Is Integrin Subunit Alpha 2 Expression a Prognostic Factor for Liver Carcinoma? A Validation Experiment Based on Bioinformatics Analysis

Predictive three-biomarker panel in peripheral blood mononuclear cells for detecting hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks among the most prevalent cancers and accounts for a significant proportion of cancer-associated deaths worldwide. This disease, marked by multifaceted etiology, often poses diagnostic challenges. Finding a reliable and non-invasive diagnostic method seems to be necessary. In this study, we analyzed the gene expression profiles of 20 HCC patients, 12 individuals with chronic hepatitis, and 15 healthy controls. Enrichment analysis revealed that platelet aggregation, secretory granule lumen, and G-protein-coupled purinergic nucleotide receptor activity were common biological processes, cellular components, and molecular function in HCC and chronic hepatitis B (CHB) compared to healthy controls, respectively. Furthermore, pathway analysis demonstrated that "estrogen response" was involved in the pathogenesis of HCC and CHB conditions, while, "apoptosis" and "coagulation" pathways were specific for HCC. Employing computational feature selection and logistic regression classification, we identified candidate genes pivotal for diagnostic panel development and evaluated the performance of these panels. Subsequent machine learning evaluations assessed these panels' performance in an independent cohort. Remarkably, a 3-marker panel, comprising RANSE2, TNF-α, and MAP3K7, demonstrated the best performance in qRT-PCR-validated experimental data, achieving 98.4% accuracy and an area under the curve of 1. Our findings highlight this panel's promising potential as a non-invasive approach not only for detecting HCC but also for distinguishing HCC from CHB patients.

Targeting UBR5 inhibits postsurgical breast cancer lung metastases by inducing CDC73 and p53 mediated apoptosis

UBR5 is a HECT domain E3 ubiquitin ligase that is frequently amplified in breast, ovarian and prostate cancers. Heightened UBR5 expression plays a profound role in tumor growth through immune-dependent mechanisms; however, its mode of action in driving tumor metastasis has not been definitively delineated. Herein, we used a tetracycline (Tet)-inducible RNAi-mediated expression silencing cell system to investigate how UBR5 enables postsurgical mammary tumor metastatic growth in mouse lungs without the continuous influence of the primary lesion. In vitro, Ubr5 knockdown induces morphological and molecular changes characteristic of epithelial-mesenchymal transition (EMT). In vivo, UBR5 promotes lung metastasis in an E3 ubiquitin ligase-dependent manner. Moreover, doxycycline-induced UBR5 expression knockdown in metastatic cells in the lungs, following removing the primary tumors, resulted in increased apoptosis, decreased proliferation and prolonged survival, whereas silencing the expression of cell division cycle 73 (CDC73), a tumor suppressor and E3 ligase substrate of UBR5, reversed these effects. Transcriptome analyses revealed a prominent role of the p53 pathway in dovitinib-induced apoptosis of tumor cells differentially regulated by UBR5 and CDC73. In human triple-negative breast cancer (TNBC) patient specimens, a strong inverse correlation was observed between UBR5 and CDC73 protein levels, with reduced CDC73 expression at metastatic sites compared to primary lesions. Furthermore, a xenograft model of human TNBC recapitulated the metastatic properties and characteristics of the unique UBR5-CDC73 functional antagonism. This study reveals the novel and critical roles and intricate relationships of UBR5, CDC73 and p53 in postsurgical breast cancer metastasis and indicates the potential of targeting this pathway in cancer therapy.

Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study

Background

Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA.

Methods

We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of CRTAM in RA. In addition, comparisons of diagnostic efficacy and biological roles among CRTAM and other classic biomarkers of RA were also performed.

Results

Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including CRTAM, PTTG1IP, ITGB2, and MMP13, were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of CRTAM could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including MMP3, S100A8, S100A9, IL6, COMP, LAG3, and ENTPD1. Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes.

Conclusion

CRTAM, PTTG1IP, ITGB2, and MMP13 were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, CRTAM might fuel cancer progression through immune signals, especially among RA patients.

Stemness analysis in hepatocellular carcinoma identifies an extracellular matrix gene–related signature associated with prognosis and therapy response

Background: Tumor stemness is the stem-like phenotype of cancer cells, as a hallmark for multiple processes in the development of hepatocellular carcinoma (HCC). However, comprehensive functions of the regulators of tumor cell’s stemness in HCC remain unclear.

Methods: Gene expression data and clinical information of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) dataset as the training set, and three validation datasets were derived from Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). Patients were dichotomized according to median mRNA expression–based stemness index (mRNAsi) scores, and differentially expressed genes were further screened out. Functional enrichment analysis of these DEGs was performed to identify candidate extracellular matrix (ECM)–related genes in key pathways. A prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) to the candidate ECM genes. The Kaplan–Meier curve and receiver operating characteristic (ROC) curve were used to evaluate the prognostic value of the signature. Correlations between signatures and genomic profiles, tumor immune microenvironment, and treatment response were also explored using multiple bioinformatic methods.

Results: A prognostic prediction signature was established based on 10 ECM genes, including TRAPPC4, RSU1, ILK, LAMA1, LAMB1, FLNC, ITGAV, AGRN, ARHGEF6, and LIMS2, which could effectively distinguish patients with different outcomes in the training and validation sets, showing a good prognostic prediction ability. Across different clinicopathological parameter stratifications, the ECMs signature still retains its robust efficacy in discriminating patient with different outcomes. Based on the risk score, vascular invasion, α-fetoprotein (AFP), T stage, and N stage, we further constructed a nomogram (C-index = 0.70; AUCs at 1-, 3-, and 5-year survival = 0.71, 0.75, and 0.78), which is more practical for clinical prognostic risk stratification. The infiltration abundance of macrophages M0, mast cells, and Treg cells was significantly higher in the high-risk group, which also had upregulated levels of immune checkpoints PD-1 and CTLA-4. More importantly, the ECMs signature was able to distinguish patients with superior responses to immunotherapy, transarterial chemoembolization, and sorafenib.

Conclusion: In this study, we constructed an ECM signature, which is an independent prognostic biomarker for HCC patients and has a potential guiding role in treatment selection.

Identification and Validation of Novel Biomarkers for Hepatocellular Carcinoma, Liver Fibrosis/Cirrhosis and Chronic Hepatitis B via Transcriptome Sequencing Technology

Purpose

The aim of this study was to identify and validate novel biomarkers for distinguishing among hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), liver fibrosis/liver cirrhosis (LF/LC) and chronic hepatitis B (CHB).

Patients and Methods

Transcriptomic sequencing was conducted on the liver tissues of 5 patients with HCC, 5 patients with LF/LC, 5 patients with CHB, and 4 healthy controls. The expression levels of selected mRNAs and proteins were assessed by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC) staining, and were verified in validation set (n=200) and testing set (n=400) via enzyme-linked immunosorbent assay (ELISA).

Results

A total of 9 hub mRNAs were identified by short time-series expression miner and weighted gene co-expression network analysis. Of note, the results of qRT-PCR and IHC staining demonstrated that SHC adaptor protein 1 (SHC1), SLAM family member 8 (SLAMF8), and interleukin-32 (IL-32) exhibited gradually increasing trends in the four groups. Subsequent ELISA tests on the validation cohort indicated that the plasma levels of SHC1, SLAMF8 and IL-32 also gradually increased. Furthermore, a diagnostic model APFSSI (age, PLT, ferritin, SHC1, SLAMF8 and IL-32) was established to distinguish among CHB, LF/LC and HCC. The performance of APFSSI model for discriminating CHB from healthy subjects (AUC=0.966) was much greater compared to SHC1 (AUC=0.900), SLAMF8 (AUC=0.744) and IL-32 (AUC=0.821). When distinguishing LF/LC from CHB, APFSSI was the most outstanding diagnostic parameter (AUC=0.924), which was superior to SHC1, SLAMF8 and IL-32 (AUC=0.812, 0.684 and 0.741, respectively). Likewise, APFSSI model with the greatest AUC value displayed an excellent performance for differentiating between HCC and LF/LC than other variables (SHC1, SLAMF8 and IL-32) via ROC analysis. Finally, the results in the test set were consistent with those in the validation set.

Conclusion

SHC1, SLAMF8 and IL-32 can differentiate among patients with HCC, LF/LC, CHB and healthy controls. More importantly, the APFSSI model greatly improves the diagnostic accuracy of HBV-associated liver diseases.

Overexpressed integrin alpha 2 inhibits the activation of the transforming growth factor β pathway in pancreatic cancer via the TFCP2-SMAD2 axis

Background

Integrin alpha 2 (ITGA2) has been recently reported to be an oncogene and to play crucial roles in tumor cell proliferation, invasion, metastasis, and angiogenesis. Our previous study showed that ITGA2 was overexpressed in pancreatic cancer and promoted its progression. However, the mechanism of ITGA2 overexpression and other mechanisms for promoting the progression of pancreatic cancer are still unclear.

Methods

The GEPIA database was used to confirm the expression of ITGA2 in pancreatic cancer. To verify the influence of ITGA2 and TGF-β on the morphological changes of pancreatic cancer and tumor cell progression, we conduct CCK8 test, plate cloning, flow cytometry experiments and animal experiments. Then we conduct Western blot, RT-qPCR to explore the relationship between ITGA2 and TGF-β, and then find the key molecules which can regulate them by immunoprecipitation, Western blot, RT-qPCR, CHIP, nuclear and cytoplasmic separation test.

Results

The results of the present study show that the abnormal activation of KRAS induced the overexpression of ITGA2 in pancreatic cancer. Moreover, ITGA2 expression significantly suppressed the activation of the TGF-β pathway. ITGA2 silencing enhanced the anti-pancreatic cancer proliferation and tumor growth effects of TGF-β. Mechanistically, ITGA2 expression suppressed the activation of the TGF-β pathway by inhibiting the SMAD2 expression transcriptionally. In addition, it interacted with and inhibited the nuclear translocation of TFCP2, which induced the SMAD2 expression as a transcription factor. Furthermore, TFCP2 also induced ITGA2 expression as a transcription factor, and the TFCP2 feedback regulated the ITGA2-TFCP2-SMAD2 pathway.

Conclusions

Taken together, these results indicated that ITGA2 expression could inhibit the activation of the TGF-β signaling pathway in pancreatic cancer via the TFCP2-SMAD2 axis. Therefore, ITGA2, by effectively enhancing the anti-cancer effects of TGF- β, might be a potential clinical therapeutic target for pancreatic cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02286-5.

Expression and prognostic impact of CD49b in human lung cancer

Lung cancer remains the worldwide leading cause of cancer-related death. Currently, prognostic biomarkers for the detection and stratification of lung cancer are being investigated for clinical use. The surface protein cluster of differentiation 49b (CD49b) plays an important role in promoting cell proliferation and invasion in different tumor entities and blocking CD49b improved the tumor immune response. Overexpression of CD49b has been associated with unfavorable survival rates in several malignant tumor entities, such as prostate cancer, gastric cancer and colon cancer. Therefore, we aimed to analyze the protein expression of CD49b in patients with different types of lung cancer and additionally to identify the influence of CD49b on clinicopathological characteristics and overall survival.Expression levels of CD49b were retrospective analyzed by immunohistochemistry in 92 cases of pulmonary adenocarcinoma (AC), 85 cases of squamous cell lung carcinoma (SQCLC) and 32 cases of small cell lung cancer (SCLC) and correlated with clinicopathological characteristics and patients' overall survival.A strong expression of CD49b was most seen in SQCLC (78%), followed by AC (48%) and SCLC (9%). All patients combined, strong expression of CD49b correlated significantly with poorer overall survival. However, an increased expression of CD49b correlated significantly with a poorer survival rate only in SQCLC. In AC and SCLC, no significant correlation could be demonstrated in this regard.In our study, CD49b expression was associated with poor overall survival in patients with SQCLC. Accordingly, CD49b could serve as a new prognostic biomarker and, moreover, be a potential new drug target in SQCLC.

Expression and Prognostic Analysis of Integrins in Gastric Cancer

Background

Integrins are involved in the biological process of a variety of cancers, but their importance in the diagnosis and prognosis of gastric cancer (GC) is still unclear. Therefore, this study aimed at exploring the significance of ITG gene expression in GC to evaluate its diagnosis and prognosis.

Methods

GEPIA data were used to evaluate the mRNA expression of ITG genes in GC patients. The prognostic value of these genes was assessed by analyzing their mRNA expression using the Kaplan–Meier curve. The biological function of ITG genes was evaluated by GC tissue sequencing combined with GSEA bioinformatics. Based on the sequencing data, ITGA5 with the largest expression difference was selected for verification, and RT-PCR was used to verify its mRNA expression level in 40 pairs of GC and normal tissues.

Results

ITG (A2, A3, A4, A5, A6, A11, AE, AL, AM, AV, AX, B1, B2, B4, B5, B6, and B8) was highly expressed in GC tissues, while ITGA8 was low, compared with their expression in normal tissues. RNA-seq data shows that ITG (A2, A5, A11, AV, and B1) expression was associated with poor prognosis and overall survival. In addition, combined with the results of GC tissue mRNA sequencing, it was further found that the differentially expressed genes in the ITGs genes. ITGA5 was highly expressed in GC tissues compared with its expression in normal tissues, as evaluated by qRT–PCR (P < 0.001) and ROC (P < 0.001, AUC (95% CI) = 0.747 (0.641–0.851)), and confirmed that ITGA5 expression was a potential diagnostic marker for GC. Bioinformatics analysis revealed that the signaling pathway involved in ITGA5 was mainly enriched in focal adhesion, ECM-receptor interaction, and PI3K-AKT and was mainly involved in biological processes such as cell adhesion, extracellular matrix, and cell migration.

Conclusion

This study suggested that ITGs were associated with the diagnosis and prognosis of GC and discovered the prognostic value and biological role of ITGA5 in GC. Thus, ITGA5 might be used as a potential diagnostic marker for GC.

Identification of the potential biomarkers in patients with glioma: a weighted gene co-expression network analysis

Glioma is the most common brain tumor with high mortality. However, there are still challenges for the timely and accurate diagnosis and effective treatment of the tumor. One hundred and twenty-one samples with grades II, III and IV from the Gene Expression Omnibus database were used to construct gene co-expression networks to identify hub modules closely related to glioma grade, and performed pathway enrichment analysis on genes from significant modules. In gene co-expression network constructed by 2345 differentially expressed genes from 121 gene expression profiles for glioma, we identified the black and blue modules that associated with grading. The module preservation analysis based on 118 samples indicates that the two modules were replicable. Enrichment analysis showed that the extracellular matrix genes were enriched for blue module, while cell division genes were enriched for black module. According to survival analysis, 21 hub genes were significantly up-regulated and one gene was significantly down-regulated. What’s more, IKBIP, SEC24D, and FAM46A are the genes with little attention among the 22 hub genes. In this study, IKBIP, SEC24D, and FAM46A related to glioma were mentioned for the first time to the current knowledge, which might provide a new idea for us to study the disease in the future. IKBIP, SEC24D and FAM46A among the 22 hub genes identified that are related to the malignancy degree of glioma might be used as new biomarkers to improve the diagnosis, treatment and prognosis of glioma.