Lung adenocarcinoma (LUAD) represents the primary tissue subtype of lung cancer, characterized by a significant mortality rate and unfavorable prognosis. Long non-coding RNAs (lncRNAs) serve as critical functional units in the initiation and progression of tumors. Peroxisomes, now recognized as vital organelles in tumor immune metabolism, have garnered considerable attention in recent research. Some LncRNAs regulate peroxisome protein conformation through the mechanism of RNA molecular chaperones to maintain its normal function. This study aims to investigate the role of peroxisome-related lncRNA signatures in predicting clinical outcomes and immunotherapy efficacy, as well as their correlation with drug sensitivity.

The genomic and clinical information stemmed from the Cancer Genome Atlas (TCGA) database, while the peroxisome-related genes came from relevant studies. We developed prognostic features through co-expression analysis, Cox regression analysis, and LASSO analysis. Then we classified patients into high-risk and low-risk cohorts and performed extensive model validations to assess the prognostic significance of the signature. We used RT-qPCR to detect the expression of six peroxisome-associated lncRNAs in lung adenocarcinoma cells. Following this, we analyzed immune-related functions and tumor mutation burden (TMB). Lastly, we identified potential drugs and evaluated the drug sensitivity for LUAD.

We identified six peroxisome-related lncRNAs that serve as prognostic biomarkers. Our analysis revealed that high-risk patients exhibited decreased survival rates and increased mortality. Independent prognostic evaluations, receiver operating characteristic (ROC) curves, and bar charts demonstrated that these peroxisome-related lncRNAs can effectively predict patient outcomes. RT-qPCR results also indicated that these peroxisomes showed significant differences in mRNA expression in lung normal and lung adenocarcinoma cells. Moreover, in high-risk individuals, we observed reduced immune cell infiltration, inhibited immune functions, and an elevated tumor mutational burden (TMB). Consequently, high-risk patients faced a higher likelihood of immune evasion, resulting in diminished effectiveness of immunotherapy.

In conclusion, the six lncRNAs linked to peroxisomes can reliably forecast the prognosis of LUAD patients and may offer novel perspectives for clinical applications and immunotherapy.

Peroxisomes are increasingly linked to cancer development, yet the prognostic role of peroxisome-related genes (PRGs) in breast cancer remains unclear.

This study aimed to construct a prognostic model based on PRG expression in breast cancer to clarify their prognostic value and clinical implications.

Transcriptomic data from TCGA and GEO were used for training and validation cohorts. TME characteristics were analyzed with ESTIMATE, MCP-counter, and CIBERSORT algorithms. qPCR validated mRNA expression levels of risk genes, and data analysis was conducted in R.

Univariate and multivariate Cox regression identified a 7-gene PRG risk signature (ACBD5, ACSL5, DAO, NOS2, PEX3, PEX10, and SLC27A2) predicting breast cancer prognosis in training (n=1069), internal validation (n=327), and external validation (merged from four GEO datasets, n=640) datasets. While basal and Her2 subtypes had higher risk scores than luminal subtypes, a significant prognostic impact of the PRG risk signature was seen only in luminal subtypes. The high-risk subgroup exhibited a higher frequency of focal synonymous copy number alterations (SCNAs), arm-level amplifications and deletions, and single nucleotide variations. These increased genomic aberrations were associated with greater immune suppression and reduced CD8+ T cell infiltration. Bulk RNA sequencing and single-cell analyses revealed distinct expression patterns of peroxisome-related genes (PRGs) in the breast cancer TME: PEX3 was primarily expressed in malignant and stromal cells, while ACSL5 showed high expression in T cells. Additionally, the PRG risk signature demonstrated efficacy comparable to that of well-known biomarkers for predicting immunotherapy responses. Drug sensitivity analysis revealed that the PRG high-risk subgroup was sensitive to inhibitors of BCL-2 family proteins (BCL-2, BCL-XL, and MCL1) and other kinases (PLK1, PLK1, BTK, CHDK1, and EGFR).

The PRG risk signature serves as a promising biomarker for evaluating peroxisomal activity, prognosis, and responsiveness to immunotherapy in breast cancer.

The current study's objective is to evaluate the molecular genetic mechanisms influencing the biological behavior of hepatocellular carcinoma (HCC) by analyzing the transcriptomic and epigenetic signatures of the tumors.

Transcriptomic data were downloaded from the NCBI GEO database. We investigated the expression differences between the GSE46444 (48 cirrhotic tissues versus 88 HCC tissues) and GSE63898 (168 cirrhotic tissues versus 228 HCC tissues) data sets using GEO2R. Differentially expressed genes were evaluated using GO and KEGG metabolic pathway analysis websites. Whole genome bisulfite sequencing (WGBS) and Methylated DNA Immunoprecipitation Sequencing (MeDIP-Seq) data sets (26 HCC tissues versus 26 adjacent non-tumoral tissues) were also downloaded from the NCBI SRA database. These data sets were analyzed using Bismark and QSEA, respectively. The methylation differences between the groups were assessed using functional enrichment analysis.

In the GSE46444 data set, 80 genes were upregulated, and 315 genes were downregulated in the tumor tissue (HCC tissue) compared to the non-tumor cirrhotic tissue. In the GSE63898 data set, 1261 genes were upregulated, and 458 genes were downregulated in the cirrhotic tissue compared to the tumor tissues. WGBS revealed that 20 protein-coding loci were hypermethylated. while the hypomethylated regions were non-protein-coding. The methylated residues of the tumor tissue, non-tumorous cirrhotic tissue, and healthy tissue were comparable. MeDIP-Seq, conducted on tumoral and non-tumoral tissues, identified hypermethylated or hypomethylated areas as protein-coding regions. The functional enrichment analysis indicated that these genes were related to pathways including peroxisome, focal adhesion, mTOR, RAP1, Phospholipase D, Ras, and PI3K/AKT signal transduction.

The investigation of transcriptomic and epigenetic mechanisms identified several genes significant in the biological behavior of HCC. These genes present potential targets for the development of targeted therapy.

Hepatocellular carcinoma (HCC) is one of the leading causes of death due to its complexity, heterogeneity, rapid metastasis and easy recurrence after surgical resection. We demonstrated that combination therapy with transcatheter arterial chemoembolization (TACE), hepatic arterial infusion chemotherapy (HAIC), Epclusa, Lenvatinib and Sintilimab is useful for patients with advanced HCC.

A 69-year-old man who was infected with hepatitis C virus (HCV) 30 years previously was admitted to the hospital with abdominal pain. Enhanced computed tomography (CT) revealed a low-density mass in the right lobe of the liver, with a volume of 12.9 cm × 9.4 cm × 15 cm, and the mass exhibited a "fast-in/fast-out" pattern, with extensive filling defect areas in the right branch of the portal vein and an alpha-fetoprotein level as high as 657 ng/mL. Therefore, he was judged to have advanced HCC. During treatment, the patient received three months of Epclusa, three TACE treatments, two HAIC treatments, three courses of sintilimab, and twenty-one months of lenvatinib. In the third month of treatment, the patient developed severe side effects and had to stop immunotherapy, and the Lenvatinib dose had to be halved. Postoperative pathological diagnosis indicated a complete response. The patient recovered well after the operation, and no tumor recurrence was found.

Multidisciplinary conversion therapy for advanced enormous HCC caused by HCV infection has a significant effect. Individualized drug adjustments should be made during any treatment according to the patient's tolerance to treatment.

Emerging evidence suggests that Rho GTPases play a crucial role in tumorigenesis and metastasis, but their involvement in the tumor microenvironment (TME) and prognosis of hepatocellular carcinoma (HCC) is not well understood.

We aim to develop a tumor prognosis prediction system called the Rho GTPases-related gene score (RGPRG score) using Rho GTPase signaling genes and further bioinformatic analyses.

Our work found that HCC patients with a high RGPRG score had significantly worse survival and increased immunosuppressive cell fractions compared to those with a low RGPRG score. Single-cell cohort analysis revealed an immune-active TME in patients with a low RGPRG score, with strengthened communication from T/NK cells to other cells through MIF signaling networks. Targeting these alterations in TME, the patients with high RGPRG score have worse immunotherapeutic outcomes and decreased survival time in the immunotherapy cohort. Moreover, the RGPRG score was found to be correlated with survival in 27 other cancers. In vitro experiments confirmed that knockdown of the key Rho GTPase-signaling biomarker SFN significantly inhibited HCC cell proliferation, invasion, and migration.

This study provides new insight into the TME features and clinical use of Rho GTPase gene pattern at the bulk-seq and single-cell level, which may contribute to guiding personalized treatment and improving clinical outcome in HCC.

Kidney clear cell carcinoma (KIRC) is the most common subtype of renal cell carcinoma. Peroxisomes play a role in the regulation of tumorigenesis and cancer progression, yet the prognostic significance of peroxisome-related genes (PRGs) remains rarely studied. The study aimed to establish a novel prognostic risk model and identify potential biomarkers in KIRC.

The significant prognostic PRGs were screened through differential and Cox regression analyses, and LASSO Cox regression analysis was performed to establish a prognostic risk model in the training cohort, which was validated internally in the testing and entire cohorts, and further assessed in the GSE22541 cohort. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function and pathway differences between the high-risk and low-risk groups. The relationship between risk score and immune cell infiltration levels was evaluated in the CIBERSORT, ESTIMATE and TIMER databases. Finally, potential biomarkers were identified and validated from model genes, using immunohistochemistry.

Fourteen significant prognostic PRGs were identified using multiple analyses, and 9 genes (ABCD1, ACAD11, ACAT1, AGXT, DAO, EPHX2, FNDC5, HAO1, and HNGCLL1) were obtained to establish a prognostic model via LASSO Cox regression analysis. Combining the risk score with clinical factors to construct a nomogram, which provided support for personalized treatment protocols for KIRC patients. GO and KEGG analyses highlighted associations with substance metabolism, transport, and the PPAR signaling pathways. Tumor immune infiltration indicated immune suppression in the high-risk group, accompanied by higher tumor purity and the expression of 9 model genes was positively correlated with the level of immune cell infiltration. ACAT1 has superior prognostic capabilities in predicting the outcomes of KIRC patients.

The peroxisome-related prognostic risk model could better predict prognosis in KIRC patients.

The severity of liver cirrhosis in hepatocellular carcinoma (HCC) patients is essential for determining the scope of surgical resection. It also affects the long-term efficacy of systemic anti-tumor therapy and transcatheter arterial chemoembolization (TACE). Non-invasive tools, including aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 (FIB-4), and γ-glutamyl transferase to platelet ratio (GPR), are less accurate in predicting cirrhosis in HCC patients. We aimed to build a novel decision tree model to improve diagnostic accuracy of liver cirrhosis.

The Mann-Whitney U test, χ2 test, and multivariate logistic regression analysis were used to identify independent cirrhosis predictors. A decision tree model was developed using machine learning algorithms in a training cohort of 141 HCC patients. Internal validation was conducted in 99 HCC patients. The diagnostic accuracy and calibration of the established model were evaluated using receiver operating characteristic (ROC) and calibration curves, respectively.

Sex and platelet count were identified as independent cirrhosis predictors. A decision tree model integrating imaging-reported cirrhosis, APRI, FIB-4, and GPR was established. The novel model had an excellent diagnostic performance in the training and validation cohorts, with area under the curve (AUC) values of 0.853 and 0.817, respectively. Calibration curves and the Hosmer-Lemeshow test showed good calibration of the novel model. The decision curve analysis (DCA) indicated that the decision tree model could provide a larger net benefit to predict liver cirrhosis.

Our developed decision tree model could successfully predict liver cirrhosis in HCC patients, which may be helpful in clinical decision-making.

Peroxisomal trans-2-enoyl-CoA reductase (PECR) encodes proteins related to fatty acid metabolism and synthesis. It has been confirmed that PECR has decreased expression in colon cancer and breast cancer, while the role of PECR in liver cancer is unknown. We aimed to study the role and mechanism of PECR in the genesis and development of liver cancer.

In this study, the expression of PECR was queried in the Cancer Genome Atlas Database and Western Blotting and RT-PCR experiments were carried out in paired liver cancer tissues to detect the expression of PECR. Functional tests were evaluated by cell count kit-8 (CCK-8), Flow cytometry, wound healing assay, Transwell, migration. In vivo study, we constructed a nude mouse tumorigenic model to observe the effect of PECR on the proliferation of liver cancer. And the tumor body of the mouse was taken out for histochemistry (IHC). Multiple Cox regression was used to analyze the correlation between PECR and Clinicopathology.

We confirmed that the overexpression of PECR inhibited the proliferation, migration and invasion of hepatocellular carcinoma and promoted the apoptosis of hepatocellular carcinoma. The low expression group of PECR promoted the proliferation and metastasis of liver cancer. In vivo, overexpression of PECR inhibits the proliferation of mouse tumors. In addition, the mechanism study shows that PECR may indirectly affect the proliferation of hepatocellular carcinoma cells through ERK pathway.

In general, PECR may be a new diagnostic marker and a potential therapeutic target for hepatocellular carcinoma.

Immune system instability and poor prognosis are the two major clinical performance of hepatocellular carcinoma (HCC). Abnormal expression of MiR-424-5p has been reported to accelerate the progression of liver cancer, but it mediated immune cell infiltration imbalance is still unknown. We aim to mine the immune-related genes (IRGs) targeted by miR-424-5p and construct a multi-gene signature to improve the prognostic prediction of HCC.

The HCC-related data of the cancer genome atlas (TCGA) database and the GSE14520 dataset of the gene expression omnibus (GEO) database were downloaded as the discovery dataset and the validation dataset, respectively. Weighted gene co-expression network analysis (WGCNA), the deconvolution algorithm of CIBERSORT and LASSO algorithm participated in the identification of IRGs and the development of prognostic signature and nomogram.

Our study found that the abundance of macrophages M0, M1 and M2 are all drastically changed during the cancerous process. A total of 920 macrophages infiltration-related LRGs were identified and a novel 4-gene signature (CDCA8, CBX2, UCK2 and SOCS2) with superior prognostic independence was established. The prognostic signature based risk score has superior capability to identify high-risk patients and predict overall survival (p < 0.001; AUC = 0.798 for 1 year; AUC = 0.748 for 3 years; AUC = 0.721 for 5 years). And it (C-index = 0.726) has a better prognostic potential than the TNM stage (C-index = 0.619), which is widely adopted in clinical practice. Additionally, the nomogram formed by combining the risk score and TNM stage further improved the accuracy of survival prediction (C-index = 0.733).

In summary, the immune landscape with abnormal infiltration of macrophages may be one of the prelude to the cancerous process. The novel macrophages-related 4-gene signature is expected to become a potential prognostic marker in liver cancer.