Identification of Nkx2-3 and TGFB1I1 expression levels as potential biomarkers to predict the effects of FOLFOX4 chemotherapy

Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes

Objective

Increasing studies have indicated that senescence was associated with tumorigenesis and progression. Lower-grade glioma (LGG) presented a less invasive nature, however, its treatment efficacy and prognosis prediction remained challenging due to the intrinsic heterogeneity. Therefore, we established a senescence-related signature and investigated its prognostic role in LGGs.

Methods

The gene expression data and clinicopathologic features were from The Cancer Genome Atlas (TCGA) database. The experimentally validated senescence genes (SnGs) from the CellAge database were obtained. Then LASSO regression has been performed to build a prognostic model. Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score. A nomogram model has been constructed for outcome prediction. Immunological analyses were further performed. Data from the Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data (REMBRANDT), and GSE16011 were used for validation.

Results

The 6-SnG signature has been established. The results showed SnG-risk score could be considered as an independent predictor for LGG patients (HR=2.763, 95%CI=1.660-4.599, P<0.001). The high SnG-risk score indicated a worse outcome in LGG (P<0.001). Immune analysis showed a positive correlation between the SnG-risk score and immune infiltration level, and the expression of immune checkpoints. The CGGA datasets confirmed the prognostic role of the SnG-risk score. And Kaplan-Meier analyses in the additional datasets (CGGA, REMBRANDT, and GSE16011) validated the prognostic role of the SnG-signature (P<0.001 for all).

Conclusion

The SnG-related prognostic model could predict the survival of LGG accurately. This study proposed a novel indicator for predicting the prognosis of LGG and provided potential therapeutic targets.

Coexpression network analysis identified lncRNAs-mRNAs with potential relevance in African ancestry prostate cancer

AIM

This study aims to investigate similarities and differences using lncRNA and mRNA coexpression network analysis in African ancestry (AA) and European ancestry (EA) among prostate cancer (PCa) patients.

METHODS

We performed weighted gene coexpression network analysis of the expression from 49 of AA and 49 of EA to identify lncRNAs-mRNAs.

RESULTS

27 lncRNAs and 36 mRNAs were highly expressed in patients of AA. Two mRNAs and their antisense lncRNAs were expressed. Additionally, seven mRNAs were DE or coexpressed and had an impact on survival.

CONCLUSION

We present a list of lncRNAs and mRNAs that were DE and coexpressed when comparing patients of AA and EA, and these data are a resource for future studies to understand the role of lncRNAs.

An aberrant DNA methylation signature for predicting the prognosis of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy worldwide with a poor prognosis. DNA methylation is an epigenetic modification that plays a critical role in the etiology and pathogenesis of HNSCC. The current study aimed to develop a predictive methylation signature based on bioinformatics analysis to improve the prognosis and optimize therapeutic outcome in HNSCC. Clinical information and methylation sequencing data of patients with HNSCC were downloaded from The Cancer Genome Atlas database. The R package was used to identify differentially methylated genes (DMGs) between HNSCC and adjacent normal tissues. We identified 22 DMGs associated with 246 differentially methylated sites. Patients with HNSCC were classified into training and test groups. Cox regression analysis was used to build a risk score formula based on the five methylation sites (cg26428455, cg13754259, cg17421709, cg19229344, and cg11668749) in the training group. The Kaplan–Meier survival curves showed that the overall survival (OS) rates were significantly different between the high‐ and low‐risk groups sorted by the signature in the training group (median: 1.38 vs. 1.57 years, log‐rank test, p < 0.001). The predictive power was then validated in the test group (median: 1.34 vs. 1.75 years, log‐rank test, p < 0.001). The area under the receiver operating characteristic curve (area under the curve) based on the signature for predicting the 5‐year survival rates, was 0.7 in the training and 0.73 in test groups, respectively. The results of multivariate Cox regression analysis showed that the riskscore (RS) signature based on the five methylation sites was an independent prognostic tool for OS prediction in patients. In addition, a predictive nomogram model that incorporated the RS signature and patient clinical information was developed. The innovative methylation signature‐based model developed in our study represents a robust prognostic tool for guiding clinical therapy and predicting the OS in patients with HNSCC.

Novel Autophagy-Related Gene Signature Investigation for Patients With Oral Squamous Cell Carcinoma

The correlation between autophagy defects and oral squamous cell carcinoma (OSCC) has been previously studied, but only based on a limited number of autophagy-related genes in cell lines or animal models. The aim of the present study was to analyze differentially expressed autophagy-related genes through The Cancer Genome Atlas (TCGA) database to explore enriched pathways and potential biological function. Based on TCGA database, a signature composed of four autophagy-related genes (CDKN2A, NKX2-3, NRG3, and FADD) was established by using multivariate Cox regression models and two Gene Expression Omnibus datasets were applied for external validation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to study the function of autophagy-related genes and their pathways. The most significant GO and KEGG pathways were enriched in several key pathways that were related to the progression of autophagy and OSCC. Furthermore, a prognostic risk score was constructed based on the four genes; patients were then divided into two groups (i.e., high risk and low risk) in terms of the median of risk score. Prognosis of the two groups and results showed that patients at the low-risk group had a much better prognosis than those at the high-risk group, regardless of whether they were in the training datasets or validation datasets. Multivariate Cox regression results indicated that the risk score of the autophagy-related gene signatures could greatly predict the prognosis of patients after controlling for several clinical covariates. The findings of the present study revealed that autophagy-related gene signatures play an important role in OSCC and are potential prognostic biomarkers and therapeutic targets.

Identification of a novel six autophagy-related genes signature for the prognostic and a miRNA-related autophagy predictor for anti-PD-1 therapy responses in prostate cancer

Background

Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear.

Methods

An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2–3). Based on STRING analysis results, we found that the NKX2–3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2–3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2–3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa.

Results

We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2–3). Further analysis demonstrated that NKX2–3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa.

Conclusions

The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

Significance of TP53 mutation in treatment and prognosis in head and neck squamous cell carcinoma

Background:TP53 is ranked as the most common mutated gene in head and neck squamous cell carcinoma (HNSCC). Results: The status of TP53 mutation was investigated on International Cancer Genome Consortium and The Cancer Genome Atlas database and TP53-related differentially expressed genes were screened out from transcriptome data and mutation information. A TP53-related prognostic gene signature (TIMP4, ONECUT2, CGNL1, DMRTA2 and NKX2.3) was constructed based on Cox regression analysis and LASSO algorithm. Univariate and multivariate analyses were carried out to identify promising prognosticators for HNSCC. Conclusion: Our findings provide a well-rounded landscape of TP53 mutation in HNSCC and pave the groundwork for developing innovative and effective cancer treatment methods for HNSCC.

Autophagy-related genes are potential diagnostic and prognostic biomarkers in prostate cancer

Background

Recently, autophagy was found related to several malignances.

Methods

To explore the diagnostic and prognostic values of autophagy in prostate cancer (PCa), we first identified differentially expressed autophagy-related genes (DEARGs) based on The Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) dataset. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were applied to perform gene functional annotation. Then, multivariate logistic regression analysis was applied to construct the risk index (RI). Receiver operating characteristic (ROC), calibration and decision curve analysis (DCA) curves were performed to identify the effectiveness of RI. Next, multivariate Cox regression analyses were performed to construct the prognostic index (PI) and autophagy-clinical prognostic index (ACPI).

Results

We identified 16 DEARGs and functional annotation demonstrated the relevance of these genes to autophagy and revealed the association of these DEARGs with digestive system, drug resistance and apoptosis. Then, the RI was constructed based on 5 DEARGs and the area under the ROC curve (AUC) was 0.9858. Validation based on Gene Expression Omnibus (GEO) datasets suggested that the RI was effective. Next, 7 ARGs were identified associated with overall survival (OS) and the PI was developed composed of 3 ARGs. Finally, ACPI was constructed based on PI and the M stage.

Conclusions

This study provided potential models for predicting the risk and prognosis of PCa and indicated the molecular insights of autophagy in PCa. While no other dataset was applied to test the effectiveness of the PI and ACPI models attribute to the well prognosis of PCa.

Development of a Prognostic Signature Based on Autophagy-related Genes for Head and Neck Squamous Cell Carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor with relative low survival rate. Increasingly evidences have emphasized the importance of autophagy in cancer initiation, progression, and the responses to cancer treatment.

AIM OF THE STUDY

This study aimed to investigate the potential biological and prognostic significance of autophagy-related genes (ARGs) in HNSCC patients.

METHODS

We collected a list of ARGs from Human Autophagy Database and obtained expression profiles and clinical information of HNSCC samples from the Cancer Genome Atlas (TCGA) portal. Differential expression analysis and functional enrichment analysis were performed by R software. The prognostic value of differentially expressed ARGs was detected by Cox regression analysis and prognosis-related ARGs were subjected to LASSO regression analysis. Univariate and multivariate Cox regression analysis were applied to identify promising independent prognosticators for HNSCC.

RESULTS

A total of 35 differentially expressed ARGs were screened out and functional enrichment analysis results indicated these genes were mainly associated with autophagy-related biological processes and pathways. Seven prognosis-related ARGs (ITGA3, CDKN2A, FADD, NKX2-3, BAK1, CXCR4, and HSPB8) were selected to construct a risk signature, which proved to be effective in predicting the survival rate of HNSCC patients. Moreover, univariate analysis showed risk score, tumor stage, T stage, and N stage were negatively correlated with patient overall survival and the multivariate Cox regression analysis results indicated risk score, age, and N stage was significantly associated with patient prognosis.

CONCLUSIONS

Our findings may provide novel evidences for the diagnosis and prognosis evaluation for HNSCC.

Response prediction to oxaliplatin plus 5-fluorouracil chemotherapy in patients with colorectal cancer using a four-protein immunohistochemical model

The response of cancer patients to oxaliplatin combined with 5-fluorouracil (5-FU) is difficult to predict. It has been reported that carcinoma-associated fibroblasts (CAFs) could induce AKT and ERK phosphorylation, and upregulate survivin expression in colorectal cancer (CRC) cells, which could lead to oxaliplatin plus 5-FU resistance. A total of 71 patients with advanced CRC (aCRC) treated with oxaliplatin plus 5-FU were included in the present study. These patients comprised 46 chemotherapy responders and 25 non-responders. The expression levels of α-smooth muscle actin (α-SMA), phosphorylated (p)-AKT, p-ERK and survivin were determined by immunohistochemical evaluation of paraffin-embedded samples from patients. A predictive model was established using a Probabilistic Neural Network model. The high expression of α-SMA, p-AKT and survivin in patients with aCRC were associated with oxaliplatin plus 5-FU resistance (P<0.001, P=0.023 and P=0.001, respectively). Furthermore, patients with stage IV CRC exhibiting high expression levels of α-SMA and survivin experienced a reduced progression-free survival time compared with patients with low expressions of α-SMA and survivin (5.5 vs. 15.0 months; 5.5 vs. 15.0 months; P=0.005 and P=0.001, respectively). Stage IV CRC and high survivin expression predicted a reduced overall survival time compared with that for patients with stage IV CRC and low survivin expression (50.0 vs. 15.0 months; P<0.001). Patients with α-SMA, p-AKT, p-ERK and survivin overexpression were more likely to present with intrinsic resistance to the oxaliplatin plus 5-FU regimen (the accuracies of modeling, validation and prediction were 83.7, 92.9 and 85.7%, respectively). In conclusion, the multifactorial predictive biomarker model of α-SMA, p-AKT, p-ERK and survivin expression for patients with aCRC to predict intrinsic resistance to oxaliplatin plus 5-FU regimens is of great efficiency and accuracy. Patients with high expression of this predictive model may be intrinsically resistant to the oxaliplatin and 5-FU regimen.

Gene signatures associated with drug resistance to irinotecan and oxaliplatin predict a poor prognosis in patients with colorectal cancer

The identification of novel survival predictors may help to improve the appropriate management of colorectal cancer (CRC). In the present study, two gene sets associated with irinotecan or oxaliplatin resistance in CRC cell lines were first identified and subsequently applied to the clinical CRC microarray dataset GSE14333. Subsequently, a 60-gene irinotecan resistance-associated signature and a 13-gene oxaliplatin resistance-associated signature were established, which were able to classify CRC patients into high- and low-risk subgroups with varied clinical outcomes [irinotecan-resistance gene signature: hazard ratio (HR)=0.4607, 95% confidence interval (CI)=0.3369-0.6300, P<0.0001; oxaliplatin-resistance gene signature: HR=0.6119, 95% CI=0.4547-0.8233, P=0.0008]. The performance of these two gene expression signatures in predicting outcome risk were also validated in two other independent CRC gene expression microarray datasets, GSE17536 (irinotecan-resistance gene signature: HR=0.5318, 95% CI=0.3359-0.8419, P=0.0079; oxaliplatin-resistance gene signature: HR=0.5383, 95% CI=0.3400-0.8521, P=0.0114) and GSE17537 (irinotecan-resistance gene signature: HR=0.2827, 95% CI=0.1173-0.6813, P=0.0088; oxaliplatin-resistance gene signature: HR=0.2378, 95% CI=0.09773-0.5784, P=0.0023). Furthermore, the combination of these two gene classifiers demonstrated a superior performance in CRC prognosis prediction than either used individually. Therefore, this study proposed novel gene classifier models for CRC prognosis prediction, which may be potentially useful to inform treatment decisions for patients with CRC in clinical settings.

Proton irradiation augments the suppression of tumor progression observed with advanced age

Proton radiation is touted for improved tumor targeting, over standard gamma radiation, due to the physical advantages of ion beams for radiotherapy. Recent studies from our laboratory demonstrate that in addition to these targeting advantages, proton irradiation can inhibit angiogenic and immune factors critical to "hallmark" processes that impact cancer progression, thereby modulating tumor development. Outside the therapeutic utilization of protons, high-energy protons constitute a principal component of galactic cosmic rays and thus are a consideration in carcinogenesis risk for space flight. Given that proton irradiation modulates fundamental biological processes known to decrease with aging (e.g. angiogenesis and immunogenicity), we investigated how proton irradiation impacts tumor advancement as a function of host age, a question with both therapeutic and carcinogenesis implications. Tumor lag time and growth dynamics were tracked, after injection of murine Lewis lung carcinoma (LLC) cells into syngeneic adolescent (68 day) vs. old (736 day) C57BL/6 mice with or without coincident irradiation. Tumor growth was suppressed in old compared to adolescent mice. These differences were further modulated by proton irradiation (1 GeV), with increased inhibition and a significant radiation-altered molecular fingerprint evident in tumors grown in old mice. Through global transcriptome analysis, TGFβ1 and TGFβ2 were determined to be key players that contributed to the tumor dynamics observed. These findings suggest that old hosts exhibit a reduced capacity to support tumor advancement, which can be further reduced by proton irradiation.