Differentiating rectal carcinoma by an immunohistological analysis of carcinomas of pelvic organs based on the NCBI Literature Survey and the Human Protein Atlas database

Identifying the tumor immune microenvironment-associated prognostic genes for prostate cancer

PURPOSE

This study aimed to explore novel tumor immune microenvironment (TIME)-associated biomarkers in prostate adenocarcinoma (PRAD).

METHODS

PRAD RNA-sequencing data were obtained from UCSC Xena database as the training dataset. The ESTIMATE package was used to evaluate stromal, immune, and tumor purity scores. Differentially expressed genes (DEGs) related to TIME were screened using the immune and stromal scores. Gene functions were analyzed using DAVID. The LASSO method was performed to screen prognostic TIME-related genes. Kaplan-Meier curves were used to evaluate the prognosis of samples. The correlation between the screened genes and immune cell infiltration was explored using Tumor IMmune Estimation Resource. The GSE70768 dataset from the Gene Expression Omnibus was used to validate the expression of the screened genes.

RESULTS

The ESTIMATE results revealed that high immune, stromal, and ESTIMATE scores and low tumor purity had better prognoses. Function analysis indicated that DEGs are involved in the cytokine-cytokine receptor interaction signaling pathway. In TIME-related DEGs, METTL7B, HOXB8, and TREM1 were closely related to the prognosis. Samples with low expression levels of METTL7B, HOXB8, and TREM1 had better survival times. Similarly, both the validation dataset and qRT-PCR suggested that METTL7B, HOXB8, and TREM1 were significantly decreased. The three genes showed a positive correlation with immune infiltration.

CONCLUSIONS

This study identified three TIME-related genes, namely, METTL7B, HOXB8, and TREM1, which correlated with the prognosis of patients with PRAD. Targeting the TIME-related genes might have important clinical implications when making decisions for immunotherapy in PRAD.

Machine learning algorithms based on proteomic data mining accurately predicting the recurrence of hepatitis B-related hepatocellular carcinoma

BACKGROUND AND AIM

Over 10% of hepatocellular carcinoma (HCC) cases recur each year, even after surgical resection. Currently, there is a lack of knowledge about the causes of recurrence and the effective prevention. Prediction of HCC recurrence requires diagnostic markers endowed with high sensitivity and specificity. This study aims to identify new key proteins for HCC recurrence and to build machine learning algorithms for predicting HCC recurrence.

METHODS

The proteomics data for analysis in this study were obtained from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database. We analyzed different proteins based on cases with or without recurrence of HCC. Survival analysis, Cox regression analysis, and area under the ROC curves (AUROC > 0.7) were used to screen for more significant differential proteins. Predictive models for HCC recurrence were developed using four machine learning algorithms.

RESULTS

A total of 690 differentially expressed proteins between 50 relapsed and 77 non-relapsed hepatitis B-related HCC patients were identified. Seven of these proteins had an AUROC > 0.7 for 5-year survival in HCC, including BAHCC1, ESF1, RAP1GAP, RUFY1, SCAMP3, STK3, and TMEM230. Among the machine learning algorithms, the random forest algorithm showed the highest AUROC values (AUROC: 0.991, 95% CI 0.962-0.999) for identifying HCC recurrence, followed by the support vector machine (AUROC: 0.893, 95% Cl 0.824-0.956), the logistic regression (AUROC: 0.774, 95% Cl 0.672-0.868), and the multi-layer perceptron algorithm (AUROC: 0.571, 95% Cl 0.459-0.682).

CONCLUSIONS

Our study identifies seven novel proteins for predicting HCC recurrence and the random forest algorithm as the most suitable predictive model for HCC recurrence.

Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Background

Cancer stem cells (CSCs) are implicated in cancer progression, chemoresistance, and poor prognosis; thus, they may be promising therapeutic targets. In this study, we aimed to investigate the prognostic application of differentially expressed CSC-related genes in lung squamous cell carcinoma (LUSC).

Methods

The mRNA stemness index (mRNAsi)-related differentially expressed genes (DEGs) in tumors were identified and further categorized by LASSO Cox regression analysis and 1,000-fold cross-validation, followed by the construction of a prognostic score model for risk stratification. The fractions of tumor-infiltrating immune cells and immune checkpoint genes were analyzed in different risk groups.

Results

We found 404 mRNAsi-related DEGs in LUSC, 77 of which were significantly associated with overall survival. An eight-gene prognostic signature (PPP1R27, TLX2, ANKLE1, TIGD3, AMH, KCNK3, FLRT3, and PPBP) was identified and used to construct a risk score model. The TCGA set was dichotomized into two risk groups that differed significantly (p = 0.00057) in terms of overall survival time (1, 3, 5-year AUC = 0.830, 0.749, and 0.749, respectively). The model performed well in two independent GEO datasets (p = 0.029, 0.033; 1-year AUC = 0747, 0.783; 3-year AUC = 0.746, 0.737; 5-year AUC = 0.706, 0.723). Low-risk patients had markedly increased numbers of CD8+ T cells and M1 macrophages and downregulated immune checkpoint genes compared to the corresponding values in high-risk patients (p < 0.05).

Conclusion

A stemness-related prognostic model based on eight prognostic genes in LUSC was developed and validated. The results of this study would have prognostic and therapeutic implications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02011-0.

Disseminated lung cancer presenting as a rectal mass

Primary lung cancer is the leading cause of cancer-related deaths globally, and approximately 50% had metastatic disease at the time of diagnosis. A rectal mass and unintended weight loss are common manifestations of rectal cancer. Our case presented with a rectal mass, but workup revealed a metastatic lesion from lung cancer. Lung cancer metastases to the lower gastrointestinal tract imply reduced survival compared with the already poor mean survival of stage IV lung cancer. Despite relevant therapy, the patient died 5 months after referral.

Unusual Presentation of Recurrent Early Stage Endometrial Carcinoma 28 Years after Primary Surgery

Endometrial carcinoma is the most common neoplasia of female genital tract. The prognosis of early stage disease (FIGO I and FIGO II) is excellent: recurrence after surgery is less than 15%, most of which are reported within 3 years after primary treatment. Herein we report a case of late rectal recurrence from FIGO Ib endometrial adenocarcinoma. Patient had also familiar and personal history of colonic adenocarcinoma and previous findings of microsatellite instability (MSI); molecular analysis evidenced heterozygotic somatic mutation in MLH1 gene. Twenty-eight years after hysterectomy and bilateral salpingoovariectomy, a rectal wall mass was detected during routine colonoscopy. Patients underwent CT scan, pelvic MRI, and rectal EUS with FNA: histopathological and immunohistochemical analysis revealed differentiated carcinoma cells of endometrial origin. No neoadjuvant treatment was planned and low rectal anterior resection with protective colostomy was performed; histology confirmed rectal lesion as metastasis from endometrial carcinoma. Recurrence of early stage endometrial carcinoma after a long period from primary surgery is possible. It is important to keep in mind this possibility in order to set a correct diagnostic and therapeutic algorithm, including preoperative immunohistochemical staining, and to plan a prolonged follow-up program.