High expression of PLAC1 in colon cancer as a predictor of poor prognosis: A study based on TCGA data

Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients

BACKGROUND

Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer.

METHOD

First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy.

RESULTS

The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response.

CONCLUSION

These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

Identification of placenta-specific protein 1 (PLAC-1) expression on human PC-3 cell line-derived prostate cancer stem cells compared to the tumor parental cells

Placenta-specific protein 1 (PLAC-1) is a gene primarily expressed in the placenta and the testis. Interestingly, it is also found to be expressed in many solid tumors, and it is involved in malignant cell features. However, no evidence has been reported regarding the relationship between PLAC-1 and cancer stem cells (CSCs). In the current research, we explored the expression of the PLAC-1 molecule in prostate cancer stem cells (PCSCs) derived from the human PC-3 cell line. The enrichment of PCSCs was achieved using a three-dimensional cell culture technique known as the sphere-formation assay. To confirm the identity of PCSCs, we examined the expression of genes associated with stemness and pluripotency, such as SOX2, OCT4, Nanog, C-Myc, and KLF-4, as well as stem cell differentiation molecules like CD44 and CD133. These evaluations were conducted in both the PCSCs and the original tumor cells (parental cells) using real-time PCR and flow cytometry. Subsequently, we assessed the expression of the PLAC-1 molecule in both enriched cells and parental tumor cells at the gene and protein levels using the same techniques. The tumor cells from the PC-3 cell line formed spheroids with CSC characteristics in a non-adherent medium. The expression of SOX2, OCT4, Nanog, and C-Myc genes (p < 0.01), and the molecules CD44 and CD133 (p < 0.05) were significantly elevated in PCSCs compared to the parental cells. The expression of the PLAC-1 molecule in PCSCs showed a significant increase compared to the parental cells at both gene (p < 0.01) and protein (p < 0.001) levels. In conclusion, it was indicated for the first time that PLAC-1 is up-regulated in PCSCs derived from human PC-3 cell line. This study may propose PLAC-1 as a potential target in targeted therapies, which should be confirmed through further studies.

Differential expression of PLAC1 and Netrin-1 in liver metastasis of colorectal cancer and its predictive value

OBJECTIVE

To explore the differential expression of placental specific gene 1 (PLAC1) and neurite guidance factor 1 (netrin-1) in colorectal cancer (CRC) liver metastasis and its predictive value.

METHODS

Paraffin specimens of primary CRC were selected, including 60 simple colorectal cancer specimens and 47 liver metastasis specimens. At the same time, 40 cases of normal colorectal mucosa were taken as the control group. The expression of PLAC1 and Netrin-1 in tissue was detected by immunohistochemistry (IHC). The correlation between PLAC1 and Netrin-1 expression and clinicopathological characteristics of patients with CRC liver metastases was analyzed. Logistic analysis was adopted to analyze the influencing factors of liver metastasis in CRC. A prediction model was established and ROC curve was used to detect the discrimination of the prediction model. The clinical value of PLAC1 and netrin-1 in predicting liver metastasis of CRC was analyzed using ROC curve. The relationship between the expression of PLAC1 and netrin-1 and the prognosis of CRC patients with liver metastasis was analyzed using Kaplan Meier survival curve.

RESULTS

The positive staining of PLAC1 and netrin-1 was mainly located in the cytoplasm by IHC detection. Positive expression of PLAC1 and netrin-1 in CRC tissues was markedly higher than that in normal colorectal mucosal epithelium (P < 0.05). Positive expression of PLAC1 in metastatic group was higher than that in non-metastatic group without significant difference (P > 0.05). The metastasis group had much higher positive expression of netrin-1 than the non-metastasis group (P < 0.05). The content of PLAC1 in the tissues of CRC with liver metastasis had a close relationship with differentiation degree and lymph node metastasis (P < 0.05). The expression of Netrin-1 in the tissues of CRC with liver metastasis was associated with Dukes stage, differentiation degree and lymph node metastasis (P < 0.05). Logistic regression analysis showed that Dukes stage, differentiation, lymph node metastasis, CEA, Alb and D-dimer were the independent risk factors for liver metastasis of CRC (P < 0.05). The model was constructed according to the regression coefficients and constant terms, and the discrimination of the prediction model was evaluated using ROC curve, with the AUC of 0.903 (95% CI 0.831 ~ 0.975), the sensitivity of 93.80%, the specificity of 80.00%, and the Jordan index of 0.738. The AUC of PLAC1 and netrin-1 alone and combined detection to predict liver metastasis of CRC were 0.805, 0.793 and 0.921, respectively. The survival time of patients with positive PLAC1 and netrin-1 expression were sharply shorter than that of the patients with negative expression (P < 0.05).

CONCLUSIONS

The expression of PLAC1 and netrin-1 was strongly increased in CRC with liver metastasis, which had a certain clinical value in predicting liver metastasis of CRC. Dukes stage, differentiation degree, lymph node metastasis, CEA, Alb and D-dimer were independent risk factors for liver metastasis of CRC, and the model based on these indicators had good discrimination for effectively evaluating the risk of liver metastasis in CRC.

Plac1 Remodels the Tumor Immune Evasion Microenvironment and Predicts Therapeutic Response in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC or HNSC) is the sixth most common cancer worldwide. Placenta-specific 1 (Plac1) belongs to the cancer testis antigen family and is highly expressed in malignant cells in HNSC. However, the biological function and prognostic value of plac1 in HNSC are still unclear. In the current research, we performed a comprehensive analysis of plac1 using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) bulk RNA sequencing databases as well as a single-cell sequencing dataset. We constructed a 15-gene prognostic signature through screening plac1-related immunomodulators and validated its efficiency and accuracy in immunotherapy cohorts and a pancancer database. We found that plac1 expression level is a prognostic predictor of poor overall survival in patients with HNSC. Plac1 is associated with epithelial–mesenchymal transition and tumor invasion. Plac1 has a “dual immunosuppressive function” on tumor microenvironment. On one hand, plac1-positive cells promote extracellular matrix formation and suppress immune cell infiltration. On the other hand, plac1-positive cells enhance the interaction between dendritic cells and macrophages, which further suppresses antitumor immunity. Finally, we constructed a 15-gene prognostic signature, the efficiency and accuracy of which were validated in immunotherapy cohorts and a pancancer database. In conclusion, plac1 is a promising candidate biomarker for prognosis, a potential target for immunotherapy, and a novel point for studying the immunosuppressive mechanisms of the tumor microenvironment in HNSC.

Prognosis prediction of stage IV colorectal cancer patients by mRNA transcriptional profile

BACKGROUND

Stage IV colorectal cancer patients with liver metastasis represent a special group of CRC patients with poor prognosis. The prognostic factors have not been investigated for stage IV CRC patients undergoing primary cancer resection but not candidates for metastasis resection.

METHODS

Ninety-nine stage IV CRC patients who underwent primary cancer resection without metastasis resection were retrospectively recruited. Both whole-exome sequencing (WES) and RNA-seq were performed with frozen primary cancer tissues, using para-cancerous normal tissues as the control. Valid data were obtained from 78 patients for WES and 84 patients for RNA-seq. Univariate, multivariate Cox analyses were performed and Nomogram model was established to predict patient prognosis.

RESULTS

The correlation between patient prognosis and clinicopathological factors, mutational status, or mRNA level changes was examined. Univariate (p = 0.0007) and subsequent multivariate analyses on clinicopathological factors showed that location (left or right) was the only independent risk factor for patient prognosis (HR = 3.63; 95% CI: 1.56-8.40, p = 0.003), while T, N, M staging, gender, race, location (rectum or colon), and pathological types were not stratifying factors. The mutational status of APC, TP53, KRAS, TTN, SYNE1, SMAD4, PIK3CA, RYR2, and BRAF did not show significant stratification in patient prognosis. RNA-seq showed that genes related to membrane function, ion channels, transporters, or receptors were among those with significant mRNA level alterations. Univariate analysis identified 97 genes with significantly altered mRNA levels, while NEUROD1, FGF18, SFTA2, PLAC1, SAA2, DSCAML1, and OTOP3 were significant in multivariate analysis. A risk model was established to stratify the prognosis of stage IV CRC patients. A Nomogram model was established with these genes to predict individual patient prognosis.

CONCLUSIONS

A panel of eight genes with significant mRNA level alterations was capable of predicting the prognosis and risk of the specific patient group. Future prospective study is needed to validate the model.

Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models

Colorectal cancer (CRC) is one of the most common cancers worldwide, and a leading cause of cancer deaths. Better classifying multicategory outcomes of CRC with clinical and omic data may help adjust treatment regimens based on individual's risk. Here, we selected the features that were useful for classifying four-category survival outcome of CRC using the clinical and transcriptomic data, or clinical, transcriptomic, microsatellite instability and selected oncogenic-driver data (all data) of TCGA. We also optimized multimetric feature selection to develop the best multinomial logistic regression (MLR) and random forest (RF) models that had the highest accuracy, precision, recall and F1 score, respectively. We identified 2073 differentially expressed genes of the TCGA RNASeq dataset. MLR overall outperformed RF in the multimetric feature selection. In both RF and MLR models, precision, recall and F1 score increased as the feature number increased and peaked at the feature number of 600-1000, while the models' accuracy remained stable. The best model was the MLR one with 825 features based on sum of squared coefficients using all data, and attained the best accuracy of 0.855, F1 of 0.738 and precision of 0.832, which were higher than those using clinical and transcriptomic data. The top-ranked features in the MLR model of the best performance using clinical and transcriptomic data were different from those using all data. However, pathologic staging, HBS1L, TSPYL4, and TP53TG3B were the overlapping top-20 ranked features in the best models using clinical and transcriptomic, or all data. Thus, we developed a multimetric feature-selection based MLR model that outperformed RF models in classifying four-category outcome of CRC patients. Interestingly, adding microsatellite instability and oncogenic-driver data to clinical and transcriptomic data improved models' performances. Precision and recall of tuned algorithms may change significantly as the feature number changes, but accuracy appears not sensitive to these changes.

A Prognostic Model Based on Nine DNA Methylation-Driven Genes Predicts Overall Survival for Colorectal Cancer

Background: Colorectal cancer (CRC) is the third most frequently diagnosed malignancy and the fourth leading cause of cancer-related death among common tumors in the world. We aimed to establish and validate a risk assessment model to predict overall survival (OS) for the CRC patients.

Methods: DNA methylation-driven genes were identified by integrating DNA methylation profile and transcriptome data from The Cancer Genome Atlas (TCGA) CRC cohort. Then, a risk score model was built based on LASSO, univariable Cox and multivariable Cox regression analysis. After analyzing the clinicopathological factors, a nomogram was constructed and assessed. Another cohort from GEO was used for external validation. Afterward, the molecular and immune characteristics in the two risk score groups were analyzed.

Results: In total, 705 methylation-driven genes were identified. Based on the LASSO and Cox regression analyses, nine genes, i.e., LINC01555, GSTM1, HSPA1A, VWDE, MAGEA12, ARHGAP, PTPRD, ABHD12B and TMEM88, were selected for the development of a risk score model. The Kaplan–Meier curve indicated that patients in the low-risk group had considerably better OS (P = 2e-08). The verification performed in subgroups demonstrated the validity of the model. Then, we established an OS-associated nomogram that included the risk score and significant clinicopathological factors. The concordance index of the nomogram was 0.81. A comprehensive molecular and immune characteristics analysis showed that the high-risk group was associated with tumor invasion, infiltration of immune cells executing pro-tumor suppression (such as myeloid-derived suppressor cells, regulatory T cells, immature dendritic cells) and higher expression of common inhibitory checkpoint molecules (ICPs).

Conclusion: Our nine-gene associated risk assessment model is a promising signature to distinguish the prognosis for CRC patients. It is expected to serve as a predictive tool with high sensitivity and specificity for individualized prediction of OS in the patients with CRC.

Hyperuricemia is a Adverse Prognostic Factor for Colon Cancer Patients

Objective

Hyperuricemia is linked to the prognosis of a number of cancers; however, its association with colon cancer survival has not been fully elucidated. To investigate whether hyperuricemia affects the prognosis of colon cancer, we conducted a retrospective study.

Methods

The study included age- and sex-matched colon cancer patients, of whom 60 patients were diagnosed with hyperuricemia, and 120 patients did not have hyperuricemia. The overall survival (OS) and disease-free survival (DFS) of these patients were evaluated by Kaplan–Meier (K-M) analysis. The association between the survival of colon cancer patients and hyperuricemia was analyzed using the Cox regression method after adjusting for tumor stage and grade and vascular infiltration.

Results

The K-M survival analysis supported that patients with hyperuricemia had poor OS (P for the Log rank test = 0.0008) and DFS. As demonstrated by the univariate analysis, the presence of hyperuricemia was correlated with decreased OS (HR_OS = 2.09, P = 0.002). Tumor grade and tumor stage were also found to be independent predictors for the prognosis of colon cancer patients. In addition, poor OS among patients with hyperuricemia was also confirmed in the adjusted analysis (HR_OS = 1.94, P = 0.005).

Conclusion

Hyperuricemia has an adverse effect on the prognosis and survival of patients with colon cancer. Further studies evaluating the cellular and molecular mechanisms are needed to validate the prognostic value of hyperuricemia in colon cancer.