Serum Proteins, HMMR, NXPH4, PITX1 and THBS4; A Panel of Biomarkers for Early Diagnosis of Hepatocellular Carcinoma

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

High expression of NXPH4 correlates with poor prognosis, metabolic reprogramming, and immune infiltration in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a prevalent gastrointestinal malignant disease with high mortality rate, and identification of novel prognostic biomarkers and therapeutic targets is urgently needed. Although neurexophilin 4 (NXPH4) has been investigated in several tumors, its role in COAD remains unclear. The aim of this study was to explore the prognostic value and potential functions of NXPH4 in COAD.

Methods

The expression of NXPH4 in COAD were analyzed using The Cancer Genome Atlas (TCGA) and datasets from the Gene Expression Omnibus (GEO) database. The prognostic value of NXPH4 was determined using Kaplan-Meier analysis and Cox regression analysis. To investigate the possible mechanism underlying the role of NXPH4 in COAD, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were employed. The correlation between NXPH4 expression and immune cell infiltration levels was examined thorough single-sample gene set enrichment analysis (ssGSEA). Furthermore, the competing endogenous RNA (ceRNA) regulatory network that may be involved in NXPH4 in COAD was predicted and constructed through a variety of databases.

Results

NXPH4 expression was significantly higher in COAD tissue compared with normal colon tissues. Meanwhile, high expression of NXPH4 was associated with poor prognosis in COAD patients. GO-KEGG and GSEA analyses indicated that NXPH4 was associated with glycolysis and hypoxia pathway, and may promote COAD progression and metastasis by modulating metabolic reprogramming. ssGSEA analysis demonstrated that NXPH4 expression also associated with immune infiltration. Furthermore, we identified various microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) as upstream regulators of NXPH4 in COAD.

Conclusions

The present study revealed that high expression of NXPH4 is associated with tumor progression, metabolic reprogramming, and immune infiltration. These findings suggest that NXPH4 could serve as a reliable prognostic biomarker and a promising therapeutic target in COAD.

NXPH4 can be used as a biomarker for pan-cancer and promotes colon cancer progression

NXPH4 promotes cancer proliferation and invasion. However, its specific role and mechanism in cancer remain unclear. Transcriptome and clinical data for pan-cancer were derived from the TCGA database. K-M survival curve and univariate Cox were used for prognostic analysis. CIBERSORT and TIMER algorithms were employed to calculate immune cell infiltration. Gene set enrichment analysis (GSEA) was employed for investigating the function of NXPH4. Western blot verified differential expression of NXPH4 in colon cancer. Functional assays (CCK-8, plate clonogenicity assay, wound healing assay, and Transwell assay) confirmed the impact of NXPH4 on proliferation, invasion, and migration of colon cancer cells. Dysregulation of NXPH4 in pan-cancer suggests its potential as a diagnostic and prognostic marker for certain cancers, including colon and liver cancer. High expression of NXPH4 in pan-cancer might be associated with the increase in copy number and hypomethylation. NXPH4 expression in pan-cancer is substantially linked to immune cell infiltration in the immune microenvironment. NXPH4 expression is associated with the susceptibility to immunotherapy and chemotherapy. Western blot further confirmed the higher expression of NXPH4 in colon cancer. Knockdown of NXPH4 significantly suppresses proliferation, invasion, and migration of colon cancer cell lines HT-29 and HCT116, as validated by functional assays.

PITX1 plays essential functions in cancer

PITX1, also known as the pituitary homeobox 1 gene, has emerged as a key regulator in animal growth and development, attracting significant research attention. Recent investigations have revealed the implication of dysregulated PITX1 expression in tumorigenesis, highlighting its involvement in cancer development. Notably, PITX1 interacts with p53 and exerts control over crucial cellular processes including cell cycle progression, apoptosis, and chemotherapy resistance. Its influence extends to various tumors, such as esophageal, colorectal, gastric, and liver cancer, contributing to tumor progression and metastasis. Despite its significance, a comprehensive review examining PITX1's role in oncology remains lacking. This review aims to address this gap by providing a comprehensive overview of PITX1 in different cancer types, with a particular focus on its clinicopathological significance.

An immune and epigenetics-related scoring model and drug candidate prediction for hepatic carcinogenesis via dynamic network biomarker analysis and connectivity mapping

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. This study aimed to build a prognostic signature for HCC patients based on immune-related genes (IRGs) and epigenetics-related genes (EPGs). RNA-seq data from Gene Expression Omnibus were used for dynamic network biomarker (DNB) analysis to identify 56 candidate IRG-EPG-DNBs and their first-neighbor genes. These genes were screened using LASSO-Cox regression analysis to finally obtain five candidate genes-RNF2, YBX1, EZH2, CAD, and PSMD1-which constituted the prognostic signature panel. According to this panel, patients in The Cancer Genome Atlas and International Cancer Genome Consortium were divided into high- and low-risk groups. The prognosis, clinicopathological features, and immune cell infiltration significantly differed between the two risk groups. The prognostic ability of the signature panel and expression profiling were further validated using online databases. We used an independent cohort of patients to validate the expression profiles of the five genes using reverse transcription-PCR. CMap and CellMiner predicted four small molecule drug-protein pairs based on the five prognostic genes. Of them, two market drugs approved by the Food and Drug Administration (AT-13387 and KU-55933) have emerged as candidates for HCC study. This new signature panel may serve as a potential prognostic marker, engendering the possibility of novel personalized therapy with classification of HCC patients.

Forkhead box protein K1‑regulated neurexophilin 4 promotes proliferation, metastasis and glycolysis in colorectal cancer

Colorectal cancer (CRC) is a common malignant tumor. At present, the in-depth study of the formation, development and treatment of CRC at the molecular and gene levels is a research hot spot. Neurexophilin 4 (NXPH4) expression has been revealed to be abnormally elevated in several types of cancer, but its expression in CRC has not yet been reported. First, relevant databases were used to predict the expression of NXPH4 in CRC and its association with the survival rate of patients with CRC. Subsequently, the expression of NXPH4 in CRC cells was verified through cell experiments. Cell Counting Kit-8, 5-ethynyl-2'-deoxyuridine staining, flow cytometry, wound healing assay, Transwell assay, western blotting and the kits were used to detect the effects of NXPH4 knockdown in CRC cells on cell proliferation, invasion, migration and glycolysis. The association between NXPH4 and forkhead box protein K1 (FOXK1) was predicted using the JASPAR database, and verified through luciferase reporter gene and chromatin immunoprecipitation experiments. The NXPH4 regulation mechanism was also discussed. NXPH4 was revealed to be highly expressed in CRC. NXPH4 knockdown in CRC cells could significantly inhibit cell proliferation and induce apoptosis. NXPH4 knockdown inhibited cell invasion, migration and glycolysis. The aforementioned process could be reversed by further FOXK1 overexpression in CRC cells. In conclusion, FOXK1-regulated NXPH4 promotes proliferation, metastasis and glycolysis in CRC.

Construction of a Novel Diagnostic Model Based on Ferroptosis-Related Genes for Hepatocellular Carcinoma Using Machine and Deep Learning Methods

Hepatocellular carcinoma (HCC) is one of the most general malignant tumors. Ferroptosis, a type of necrotic cell death that is oxidative and iron-dependent, has a strong correlation with the development of tumors and the progression of cancer. The present study was designed to identify potential diagnostic Ferroptosis-related genes (FRGs) using machine learning. From GEO datasets, two publicly available gene expression profiles (GSE65372 and GSE84402) from HCC and nontumor tissues were retrieved. The GSE65372 database was used to screen for FRGs with differential expression between HCC cases and nontumor specimens. Following this, a pathway enrichment analysis of FRGs was carried out. In order to locate potential biomarkers, an analysis using the support vector machine recursive feature elimination (SVM-RFE) model and the LASSO regression model were carried out. The levels of the novel biomarkers were validated further using data from the GSE84402 dataset and the TCGA datasets. In this study, 40 of 237 FRGs exhibited a dysregulated level between HCC specimens and nontumor specimens from GSE65372, including 27 increased and 13 decreased genes. The results of KEGG assays indicated that the 40 differential expressed FRGs were mainly enriched in the longevity regulating pathway, AMPK signaling pathway, the mTOR signaling pathway, and hepatocellular carcinoma. Subsequently, HSPB1, CDKN2A, LPIN1, MTDH, DCAF7, TRIM26, PIR, BCAT2, EZH2, and ADAMTS13 were identified as potential diagnostic biomarkers. ROC assays confirmed the diagnostic value of the new model. The expression of some FRGs among 11 FRGs was further confirmed by the GSE84402 dataset and TCGA datasets. Overall, our findings provided a novel diagnostic model using FRGs. Prior to its application in a clinical context, there is a need for additional research to evaluate the diagnostic value for HCC.

Increased Expression of NXPH4 Correlates with Immune Cell Infiltration and Unfavorable Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the leading malignant carcinomas. Despite the advancement in the treatment for HCC, such as precise hepatectomy, radiotherapy, transarterial therapies, chemotherapy, targeted treatments, and immunotherapy, the 5-year overall survival rate of HCC is extremely low. Hence, novel biomarkers are urgently needed for advancing the therapy and prognosis of HCC. Neurexophilin 4 (NXPH4) is a neuropeptide-like glycoprotein. The study is designed to investigate the function of NXPH4 in HCC through a comprehensive bioinformatics analysis. NXPH4 expression status and prognostic values were analyzed via multiple datasets, such as TCGA, GEO, and ICGC. The association between NXPH4 and immune cell infiltration was estimated by TIMER, TISIDB, and CIBERSORT. In vitro, we explored the biological function of NXPH4 in JHH7 and SNU182 cells through knocking down the expression of NXPH4 via siRNA. In general, NXPH4 was predominantly upregulated in HCC tumors, and increased NXPH4 expression predicted unfavorable prognosis. The gene enrichment analysis displayed that NXPH4 was related with metabolic pathways. NXPH4 expression was correlated with immune cell infiltration. NXPH4 knockdown significantly suppressed proliferation, migration, and invasion of JHH7 and SNU182 cells. This study suggested that the upregulation of NXPH4 is associated with adverse prognosis and immune cell infiltration in HCC. NXPH4 could be a novel biomarker of unfavorable prognosis and an underlying target for immunotherapy in HCC.

NXPH4 Used as a New Prognostic and Immunotherapeutic Marker for Muscle-Invasive Bladder Cancer

Background

One of the most common malignant tumors of the urinary system is muscle-invasive bladder cancer (MIBC). With the increased use of immunotherapy, its importance in the field of cancer is becoming abundantly evident. This study classifies MIBC according to GSVA score from the perspective of the GSEA immune gene set.

Methods

This study integrated the sequencing and clinical data of MIBC patients in TCGA and GEO databases, then scored the data using the GSVA algorithm, the CNMF algorithm was implemented to divide the subtypes of GEO and TCGA datasets, respectively, and finally screened and determined the key pathways in combination with clinical data. Simultaneously, LASSO Cox regression model was constructed based on key pathway genes to assess the model's predictive ability (ROC) and describe the immune landscape differences between high- and low-risk groups; key genes were further analyzed and verified in patient tissues.

Results

404 TCGA and 297 GEO datasets were divided into C1-3 groups (TCGA-C1:120/C2:152/C3:132; GEO- C1:112/C2:101/C3:84), of which TCGA-C2 (n = 152) subtype and GEO-C1 (n = 112) subtype had the worst prognosis. LASSO Cox regression model with ROC (train set = 0.718, test set = 0.667) could be constructed. When combined with the Cancer Immunome Atlas database, it was found that patients with high-risk scores were more sensitive to PD-1 inhibitor and PD-1 inhibitor combined with CTLA-4. NXPH4, as a key gene, plays a role in MIBC with tissue validation results show that nxph4 is highly expressed in tumor.

Conclusion

The immune gene score of MIBC data in TCGA and GEO databases was successfully evaluated using GSVA in this research. The lasso Cox expression model was successfully constructed by screening immune genes, the high-risk group had a worse prognosis and higher sensitivity to immunotherapy, PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors can be preferentially used in high-risk patients who are sensitive to immunotherapy, and NXPH4 may be a molecular target to adjust the effect of immunotherapy.