Update on diagnosis and treatment of early signet-ring cell gastric carcinoma: A literature review

A predictive and prognostic model for metastasis risk and prognostic factors in gastrointestinal signet ring cell carcinoma

BACKGROUND

This study aimed to predict metastasis risk and identify prognostic factors of gastrointestinal signet ring cell carcinoma (SRCC) using data from the SEER database, the largest cancer dataset in North America.

METHODS

Data were obtained from the SEER database, covering 17 cancer registries from 2004 to 2020. Demographic and clinical data included sex, age, race, tumor location, size, pathological grade, stage, overall survival time, and treatment modalities. Statistical analyses were conducted using SPSS and R software. Propensity Score Matching (PSM) ensured comparable baseline characteristics between gastric cancer (GC) and colorectal cancer (CRC) groups. LASSO regression analysis identified predictors of metastasis, leading to the construction of predictive models using the lrm function in R. Nomograms were visualized with the "rms" package and assessed via ROC curves, calibration curves, and decision curve analysis (DCA). Cox regression analyses identified prognostic indicators for overall survival (OS), and Kaplan-Meier curves compared OS between high-risk and low-risk groups.

RESULTS

From 2004 to 2020, 7680 SRCC patients were identified, including 4980 GC and 2700 CRC patients. CRC patients were older and had larger tumors, higher staging, and worse differentiation. Nomograms demonstrated good discriminative ability, with AUCs of 0.704 and 0.694 for GC, and 0.694 and 0.701 for CRC in training and validation cohorts, respectively. The DCA curve indicates that this predictive model has a high gain in predicting metastasis and OS.

CONCLUSIONS

The nomograms effectively predicted metastasis risk and OS in metastatic SRCC patients, offering clinical utility in stratifying patients and guiding treatment decisions, thereby enhancing personalized treatment approaches.

A Rare Case of Primary Gastric Signet Ring Cell Carcinoma: a Review of Guidelines for the Management of Gastric Cancer

Gastric carcinoma is the fifth most common and the third leading cause of cancer deaths worldwide. The incidence of diffuse-type gastric cancer, of which signet ring cell carcinoma is a subtype, is rising in the world. Due to non-specific gastritis-like symptoms, difficulty in assessing true tumor characteristics owing to its horizontal spread, and non-distinguishable endoscopic appearance from other gastric pathologies, the diagnosis of this subtype is challenging. We present a case of a 67-year-old woman with progressively worsening abdominal pain who came for an endoscopic ultrasound evaluation of an incidentally noted pancreatic cyst on a previous MRI. During endoscopy, a 1-cm gastric ulcer was noted along the lesser curvature of the gastric body. Biopsy confirmed a diagnosis of gastric signet ring cell carcinoma (SRCC) with CDX-2 and keratin positivity. The patient underwent total gastrectomy with Roux-en-Y reconstruction. Gross specimen revealed a diffuse SRCC invading the muscularis propria, along with lymphovascular and perineural invasion. In the context of our case, we discuss the morphological features of SRCC and the effectiveness of treatment options based on existing literature. Early accurate diagnosis and staging play an important role in determining treatment options as well as the clinical course of gastric SRCC.

Characterization of the Prognosis and Tumor Microenvironment of Cellular Senescence-related Genes through scRNA-seq and Bulk RNA-seq Analysis in GC

BACKGROUND

Cellular senescence (CS) is thought to be the primary cause of cancer development and progression. This study aimed to investigate the prognostic role and molecular subtypes of CS-associated genes in gastric cancer (GC).

MATERIALS AND METHODS

The CellAge database was utilized to acquire CS-related genes. Expression data and clinical information of GC patients were obtained from The Cancer Genome Atlas (TCGA) database. Patients were then grouped into distinct subtypes using the "Consesus- ClusterPlus" R package based on CS-related genes. An in-depth analysis was conducted to assess the gene expression, molecular function, prognosis, gene mutation, immune infiltration, and drug resistance of each subtype. In addition, a CS-associated risk model was developed based on Cox regression analysis. The nomogram, constructed on the basis of the risk score and clinical factors, was formulated to improve the clinical application of GC patients. Finally, several candidate drugs were screened based on the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset.

RESULTS

According to the cluster result, patients were categorized into two molecular subtypes (C1 and C2). The two subtypes revealed distinct expression levels, overall survival (OS) and clinical presentations, mutation profiles, tumor microenvironment (TME), and drug resistance. A risk model was developed by selecting eight genes from the differential expression genes (DEGs) between two molecular subtypes. Patients with GC were categorized into two risk groups, with the high-risk group exhibiting a poor prognosis, a higher TME level, and increased expression of immune checkpoints. Function enrichment results suggested that genes were enriched in DNA repaired pathway in the low-risk group. Moreover, the Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that immunotherapy is likely to be more beneficial for patients in the low-risk group. Drug analysis results revealed that several drugs, including ML210, ML162, dasatinib, idronoxil, and temsirolimus, may contribute to the treatment of GC patients in the high-risk group. Moreover, the risk model genes presented a distinct expression in single-cell levels in the GSE150290 dataset.

CONCLUSION

The two molecular subtypes, with their own individual OS rate, expression patterns, and immune infiltration, lay the foundation for further exploration into the GC molecular mechanism. The eight gene signatures could effectively predict the GC prognosis and can serve as reliable markers for GC patients.