Retrospective Cohort Study Open Access
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Apr 24, 2023; 14(4): 179-189
Published online Apr 24, 2023. doi: 10.5306/wjco.v14.i4.179
Interaction between age and gender on survival outcomes in extramedullary multiple myeloma over the past two decades
Ayrton I Bangolo, Chinmay Trivedi, Sowmya Sagireddy, Hamed Aljanaahi, Auda Auda, Maryama Mohamed, Sonia Onyeka, Miriam Fisher, Jyoti Thapa, Erwin J Tabucanon, Lyuben Georgiev, Annetta Wishart, Shilpee Kumari, Conrad Erikson, Mary Bangura, Orent Paddy, Rashmi Madhukar, Eugenio L Gomez, Joshua Rathod, Mansi Naria, Basel Hajal, Mohammad Awadhalla, Simcha Weissman, Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
Pierre Fwelo, Department of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health, Houston, TX 77204, United States
David Siegel, Harsh Parmar, Noa Biran, David H Vesole, Pooja Phull, Division of Myeloma, John Theurer Cancer Center, Hackensack, NJ 07601, United States
ORCID number: Ayrton I Bangolo (0000-0002-2133-2480); Simcha Weissman (0000-0002-0796-6217).
Author contributions: Bangolo AI searched the literature, wrote, and revised the manuscript; Fwelo P extracted and analysed the data, revised, and edited the manuscript; Trivedi C, Sagireddy S, Aljanaahi H, Auda A, Mohamed M, Onyeka S, Fisher M, Thapa J, Tabucanon EJ, Georgiev L, Wishart A, Kumari S, Erikson C, Bangura M, Paddy O, Madhukar R, Gomez EL, Rathod J, Naria M, Hajal B, Awadhalla M, Siegel D, Parmar H, Biran N, and Vesole DH revised and edited the manuscript; Phull P and Weissman S revised and approved the final version and are the article’s guarantors; All authors certify that they contributed sufficiently to the intellectual content and data analysis; Each author has reviewed the final version of the manuscript and approved it for publication.
Institutional review board statement: The study protocol was reviewed by the Ethics Committee at Palisades Medical Center and the need for IRB approval was waived as the SEER database is a public-use dataset.
Informed consent statement: The Surveillance, Epidemiology, and End Results (SEER) database was a public-use dataset, of which the informed consent was waived.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data used and/or analyzed in this study are available in the Surveillance, Epidemiology, and End Results (SEER) Database of the National Cancer Institute (http://seer.cancer.gov).
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ayrton I Bangolo, MBBS, MD, Doctor, Department of Medicine, Palisades Medical Center, 7600 River Road, North Bergen, NJ 07047, United States. ayrtonbangolo@yahoo.com
Received: December 17, 2022
Peer-review started: December 17, 2022
First decision: February 20, 2023
Revised: February 20, 2023
Accepted: March 17, 2023
Article in press: March 17, 2023
Published online: April 24, 2023
Processing time: 124 Days and 18.5 Hours

Abstract
BACKGROUND

Extramedullary multiple myeloma (MM) (EMM) is a rare and aggressive subentity of MM that can be present at diagnosis or develop anytime during the disease course. There is a paucity of data on the clinical characteristics and overall epidemiology of EMM. Furthermore, there is a scarcity of data on how the interaction of age and gender influences the survival of EMM.

AIM

To evaluate the clinical characteristics of patients with EMM over the past 2 decades and to identify epidemiologic characteristics that may impact overall prognosis.

METHODS

A total of 858 patients diagnosed with EMM, between 2000 and 2017, were ultimately enrolled in our study by retrieving the Surveillance, Epidemiology, and End Results database. We analyzed demographics, clinical characteristics, and overall mortality (OM) as well as cancer-specific mortality (CSM) of EMM. Variables with a P value < 0.1 in the univariate Cox regression were incorporated into the multivariate Cox model to determine the independent prognostic factors, with a hazard ratio (HR) of greater than 1 representing adverse prognostic factors.

RESULTS

From a sample of 858 EMM, the male gender (63.25%), age range 60-79 years (51.05%), and non-Hispanic whites (66.78%) were the most represented. Central Nervous System and the vertebral column was the most affected site (33.10%). Crude analysis revealed higher OM in the age group 80+ [HR = 6.951, 95% confidence interval (95%CI): 3.299-14.647, P = 0], Non-Hispanic Black population (HR = 1.339, 95%CI: 1.02-1.759, P = 0.036), Bones not otherwise specified (NOS) (HR = 1.74, 95%CI: 1.043-2.902, P = 0.034), and widowed individuals (HR = 2.107, 95%CI: 1.511-2.938, P = 0). Skin involvement (HR = 0.241, 95%CI: 0.06-0.974, P = 0.046) and a yearly income of $75000+ (HR = 0.259, 95%CI: 0.125-0.538, P = 0) had the lowest OM in the crude analysis. Crude analysis revealed higher CSM in the age group 80+, Non-Hispanic Black, Bones NOS, and widowed. Multivariate cox proportional hazard regression analyses only revealed higher OM in the age group 80+ (HR = 9.792, 95%CI: 4.403-21.774, P = 0) and widowed individuals (HR = 1.609, 95%CI: 1.101-2.35, P = 0.014). Multivariate cox proportional hazard regression analyses of CSM also revealed higher mortality of the same groups. Eyes, mouth, and ENT involvement had the lowest CSM in the multivariate analysis. There was no interaction between age and gender in the adjusted analysis for OM and CSM.

CONCLUSION

EMM is a rare entity. To our knowledge, there is a scarcity of data on the clinical characteristics and prognosis factors of patients with extramedullary multiple myeloma. In this retrospective cohort, using a United States-based population, we found that age, marital status, and tumor site were independent prognostic factors. Furthermore, we found that age and gender did not interact to influence the mortality of patients with EMM.

Key Words: Multiple myeloma; Age; Gender; Mortality; Plasmacytoma

Core Tip: Very little is known about extramedullary multiple myeloma (EMM), owing to its rarity and scarcity of data on the subject. So far it was found that advanced age was the single most important prognostic value for poor outcome in EMM. However, how age interacts with gender to affect mortality in EMM remains unknown. We found that age did not interact with gender to affect mortality in EMM.
  • Citation: Bangolo AI, Fwelo P, Trivedi C, Sagireddy S, Aljanaahi H, Auda A, Mohamed M, Onyeka S, Fisher M, Thapa J, Tabucanon EJ, Georgiev L, Wishart A, Kumari S, Erikson C, Bangura M, Paddy O, Madhukar R, Gomez EL, Rathod J, Naria M, Hajal B, Awadhalla M, Siegel D, Parmar H, Biran N, Vesole DH, Phull P, Weissman S. Interaction between age and gender on survival outcomes in extramedullary multiple myeloma over the past two decades. World J Clin Oncol 2023; 14(4): 179-189
  • URL: https://www.wjgnet.com/2218-4333/full/v14/i4/179.htm
  • DOI: https://dx.doi.org/10.5306/wjco.v14.i4.179
INTRODUCTION

Multiple myeloma (MM) is a rare cancer with the hallmark of monoclonal plasma cell proliferation in the bone marrow[1]. MM accounts for approximately 1%-2% of all cancers. A subclone can thrive and grow independent of the bone marrow microenvironment resulting in extramedullary MM (EMM) which is an aggressive subentity of MM[2]. Affecting up to 30% of patients with MM, EMM can be present either at diagnosis or anytime during the disease process[1,2].

EMM is frequently associated with high-risk cytogenetics. As evidenced by a pilot study, which revealed an association of chromosome 1 abnormalities in bone marrow myeloma cells with extramedullary progression. Optical mapping showed the potential for refining the complex genomic architecture in MM and its phenotypes[3]. Only few studies in the literature have addressed the clinical characteristics of patients with EMM[4-8]. Age at the diagnosis of MM and the site of extramedullary disease have been shown to be independent prognostic factors[4,9]. Furthermore, there is some data associating the male gender with MM[5]. However, to the best of our knowledge, there is a lack of studies addressing the interaction between age and gender in EMM, which makes our study the first of its kind.

To fill in the gaps in the literature, we conducted a retrospective cohort study amongst patients with EMM using the Surveillance, Epidemiology, and End Results (SEER) database, to evaluate the interaction of age and gender in regard to mortality of EMM as well as independent prognostic factors of patients with EMM over the past 2 decades.

MATERIALS AND METHODS
Study design

A population-based retrospective cohort study of patients with EMM was conducted using the SEER research. In addition, 18 registries in the November 2020 submission database were also utilized (http://www.seer.cancer.gov). The SEER Program is one of the largest and most authoritative sources of the cancer-related dataset in the United States, which is sponsored by the United States National Cancer Institute. The SEER 18 database collects cancer incidence, patients’ clinicopathological features, and survival data from 18 population-based cancer registries and covers nearly 28% of the United States population[9]. This dataset is de-identified and publicly available, thus, the study is exempt from an Institutional Review Board’s review. A detailed description of the database and data collection can be found elsewhere[10].

Patient selection

Inclusion criteria: All patients with EMM diagnosed from 2000 to 2017 were identified following criteria from previous studies[11]. We used site and morphology ICD-O-3 histology/behavior, malignant variables codes 9731/3 (i.e., solitary plasmacytoma of bone) and 9734/3 (i.e., extraosseous plasmacytoma) to identify patients with EMM. We also restricted our cohort to patients with 2 tumors and diagnostic confirmation through positive histology, immunotherapy, or genetic studies. Thus, increasing the accuracy of our findings and eliminating possible false-positive diagnoses.

Exclusion criteria: We excluded patients with unknown age at diagnosis, tumor stage, tumor site, or race. Lastly, we excluded patients diagnosed through autopsy.

Study variables

Main exposures: Gender (male and female), age (0-39, 40-59, 60-79, and 80+), and their interaction were the main exposures of interest.

Sociodemographic and tumor characteristics: Gender, year of diagnosis, extramedullary site of the tumor, location, annual salary, Civil status, year of diagnosis, surgical resection, as well as chemotherapy, were assessed for the purpose of the study.

Statistical analysis

We performed a crude and adjusted Cox proportional hazard regression to investigate the impact of the interaction between age and gender on EMM mortality. Variables with a value < 0.1 in the univariate Cox regression model were incorporated into the multivariate Cox proportional analysis to determine the independent prognostic factors associated with overall mortality (OM) and cancer-specific mortality (CSM), with a hazard ratio (HR) > 1 representing adverse prognostic factors. All tests were two-sided, with a confidence interval set as 95% and P value < 0.05 deemed statistically significant. All statistical tests were performed by using Software STATA16.1.

RESULTS

We enrolled 858 patients with EMM in our study. The baseline characteristics of our study are summarized in Table 1. The male gender (63.25%), age range 60-79 at diagnosis (51.05%), Non-Hispanic Whites (66.78 %), and married patients (66.32%) were the most represented groups. The Central Nervous System and vertebral column were the most affected location (33.10%). Most patients were living in metropolitan areas with a population of at least 1 million people (56.06%). Most patients did not receive chemotherapy (81.47%).

Table 1 Demographic and Clinicopathologic characteristics of United States patients with extramedullary multiple myeloma between 2000 and 2017.
Characteristics
n
%
Total 858100
Gender
Female 31136.25
Male 54763.25
Age at diagnosis, yr
0-39414.78
40-5930936.01
60-7943851.05
80+708.16
Race
Non-Hispanic white 57366.78
Non-Hispanic black13315.50
Hispanic 11012.82
Other 424.90
Extramedullary site
CNS and vertebral column28433.10
Bones, subcutaneous tissues, connective tissues, and soft tissues of the trunk10812.59
Bones, soft tissues, subcutaneous tissues, and connective tissues of the pelvis and sacrum9711.31
Bones, soft tissues, subcutaneous tissues, connective tissues, and lymph nodes of the upper extremities647.46
Bones, soft tissues, subcutaneous tissues, and connective tissues of the lower extremities455.24
Bones, soft tissues, subcutaneous tissues, connective tissues, and lymph nodes of the face and skull606.99
Other bones, NOS374.31
Eyes, mouth, and ENT10111.77
Lung, breast, and mediastinum263.03
Gastrointestinal tract182.10
Skin121.40
Kidney, suprarenal glands, and retroperitoneum60.70
Living area
Counties in metropolitan areas of 1 million persons 48156.06
Counties in metropolitan areas of 250000 to 1 million persons 17320.16
Counties in metropolitan areas of 250000 persons 768.86
Nonmetropolitan counties adjacent to a metropolitan area778.97
Nonmetropolitan counties not adjacent to a metropolitan area515.94
Income per year
< $35000121.40
$35000-44999748.62
$45000-5499915417.95
$55000-6499923227.04
$65000-7499918321.33
$75000+20323.66
Marital Status
Married 56966.32
Single 10612.35
Divorced/separated 799.21
Widowed 627.23
Unknown 424.90
CNS: Central Nervous System; NOS: Not otherwise specified.

A crude analysis of factors associated with all-cause mortality and EMM-related mortality among United States patients between 2000 and 2017 is demonstrated in Table 2. Crude analysis revealed higher OM in the age group 80+ [HR = 6.951, 95% confidence interval (95%CI): 3.299-14.647, P = 0], Non-Hispanic Black population (HR = 1.339, 95%CI: 1.02-1.759, P = 0.036), other bones (HR = 1.74, 95%CI: 1.043-2.902, P = 0.034), and widowed individuals (HR = 2.107, 95%CI: 1.511-2.938, P = 0). Skin involvement (HR = 0.241, 95%CI: 0.06-0.974, P = 0.046) and a yearly income of $75000+ (HR = 0.259, 95%CI: 0.125-0.538, P = 0) had the lowest OM in the crude analysis. Crude analysis revealed higher CSM in age group 80+ (HR = 10.111, 95%CI: 3.083-33.159, P = 0), Non-Hispanic Black (HR = 1.446, 95%CI: 1.017-2.055, P = 0.04), other bones (HR = 1.887, 95%CI: 1.044-3.411, P = 0.035) and widowed individuals (HR = 2.463, 95%CI: 1.612-3.765, P = 0).

Table 2 Crude analysis of factors associated with all-cause mortality and extramedullary multiple myeloma; related mortality among United States patients between 2000 and 2017.
CharacteristicsOverall mortality
EMD MM mortality
Crude proportional-hazard ratio (95% confidence interval)
Gender
Female1 (reference) 1 (reference)
Male 1.02 (0.826-1.259)0.804 (0.611-1.056)
Age at diagnosis, yr
0-391 (reference)1 (reference)
40-591.683 (0.82-3.452)2.409 (0.754-7.696)
60-793.271 (1.615-6.627)c4.918 (1.565-15.461)c
80+6.951 (3.299-14.647)c10.111 (3.083-33.159)c
Race
Non-Hispanic white 1 (reference)1 (reference)
Non-Hispanic black1.339 (1.02-1.759)b1.446 (1.017-2.055)b
Hispanic 0.991 (0.719-1.365)0.791 (0.495-1.263)
Other 1.016 (0.63-1.639)1.209 (0.67-2.179)
Extramedullary site
CNS and vertebral column1 (reference)1 (reference)
Bones, subcutaneous tissues, connective tissues, and soft tissues of the trunk1.53 (1.113-2.102)c1.187 (0.774-1.82)
Bones, soft tissues, subcutaneous tissues, and connective tissues of the pelvis and sacrum1.027 (0.716-1.475)1.15 (0.746-1.772)
Bones, soft tissues, subcutaneous tissues, connective tissues, and lymph nodes of the upper extremities1.036 (0.682-1.573)0.718 (0.391-1.32)
Bones, soft tissues, subcutaneous tissues, and connective tissues of the lower extremities1.668 (1.058-2.63)b1.466 (0.814-2.642)
Bones, soft tissues, subcutaneous tissues, connective tissues, and lymph nodes of the face and skull1.135 (0.756-1.702)0.919 (0.529-1.598)
Other bones, NOS1.74 (1.043-2.902)b1.887 (1.044-3.411)b
Eyes, mouth, and ENT0.929 (0.668-1.293)0.451 (0.259-0.783)c
Lung, breast, and mediastinum1.588 (0.895-2.816)1.278 (0.589-2.773)
Gastrointestinal tract0.787 (0.367-1.688)0.55 (0.174-1.745)
Skin0.241 (0.06-0.974)b0.195 (0.027-1.403)
Kidney, suprarenal glands, and retroperitoneum1.861 (0.591-5.86)0.907 (0.126-6.531)
Living area
Counties in metropolitan areas of 1 million persons 1 (reference)1 (reference)
Counties in metropolitan areas of 250000 to 1 million persons 1.021 (0.789-1.322)0.803 (0.556-1.158)
Counties in metropolitan areas of 250000 persons 0.794 (0.538-1.17)0.8 (0.482-1.327)
Nonmetropolitan counties adjacent to a metropolitan area1.099 (0.775-1.559)0.937 (0.578-1.518)
Nonmetropolitan counties not adjacent to a metropolitan area1.191 (0.798-1.779)1.105 (0.647-1.888)
Income per year
< $350001 (reference) 1 (reference)
$35000-449990.457 (0.213-0.984)b0.412 (0.167-1.014)a
$45000-549990.356 (0.17-0.745)c0.33 (0.139-0.782)b
$55000-649990.358 (0.174-0.737)c0.236 (0.101-0.554)c
$65000-749990.328 (0.158-0.681)c0.292 (0.124-0.685)c
$75000+0.259 (0.125-0.538)c0.24 (0.102-0.563)c
Marital status
Married 1 (reference)1 (reference)
Single 1.305 (0.967-1.763)a1.515 (1.027-2.236)b
Divorced/separated 1.531 (1.094-2.142)b1.681 (1.084-2.605)b
Widowed 2.107 (1.511-2.938)c2.463 (1.612-3.765)c
aP < 0.1.
bP < 0.05.
cP < 0.01.
EMD: Extramedullary disease; MM: Multiple myeloma; CNS: Central Nervous System; NOS: Not otherwise specified.

Multivariate cox proportional hazard regression analyses of factors affecting all-cause mortality and EMM-related mortality among United States patients between 2000 and 2017 are demonstrated in Table 3. Multivariate cox proportional hazard regression analyses only revealed higher OM in the age group 80+ (HR = 9.792, 95%CI: 4.403-21.774, P = 0) and widowed individuals (HR = 1.609, 95%CI: 1.101-2.35, P = 0.014). Multivariate cox proportional hazard regression analyses of CSM showed similar findings revealing higher mortality in the age group 80+ (HR = 13.672, 95%CI: 3.915-47.746, P = 0) and widowed individuals (HR = 2.085, 95%CI: 1.275-3.409, P = 0.003). Involvement of eyes, mouth and ENT sites (HR = 0.425, 95%CI: 0.235-0.768, P = 0.005) had the lowest CSM in the multivariate analysis. Importantly, the study also revealed that the interaction between age and gender was not a statistically significant predictor of mortality in patients with EMM as shown in Table 4.

Table 3 Multivariate cox proportional hazard regression analyses of factors affecting all-cause mortality and extramedullary disease multiple myeloma related mortality among United States patients between 2000 and 2017.
CharacteristicsOverall mortality
EMD MM mortality
Adjusted proportional hazard ratio (95% confidence interval)
Gender
Female1 (reference)1 (reference)
Male 1.256 (0.989-1.594)a1.022 (0.748-1.397)
Age at diagnosis, yr
0-391 (reference)1 (reference)
40-592.206 (1.047-4.647)b3.154 (0.957-10.395)a
60-794.129 (1.974-8.635)c5.667 (1.738-18.48)c
80+9.792 (4.403-21.774)c13.672 (3.915-47.746)c
Race
Non-Hispanic white 1 (reference)1 (reference)
Non-Hispanic black1.315 (0.96-1.802)a1.34 (0.884-2.03)
Hispanic 1.034 (0.734-1.457)0.833 (0.506-1.371)
Other 1.25 (0.743-2.104)1.741 (0.916-3.308)a
Extramedullary site
CNS and vertebral column1 (reference)1 (reference)
Bones, subcutaneous tissues, connective tissues, and soft tissues of the trunk1.401 (0.996-1.972)a1.046 (0.664-1.649)
Bones, soft tissues, subcutaneous tissues, and connective tissues of the pelvis and sacrum0.98 (0.671-1.432)1.091 (0.689-1.729)
Bones, soft tissues, subcutaneous tissues, connective tissues, and lymph nodes of the upper extremities1.024 (0.661-1.586)0.672 (0.353-1.279)
Bones, soft tissues, subcutaneous tissues, and connective tissues of the lower extremities1.488 (0.909-2.436)1.382 (0.733-2.605)
Bones, soft tissues, subcutaneous tissues, connective tissues, and lymph nodes of the face and skull0.99 (0.641-1.53)0.76 (0.414-1.394)
Other bones, NOS1.195 (0.694-2.058)1.199 (0.629-2.284)
Eyes, mouth, and ENT0.902 (0.631-1.29)0.425 (0.235-0.768)c
Lung, breast, and mediastinum1.187 (0.628-2.246)0.959 (0.392-2.346)
Gastrointestinal tract0.677 (0.303-1.512)0.383 (0.114-1.283)
Skin0.327 (0.08-1.34)0.325 (0.044-2.394)
Kidney, suprarenal glands, and retroperitoneum3.055 (0.881-10.601)a0.865 (0.108-6.901)
Living area
Counties in metropolitan areas of 1 million persons 1 (reference)1 (reference)
Counties in metropolitan areas of 250000 to 1 million persons 0.957 (0.715-1.282)0.778 (0.512-1.181)
Counties in metropolitan areas of 250000 persons 0.819 (0.523-1.282)0.85 (0.47-1.539)
Nonmetropolitan counties adjacent to a metropolitan area0.997 (0.653-1.522)0.836 (0.461-1.516)
Nonmetropolitan counties not adjacent to a metropolitan area0.877 (0.533-1.442)0.757 (0.379-1.512)
Income per year
< $350001 (reference)1 (reference)
$35000-449990.48 (0.21-1.095)a0.381 (0.14-1.036)a
$45000-549990.452 (0.196-1.04)a0.375 (0.135-1.044)a
$55000-64,9990.446 (0.192-1.036)a0.275 (0.096-0.788)b
$65000-749990.413 (0.173-0.983)b0.381 (0.129-1.121)a
$75000+0.324 (0.134-0.783)b0.29 (0.095-0.878)b
Marital status
Married 1 (reference)1 (reference)
Single 1.5 (1.079-2.086)b1.668 (1.089-2.556)b
Divorced/separated 1.49 (1.037-2.139)b1.463 (0.908-2.355)
Widowed 1.609 (1.101-2.35)b2.085 (1.275-3.409)c
aP < 0.1.
bP < 0.05.
cP < 0.01.
EMD: Extramedullary disease; MM: Multiple myeloma; CNS: Central Nervous System; NOS: Not otherwise specified.
Table 4 Joint test analysis of the predictors of extramedullary multiple myeloma and overall mortality among United States extramedullary multiple myeloma patients, 2000-2017.
VariablesMM mortality
Overall mortality
DF
χ2
P value
χ2
P value
Race/ethnicity35.74360.12483.24030.3560
Age at diagnosis321.2193< 0.000149.2869< 0.0001
Gender10.50440.47760.51680.4722
Extramedullary Site1116.60700.120015.65780.1543
Living area42.10230.71690.81750.9361
Income57.35390.19567.41570.1915
Marital status310.81830.012811.79670.0081
chemotherapy12.31040.12851.45360.2280
Year of diagnosis1725.31420.087916.18480.5108
Interaction between age and gender32.12850.54621.02960.7941
DF: Degree of freedom; MM: Multiple myeloma.
DISCUSSION

In this large SEER data-based retrospective cohort study, we demonstrated that EMM was associated with a higher OM and CSM in patients greater than 80 years of age and those patients who had been widowed. However, interestingly, the interaction between age and gender was not found to be statistically significant in predicting mortality in EMM patients.

EMM is a highly aggressive entity of MM, with clinical behavior distinct from marrow-restricted myeloma[12]. EMM is historically known to bear a worse prognosis compared to marrow-restricted myeloma[13]. Several studies have been carried out to investigate clinical characteristics and prognostic factors of EMM[4-8,12]. However, there is a paucity of data investigating the interaction of age and gender in regard to the mortality of EMM.

The interaction between gender and race and its influence on survival disparities in head and neck cancers has been well-documented[13]. Furthermore, gender was found to be the most important predictor with young and middle-aged females having the most favorable prognosis in non-smokers with oral squamous cell carcinoma[14]. However, no study has evaluated the impact of these interactions in the EMM population subgroup.

Our study did not reveal any interaction between age, gender, and race in regard to adjusted mortality in patients with EMM. Age was found to be the single most important prognostic factor for OM and CSM. Age was also found to be an important prognostic factor for the survival of EMM in a study by Li et al[5]. Gender and race were not of prognostic value in our cohort reaffirming the similar results found in the Li series[5].

Several retrospective studies have found marital status to be an independent prognostic factor in the survival of oncologic patients[15-19]. Patients that were married had better survival compared to their nonmarried counterparts[20-24]. This was also true in our study, where widowed patients had the highest OM and CSM, followed by single and divorced patients. This is perhaps due to the lack of psychological and emotional support as well as the increased incidence of depression and other mood disorders amongst these individuals, which could directly, or indirectly influence the treatment and regular oncology follow-up.

We hope that the results of this study will shed some light on the clinical presentation of this rare and aggressive manifestation of MM. In better understanding EMM, we hope to inspire larger prospective studies on the management of this subset of patients, which is particularly important in the era of novel agents including immunomodulatory agents, proteasome inhibitors, monoclonal antibodies, and, more recently, the advent of chimeric antigen receptor T-cell therapy and bispecific agents. This can be especially important with the new emergence of microRNAs that help prevent drug resistance when combined with anti-MM drug regimens and improve the patient’s management[25].

Our study has several strengths. Firstly, the database used is the largest cancer database in the United States. The sample size of the study is non-negligible. Also, owing to the stringent inclusion criteria and the fact that we used patients with only confirmed EMM for our diagnosis, we eliminated false positive results which increase the accuracy of our study findings. However, a few limitations should be considered in our study. Information could not be obtained on radiotherapy and Hematopoietic Stem Cell Transplant. The information on chemotherapy was unfulfilled. Furthermore, the SEER database publicly available lacks information on comorbidities, which could lead to missing data on potential confounders owing to the retrospective nature of the study.

CONCLUSION

EMM is a rare entity of MM that can be present at diagnosis or develop during the disease course. In this large retrospective SEER database-based study, we found that age and gender do not interact to influence the mortality of patients with EMM. Age was the single most important prognostic factor. We hope that the results of this study will shed light on this important non-significant interaction between age and gender in regard to mortality amongst EMM patients and perhaps inspire larger prospective studies on this subject.

ARTICLE HIGHLIGHTS
Research background

Age has been established as the single most important prognostic factor of extramedullary multiple myeloma (EMM). However, the interaction between age and gender in the mortality of EMM has yet to be studied.

Research motivation

The main motivation of this study was to identify independent predictors of outcomes, as well as how age and gender interact to affect mortality in EMM.

Research objectives

This study has the objective to establish the overall epidemiology of EMM, as well as the interaction between age and gender on mortality.

Research methods

This is a retrospective study involving 858 patients diagnosed with EMM, between 2000 and 2017 using the Surveillance, Epidemiology, and End Results database.

Research results

Patients older than 80 years and widowed had higher overall mortality (OM) and cancer-specific mortality (CSM). Eyes, mouth, and ENT involvement were protective factors regarding CSM. There was no interaction between age and gender in the adjusted analysis for OM and CSM.

Research conclusions

Although age is the single most important prognostic value of mortality in EMM, it does not interact with gender to affect mortality in patients with EMM.

Research perspectives

Future prospective studies are needed to better understand the impact of newer agents in the management of this aggressive subset of MM.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Oncology

Country/Territory of origin: United States

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): 0

Grade C (Good): C, C, C

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: van Hest N, United Kingdom; Solimando AG, Italy S-Editor: Fan JR L-Editor: A P-Editor: Fan JR

References
1.  Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72:7-33.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4235]  [Cited by in F6Publishing: 9792]  [Article Influence: 4896.0]  [Reference Citation Analysis (2)]
2.  Bhutani M, Foureau DM, Atrash S, Voorhees PM, Usmani SZ. Extramedullary multiple myeloma. Leukemia. 2020;34:1-20.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 78]  [Cited by in F6Publishing: 81]  [Article Influence: 16.2]  [Reference Citation Analysis (0)]
3.  Kriegova E, Fillerova R, Minarik J, Savara J, Manakova J, Petrackova A, Dihel M, Balcarkova J, Krhovska P, Pika T, Gajdos P, Behalek M, Vasinek M, Papajik T. Whole-genome optical mapping of bone-marrow myeloma cells reveals association of extramedullary multiple myeloma with chromosome 1 abnormalities. Sci Rep. 2021;11:14671.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 7]  [Cited by in F6Publishing: 17]  [Article Influence: 5.7]  [Reference Citation Analysis (0)]
4.  Mohan M, Yarlagadda N, Szabo A, Singh A, Pina Oviedo S, Schinke C. Clinical characteristics of testicular extramedullary involvement in multiple myeloma. Am J Hematol. 2021;96:E77-E81.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
5.  Li G, Song YP, Lv Y, Li ZZ, Zheng YH. Clinical Characteristics and Prognostic Analysis of Multiple Myeloma with Extramedullary Disease: A SEER-Based Study. J Oncol. 2021;2021:6681521.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 6]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
6.  Tian C, Wang L, Wu L, Zhu L, Xu W, Ye Z, Zhao Z, Wang Y, Zhang Y. Clinical characteristics and prognosis of multiple myeloma with bone-related extramedullary disease at diagnosis. Biosci Rep. 2018;38.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4]  [Cited by in F6Publishing: 5]  [Article Influence: 0.8]  [Reference Citation Analysis (0)]
7.  Gao S, Li Q, Dong F, Yang P, Chen Y, Wang J, Wang Y, Jing H. Clinical characteristics and survival outcomes of newly diagnosed multiple myeloma patients presenting with extramedullary disease: A retrospective study. Leuk Res. 2022;115:106793.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1]  [Cited by in F6Publishing: 1]  [Article Influence: 0.5]  [Reference Citation Analysis (0)]
8.  Janjetovic S, Lohneis P, Nogai A, Balci D, Rasche L, Jähne D, Bokemeyer C, Schilling G, Blau IW, Schmidt-Hieber M. Clinical and Biological Characteristics of Medullary and Extramedullary Plasma Cell Dyscrasias. Biology (Basel). 2021;10.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 3]  [Reference Citation Analysis (0)]
9.  Wu D, Wang WJ, Zhang M. [Bone Marrow Morphology of Multiple Myeloma with Non-bone-related Extramedullary Disease]. Zhongguo Shi Yan Xue Ye Xue Za Zhi. 2018;26:807-811.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
10.  Duggan MA, Anderson WF, Altekruse S, Penberthy L, Sherman ME. The Surveillance, Epidemiology, and End Results (SEER) Program and Pathology: Toward Strengthening the Critical Relationship. Am J Surg Pathol. 2016;40:e94-e102.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 296]  [Cited by in F6Publishing: 288]  [Article Influence: 36.0]  [Reference Citation Analysis (0)]
11.  Jagosky MH, Usmani SZ. Extramedullary Disease in Multiple Myeloma. Curr Hematol Malig Rep. 2020;15:62-71.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 14]  [Cited by in F6Publishing: 28]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
12.  Weinstock M, Ghobrial IM. Extramedullary multiple myeloma. Leuk Lymphoma. 2013;54:1135-1141.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 63]  [Cited by in F6Publishing: 66]  [Article Influence: 5.5]  [Reference Citation Analysis (0)]
13.  Mazul AL, Naik AN, Zhan KY, Stepan KO, Old MO, Kang SY, Nakken ER, Puram SV. Gender and race interact to influence survival disparities in head and neck cancer. Oral Oncol. 2021;112:105093.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 15]  [Cited by in F6Publishing: 33]  [Article Influence: 8.3]  [Reference Citation Analysis (0)]
14.  Oh LJ, Asher R, Veness M, Smee R, Goldstein D, Gopalakrishna Iyer N, Balasubramanian D, Low TH, Palme CE, Gupta R, Clark J. Effect of age and gender in non-smokers with oral squamous cell carcinoma: Multi-institutional study. Oral Oncol. 2021;116:105210.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 6]  [Cited by in F6Publishing: 7]  [Article Influence: 2.3]  [Reference Citation Analysis (0)]
15.  Tang L, Pan Z, Zhang X. The effect of marital status on the survival of patients with multiple myeloma. Hematology. 2022;27:187-197.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4]  [Cited by in F6Publishing: 10]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
16.  Wang S, Chen L, Chen D, Chao J, Shao Y, Tang K, Chen W. Effect of Marital Status on the Survival of Patients With Adenocarcinoma of the Esophagogastric Junction: A Population-Based, Propensity-Matched Study. Cancer Control. 2021;28:10732748211066309.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 6]  [Reference Citation Analysis (0)]
17.  Zhou C, Zhang Y, Hu X, Fang M, Xiao S. The effect of marital and insurance status on the survival of elderly patients with stage M1b colon cancer: a SEER-based study. BMC Cancer. 2021;21:891.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3]  [Cited by in F6Publishing: 3]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
18.  Alyabsi M, Ramadan M, Algarni M, Alshammari K, Jazieh AR. The effect of marital status on stage at diagnosis and survival in Saudis diagnosed with colorectal cancer: cancer registry analysis. Sci Rep. 2021;11:8603.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 13]  [Cited by in F6Publishing: 11]  [Article Influence: 3.7]  [Reference Citation Analysis (0)]
19.  Xiao K, Zhao Y, Cai Y, Chen P, Chen J, Ye R, Yuan B, Liu X. The effect of marital status on the survival of patients with colorectal neuroendocrine neoplasms: an analysis of the SEER database. Rev Esp Enferm Dig. 2020;112:109-117.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 2]  [Article Influence: 0.7]  [Reference Citation Analysis (0)]
20.  Dong J, Dai Q, Zhang F. The effect of marital status on endometrial cancer-related diagnosis and prognosis: a Surveillance Epidemiology and End Results database analysis. Future Oncol. 2019;15:3963-3976.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 19]  [Article Influence: 3.8]  [Reference Citation Analysis (0)]
21.  Hinyard L, Wirth LS, Clancy JM, Schwartz T. The effect of marital status on breast cancer-related outcomes in women under 65: A SEER database analysis. Breast. 2017;32:13-17.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 52]  [Cited by in F6Publishing: 45]  [Article Influence: 6.4]  [Reference Citation Analysis (0)]
22.  Xie JC, Yang S, Liu XY, Zhao YX. Effect of marital status on survival in glioblastoma multiforme by demographics, education, economic factors, and insurance status. Cancer Med. 2018;7:3722-3742.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 26]  [Cited by in F6Publishing: 42]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
23.  Liang Y, Wu X, Lu C, Xiao F. Impact of marital status on the prognosis of liver cancer patients without surgery and the critical window. Ann Palliat Med. 2021;10:2990-2999.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 10]  [Article Influence: 3.3]  [Reference Citation Analysis (0)]
24.  Feng Y, Dai W, Li Y, Mo S, Li Q, Cai S. The effect of marital status by age on patients with colorectal cancer over the past decades: a SEER-based analysis. Int J Colorectal Dis. 2018;33:1001-1010.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 13]  [Cited by in F6Publishing: 15]  [Article Influence: 2.5]  [Reference Citation Analysis (0)]
25.  Desantis V, Saltarella I, Lamanuzzi A, Melaccio A, Solimando AG, Mariggiò MA, Racanelli V, Paradiso A, Vacca A, Frassanito MA. MicroRNAs-Based Nano-Strategies as New Therapeutic Approach in Multiple Myeloma to Overcome Disease Progression and Drug Resistance. Int J Mol Sci. 2020;21.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 23]  [Cited by in F6Publishing: 41]  [Article Influence: 10.3]  [Reference Citation Analysis (0)]