18F-FDG PET/CT metabolic parameters correlate with EIF2S2 expression status in colorectal cancer

Eukaryotic Translation Initiation Factor 2 Subunit β as a Prognostic Biomarker Associates With Immune Cell Infiltration in Breast Cancer

INTRODUCTION

The present study aims to explore the expression level of eukaryotic translation initiation factor 2 subunit β (EIF2S2) in breast cancer tissue, and its role both in breast cancer prognosis and in the immune microenvironment.

METHODS

To assess the association between the expression levels of EIF2S2 and prognosis, the Gene Expression Profiling Interactive Analysis database was initially applied to determine differences in the gene expression of EIF2S2 in various malignant and normal tissues. Furthermore, the expression levels of EIF2S2 were determined in the clinical breast cancer tissues and corresponding para-neoplastic tissues using immunohistochemical analysis. In addition, Kaplan-Meier survival and Cox regression analyses were employed to explore the association between EIF2S2 expression levels and patient prognosis. Finally, the correlation between the expression levels of EIF2S2 and immune cell infiltration in breast cancer was analyzed using the TIMER2.0 database, and subsequently validated by immunohistochemical experiments.

RESULTS

The Gene Expression Profiling Interactive Analysis database revealed the presence of higher expression levels of EIF2S2 in various different types of cancer compared with normal cells, also correlating its expression with both the age and the tumor stage of patients with breast cancer. The survival analysis results revealed that high expression levels of EIF2S2 could be a risk factor for poor prognosis in patients with breast cancer. Moreover, the EIF2S2 expression level was found to be closely associated with the infiltration levels of various immune cells, including regulatory T cells, CD4+, CD8+, and natural killer cells, in breast cancer.

CONCLUSIONS

In conclusion, the present study has demonstrated that an upregulated expression level of EIF2S2 in breast cancer may be associated with poor patient prognosis, affecting immune cell infiltration in breast cancer. Taken together, the findings of the present study have shown that EIF2S2 expression may be a novel therapeutic target for breast cancer.

SFXN1 as a potential diagnostic and prognostic biomarker of LUAD is associated with 18F-FDG metabolic parameters

BACKGROUND

Sideroflexin 1 (SFXN1) has been discovered as a novel tumor marker for lung adenocarcinoma, but data on its importance in the development of lung adenocarcinoma is still limited. This study evaluated the correlation between SFXN1 and parameters related to 18F-flurodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), and further explored the role of SFXN1 in the value-added and glycolytic processes of LUAD.

METHOD

The expression and prognostic value of SFXN1 mRNA in LUAD were analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data base. Retrospective analysis of 18F-FDG PET imaging and metabolic parameters in 42 patients to explore the relationship between the expression of SFXN1 and glucose metabolism levels in lung adenocarcinoma and its clinical significance. H1975 cells were selected as the in vitro research object, and the biological effects of SFXN1 on LUAD were further elucidated through Edu proliferation assay, CCK8 activity assay, wound healing experiment, and cell flow cytometry.

RESULT

SFXN1 is highly expressed in various tumors, including LUAD, and its high expression can serve as an independent predictor of overall survival in lung adenocarcinoma. In addition, the expression of SFXN1 in LUAD was significantly correlated with 18F-FDG PET/CT parameters: maximum and average standardized uptake values (SUVmax and SUVmean), as well as total lesion glycolysis (TLG) (rho = 0.574, 0.589, and 0.338, p < 0.05), which can predict the expression of SFXN1 with an accuracy of 0.934. In vitro functional experiments have shown that knocking down SFXN1 inhibits the proliferation and migration of LUAD cells, promotes cell apoptosis, and may inhibit tumor activity by regulating the expression of glycolytic related genes SLC2A1, HK2, GPI, ALDOA, GAPDH, ENO1, PKM, and LDHA.

CONCLUSION

The overexpression of SFXN1 is closely related to FDG uptake, and SFXN1, as a promising prognostic biomarker, may mediate the development of LUAD through the glycolytic pathway.

Data mining identifies novel RNA-binding proteins involved in colon and rectal carcinomas

Colorectal adenocarcinoma (COREAD) is the second most deadly cancer and third most frequently encountered malignancy worldwide. Despite efforts in molecular subtyping and subsequent personalized COREAD treatments, multidisciplinary evidence suggests separating COREAD into colon cancer (COAD) and rectal cancer (READ). This new perspective could improve diagnosis and treatment of both carcinomas. RNA-binding proteins (RBPs), as critical regulators of every hallmark of cancer, could fulfill the need to identify sensitive biomarkers for COAD and READ separately. To detect new RBPs involved in COAD and READ progression, here we used a multidata integration strategy to prioritize tumorigenic RBPs. We analyzed and integrated 1) RBPs genomic and transcriptomic alterations from 488 COAD and 155 READ patients, 2) ∼ 10,000 raw associations between RBPs and cancer genes, 3) ∼ 15,000 immunostainings, and 4) loss-of-function screens performed in 102 COREAD cell lines. Thus, we unraveled new putative roles of NOP56, RBM12, NAT10, FKBP1A, EMG1, and CSE1L in COAD and READ progression. Interestingly, FKBP1A and EMG1 have never been related with any of these carcinomas but presented tumorigenic features in other cancer types. Subsequent survival analyses highlighted the clinical relevance of FKBP1A, NOP56, and NAT10 mRNA expression to predict poor prognosis in COREAD and COAD patients. Further research should be performed to validate their clinical potential and to elucidate their molecular mechanisms underlying these malignancies.

High expression of HNRNPR in ESCA combined with 18F-FDG PET/CT metabolic parameters are novel biomarkers for preoperative diagnosis of ESCA

Background

The aim of this study was to determine the expression and function of heterogeneous nuclear ribonucleoprotein R (HNRNPR) in esophageal carcinoma (ESCA), the correlation between its expression and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computerized tomography scan (PET/CT)-related parameters. We also investigated whether ¹⁸F-FDG PET/CT can be used to predict the expression of HNRNPR in ESCA.

Methods

We analyzed patients with ESCA who underwent ¹⁸F-FDG PET/CT before surgery, and their tissues were stained with HNRNPR IHC. The associated parameters were derived using the ¹⁸F-FDG PET imaging data, and the correlation with the IHC score was evaluated. The Oncomine, TCGA, and GEO datasets were used to investigate HNRNPR expression in the pan- and esophageal cancers, as well as its relationship with N6-methyladenosine (m6A) modification and glycolysis. The R software, LinkedOmics, GeneMANIA, and StringOnline tools were used to perform GO/KEGG, GGI, and PPI analyses on the HNRNPR.

Results

HNRNPR is highly expressed in the majority of pan-cancers, including ESCA, and is associated with BMI, weight, and history of reflux in patients with ESCA. HNRNPR is somewhat accurate in predicting the clinical prognosis of ESCA. HNRNPR expression was positively correlated with SUV_max, SUV_mean, and TLG in ESCA (p < 0.05). The combination of these three variables provides a strong predictive value for HNRNPR expression in ESCA. GO/KEGG analysis showed that HNRNPR played a role in the regulation of cell cycle, DNA replication, and the Fannie anemia pathway. The analysis of the TCGA and GEO data sets revealed a significant correlation between HNRNPR expression and m6A and glycolysis-related genes. GSEA analysis revealed that HNRNPR was involved in various m6A and glycolysis related-pathways.

Conclusion

HNRNPR overexpression correlates with ¹⁸F-FDG uptake in ESCA and may be involved in the regulation of the cell cycle, m6A modification, and cell glycolysis. ¹⁸F-FDG PET/CT-related parameters can predict the diagnostic accuracy of HNRNPR expression in ESCA.

Relationship between standardized uptake value on 18F-FDG PET and PD-L1 expression in clear cell renal cell carcinoma

Purpose

Partial clear cell renal cell carcinoma (CCRCC) may be sensitive to immune checkpoint inhibitor treatment targeting the programmed cell death 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) pathway. Assessing the levels of PD-L1 using non-invasive imaging is useful to select immunotherapy-sensitive patients. Currently, whether PD-L1 levels in CCRCC correlate with 18F fluorodeoxyglucose (18F-FDG) uptake is unknown. This study aimed to assess whether 18F-FDG-positron emission tomography (PET) imaging could be used to infer PD-L1 levels in CCRCC.

Methods

Immunohistochemistry (IHC) was used to assess PD-L1 levels in samples of tumors obtained retrospectively from a cohort of 58 patients with CCRCC who also received 18F-FDG PET/CT imaging. The IHC scores for PD-L1 were compared with the 18F-FDG maximum standardized uptake value (SUVmax), and the mean standardized uptake value (SUVmean) value, with the clinical characteristics of CCRCC, and with the IHC scores of enzymes related to glucose metabolism (glucose transporter type 1 (GLUT1), hexokinase 2 (HK2), lactate dehydrogenase A (LDHA)), and Von Hippel-Lindau tumor suppressor (VHL).

Results

Increased renal venous invasion, lymph node metastasis, tumor size, SUVmean, and SUVmax correlated significantly with higher PD-L1 levels (P &lt; 0.05). The IHC scores of VHL and LDHA correlated positively with those of PD-L1 (P = 0.035, P = 0.011, respectively). Significant correlations between PD-L1 levels and SUVmean and lymph node metastasis were observed upon multivariate analysis. SUVmean combined with lymph node metastasis predicted that 20.59% of the low probability group would express PD-L1, 29.41% of the medium probability group would express PD-L1, and 71.43% of the high probability group would express PD-L1.

Conclusion

The status of lymph node metastasis, SUVmax, and SUVmean of the primary lesion correlated with PD-L1 levels in CCRCC. A combination of lymph node metastasis status and SUVmean could be utilized to predict PD-L1 levels, thus allowing monitoring of a tumor’s immunotherapy response.

Comprehensive Analysis of SLC17A9 and Its Prognostic Value in Hepatocellular Carcinoma

Background

Solute carrier family 17 member 9 (SLC17A9) encodes a member of a family of transmembrane proteins that are involved in the transport of small molecules. SLC17A9 is involved in the occurrence and development of various cancers, but its biological role in liver hepatocellular carcinoma (LIHC) is unclear.

Methods

The expression level of SLC17A9 was assessed using The Cancer Genome Atlas (TCGA) database and immunohistochemistry of tumor tissues and adjacent normal liver tissues. The receiver operating characteristic (ROC) and R software package performed diagnosis and prognosis. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes functional enrichment and co-expression of SLC17A9, gene–gene interaction (GGI), and protein–protein interaction (PPI) networks were performed using R, GeneMANIA, and STRING. Western blot, real-time quantitative PCR (RT-qPCR), immunofluorescence, colony formation, wound scratch assay, ATP production assays, and high connotation were applied to determine the effect of SLC17A9 knockdown on HEPG2 (hepatocellular liver carcinoma) cells. TIMER, GEPIA, and TCGA analyzed the relationship between SLC17A9 expression and immune cells, m6A modification, and ferroptosis.

Results

SLC17A9 expression in LIHC tissues was higher than in normal liver tissues (p < 0.001), and SLC17A9 was related to sex, DSS (disease-specific survival), and PFI (progression-free interval) (p = 0.015, 0.006, and 0.023). SLC17A9 expression has diagnostic (AUC: 0.812; CI: 0.770–0.854) and prognostic potential (p = 0.015) in LIHC. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) functional enrichment analysis showed that SLC17A9 was closely related to neuronal cell body, presynapse, axonogenesis, PI3K/Akt signaling pathway. GGI showed that SLC17A9 was closely related to MYO5A. PPI showed that SLC17A9 was closely related to SLC18A3. SLC17A9 silencing inhibited HepG2 cells proliferation, migration, colony formation, and reduced their ATP level. SLC17A9 expression level was related to immune cells: B cells (r = 0.094, P = 8.06E-02), CD4⁺ T cells (r = 0.184, P = 5.95E-04), and macrophages (r = 0.137, P = 1.15E-02); m6A modification: HNRNPC (r = 0.220, p < 0.001), METTL3 (r = 0.180, p < 0.001), and WTAP (r = 0.130, p = 0.009); and ferroptosis: HSPA5 (r = 0.240, p < 0.001), SLC7A11 (r = 0.180, p < 0.001), and FANCD2 (r = 0.280, p < 0.001).

Conclusion

Our data show that SLC17A9 may influence LIHC progression. SLC17A9 expression correlates with tumor immune infiltration, m6A modification, and ferroptosis in LIHC and may have diagnostic and prognostic value in LIHC.

eIF6 is potential diagnostic and prognostic biomarker that associated with 18F-FDG PET/CT features and immune signatures in esophageal carcinoma

Background

Although eukaryotic initiation factor 6 (eIF6) is a novel therapeutic target, data on its importance in the development of esophageal carcinoma (ESCA) remains limited. This study evaluated the correlation between eIF6 expression and metabolic analysis using fluorine-18 fluorodeoxyglucose (¹⁸F-FDG) -Positron emission tomography (PET) and immune gene signatures in ESCA.

Methods

This study employed The Cancer Genome Atlas (TCGA) to analyze the expression and prognostic value of eIF6, as well as its relationship with the immune gene signatures in ESCA patients. The qRT-PCR and Western blot analyses were used to profile the expression of eIF6 in ESCA tissues and different ESCA cell lines. The expression of tumor eIF6 and glucose transporter 1 (GLUT1) was examined using immunohistochemical tools in fifty-two ESCA patients undergoing routine ¹⁸F-FDG PET/CT before surgery. In addition, the cellular responses to eIF6 knockdown in human ESCA cells were assessed via the MTS, EdU, flow cytometry and wound healing assays.

Results

Our data demonstrated that compared with the normal esophageal tissues, eIF6 expression was upregulated in ESCA tumor tissues and showed a high diagnostic value with an area under curve of 0.825 for predicting ESCA. High eIF6 expression was significantly correlated with shorter overall survival of patients with esophagus adenocarcinoma (p = 0.038), but not in squamous cell carcinoma of the esophagus (p = 0.078). In addition, tumor eIF6 was significantly associated with ¹⁸F-FDG PET/CT parameters: maximal and mean standardized uptake values (SUVmax and SUVmean) and total lesion glycolysis (TLG) (rho = 0.458, 0.460, and 0.300, respectively, p < 0.01) as well as GLUT1 expression (rho = 0.453, p < 0.001). A SUVmax cutoff of 18.2 led to prediction of tumor eIF6 expression with an accuracy of 0.755. Functional analysis studies demonstrated that knockdown of eIF6 inhibited ESCA cell growth and migration, and fueled cell apoptosis. Moreover, the Bulk RNA gene analysis revealed a significant inverse association between eIF6 and the tumor-infiltrating immune cells (macrophages, T cells, or Th1 cells) and immunomodulators in the ESCA microenvironment.

Conclusion

Our study suggested that eIF6 might serve as a potential prognostic biomarker associated with metabolic variability and immune gene signatures in ESCA tumor microenvironment.

A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA

Background

Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein-protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and “CancerSubtypes” package of R software for transcriptomics can help identify biomarkers related to BLCA prognosis. This will have significant implications for prevention and treatment.

Method

BLCA data were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database (GSE13507). GSVA analysis converted the gene expression matrix to the gene set expression matrix. “CancerSubtypes” classified patients into three subtypes and established a prognostic model based on differentially expressed gene sets (DEGSs) among the three subtypes. For genes from prognosis-related DEGSs, functional and pathway enrichment analyses and PPI network analysis were carried out. The Human Protein Atlas (HPA) database was used for validation. Finally, the proportion of tumor-infiltrating immune cells (TIICs) was determined using the CIBERSORT algorithm.

Results

In total, 414 tumor samples and 19 adjacent-tumor samples were obtained from TCGA, with 145 samples belonging to subtype A, 126 samples belonging to subtype B, and 136 samples belonging to subtype C. Then, we identified 83 DEGSs and constituted a prognostic signature with two of them: “GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.” Finally, five subnets of two PPI networks were established, and nine core proteins were obtained: CDH2, COL1A1, EIF2S2, PSMA3, NAA10, DNM1L, TUBA4A, KIF11, and KIF23. The HPA database confirmed the expression of the nine core proteins in BLCA tissues. Furthermore, EIF2S2, PSMA3, DNM1L, and TUBA4A could be novel BLCA prognostic biomarkers.

Conclusions

In this study, we discovered two gene sets linked to BLCA prognosis. PPI analysis confirmed the network's core proteins, and several newly discovered biomarkers of BLCA prognosis were identified.