D CRC spheroid models by incorporating CAFs, ECs, immune cells, and gut bacteria. We introduce how, in these models, the diverse cells influence chemoresistance and tumor progression of the CRC spheroids. We also highlight important parameters evaluated during drug screening in the CRC heterocellular spheroids.